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u?(sa(),u=o.fallback,v=n.mode,A=e.child,C=A.sibling,o=Hn(A,{mode:"hidden",children:o.children}),o.subtreeFlags=A.subtreeFlags&65011712,C!==null?u=Hn(C,u):(u=Oa(u,v,i,null),u.flags|=2),u.return=n,o.return=n,o.sibling=u,n.child=o,o=u,u=n.child,v=e.child.memoizedState,v===null?v=iu(i):(A=v.cachePool,A!==null?(C=dt._currentValue,A=A.parent!==C?{parent:C,pool:C}:A):A=mp(),v={baseLanes:v.baseLanes|i,cachePool:A}),u.memoizedState=v,u.childLanes=ru(e,g,i),n.memoizedState=au,o):(la(n),i=e.child,e=i.sibling,i=Hn(i,{mode:"visible",children:o.children}),i.return=n,i.sibling=null,e!==null&&(g=n.deletions,g===null?(n.deletions=[e],n.flags|=16):g.push(e)),n.child=i,n.memoizedState=null,i)}function ou(e,n){return n=Zo({mode:"visible",children:n},e.mode),n.return=e,e.child=n}function Zo(e,n){return e=Zt(22,e,null,n),e.lanes=0,e.stateNode={_visibility:1,_pendingMarkers:null,_retryCache:null,_transitions:null},e}function lu(e,n,i){return _i(n,e.child,null,i),e=ou(n,n.pendingProps.children),e.flags|=2,n.memoizedState=null,e}function Rd(e,n,i){e.lanes|=n;var o=e.alternate;o!==null&&(o.lanes|=n),ks(e.return,n,i)}function su(e,n,i,o,u){var f=e.memoizedState;f===null?e.memoizedState={isBackwards:n,rendering:null,renderingStartTime:0,last:o,tail:i,tailMode:u}:(f.isBackwards=n,f.rendering=null,f.renderingStartTime=0,f.last=o,f.tail=i,f.tailMode=u)}function Dd(e,n,i){var o=n.pendingProps,u=o.revealOrder,f=o.tail;if(_t(e,n,o.children,i),o=mt.current,(o&2)!==0)o=o&1|2,n.flags|=128;else{if(e!==null&&(e.flags&128)!==0)e:for(e=n.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&Rd(e,i,n);else if(e.tag===19)Rd(e,i,n);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===n)break e;for(;e.sibling===null;){if(e.return===null||e.return===n)break 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uu(e,n){return(e.lanes&n)!==0?!0:(e=e.dependencies,!!(e!==null&&Do(e)))}function bb(e,n,i){switch(n.tag){case 3:Ne(n,n.stateNode.containerInfo),na(n,dt,e.memoizedState.cache),ur();break;case 27:case 5:kn(n);break;case 4:Ne(n,n.stateNode.containerInfo);break;case 10:na(n,n.type,n.memoizedProps.value);break;case 13:var o=n.memoizedState;if(o!==null)return o.dehydrated!==null?(la(n),n.flags|=128,null):(i&n.child.childLanes)!==0?Cd(e,n,i):(la(n),e=Qn(e,n,i),e!==null?e.sibling:null);la(n);break;case 19:var u=(e.flags&128)!==0;if(o=(i&n.childLanes)!==0,o||(fr(e,n,i,!1),o=(i&n.childLanes)!==0),u){if(o)return Dd(e,n,i);n.flags|=128}if(u=n.memoizedState,u!==null&&(u.rendering=null,u.tail=null,u.lastEffect=null),x(mt,mt.current),o)break;return null;case 22:case 23:return n.lanes=0,Sd(e,n,i);case 24:na(n,dt,e.memoizedState.cache)}return Qn(e,n,i)}function Od(e,n,i){if(e!==null)if(e.memoizedProps!==n.pendingProps)bt=!0;else{if(!uu(e,i)&&(n.flags&128)===0)return bt=!1,bb(e,n,i);bt=(e.flags&131072)!==0}else bt=!1,qe&&(n.flags&1048576)!==0&&lp(n,Ro,n.index);switch(n.lanes=0,n.tag){case 16:e:{e=n.pendingProps;var o=n.elementType,u=o._init;if(o=u(o._payload),n.type=o,typeof o=="function")bs(o)?(e=Ba(o,e),n.tag=1,n=Td(null,n,o,e,i)):(n.tag=0,n=nu(null,n,o,e,i));else{if(o!=null){if(u=o.$$typeof,u===we){n.tag=11,n=wd(null,n,o,e,i);break e}else if(u===ue){n.tag=14,n=_d(null,n,o,e,i);break e}}throw n=J(o)||o,Error(l(306,n,""))}}return n;case 0:return nu(e,n,n.type,n.pendingProps,i);case 1:return o=n.type,u=Ba(o,n.pendingProps),Td(e,n,o,u,i);case 3:e:{if(Ne(n,n.stateNode.containerInfo),e===null)throw Error(l(387));o=n.pendingProps;var f=n.memoizedState;u=f.element,Ms(e,n),br(n,o,null,i);var g=n.memoizedState;if(o=g.cache,na(n,dt,o),o!==f.cache&&Ts(n,[dt],i,!0),yr(),o=g.element,f.isDehydrated)if(f={element:o,isDehydrated:!1,cache:g.cache},n.updateQueue.baseState=f,n.memoizedState=f,n.flags&256){n=Ed(e,n,o,i);break e}else if(o!==u){u=cn(Error(l(424)),n),cr(u),n=Ed(e,n,o,i);break e}else{switch(e=n.stateNode.containerInfo,e.nodeType){case 9:e=e.body;break;default:e=e.nodeName==="HTML"?e.ownerDocument.body:e}for(nt=_n(e.firstChild),Mt=n,qe=!0,Na=null,En=!0,i=ud(n,null,o,i),n.child=i;i;)i.flags=i.flags&-3|4096,i=i.sibling}else{if(ur(),o===u){n=Qn(e,n,i);break e}_t(e,n,o,i)}n=n.child}return n;case 26:return Ko(e,n),e===null?(i=Im(n.type,null,n.pendingProps,null))?n.memoizedState=i:qe||(i=n.type,e=n.pendingProps,o=cl(re.current).createElement(i),o[Et]=n,o[jt]=e,St(o,i,e),yt(o),n.stateNode=o):n.memoizedState=Im(n.type,e.memoizedProps,n.pendingProps,e.memoizedState),null;case 27:return kn(n),e===null&&qe&&(o=n.stateNode=Mm(n.type,n.pendingProps,re.current),Mt=n,En=!0,u=nt,ya(n.type)?(Vu=u,nt=_n(o.firstChild)):nt=u),_t(e,n,n.pendingProps.children,i),Ko(e,n),e===null&&(n.flags|=4194304),n.child;case 5:return e===null&&qe&&((u=o=nt)&&(o=Xb(o,n.type,n.pendingProps,En),o!==null?(n.stateNode=o,Mt=n,nt=_n(o.firstChild),En=!1,u=!0):u=!1),u||Ia(n)),kn(n),u=n.type,f=n.pendingProps,g=e!==null?e.memoizedProps:null,o=f.children,qu(u,f)?o=null:g!==null&&qu(u,g)&&(n.flags|=32),n.memoizedState!==null&&(u=zs(e,n,cb,null,null,i),Br._currentValue=u),Ko(e,n),_t(e,n,o,i),n.child;case 6:return e===null&&qe&&((e=i=nt)&&(i=Yb(i,n.pendingProps,En),i!==null?(n.stateNode=i,Mt=n,nt=null,e=!0):e=!1),e||Ia(n)),null;case 13:return Cd(e,n,i);case 4:return Ne(n,n.stateNode.containerInfo),o=n.pendingProps,e===null?n.child=_i(n,null,o,i):_t(e,n,o,i),n.child;case 11:return wd(e,n,n.type,n.pendingProps,i);case 7:return _t(e,n,n.pendingProps,i),n.child;case 8:return _t(e,n,n.pendingProps.children,i),n.child;case 12:return _t(e,n,n.pendingProps.children,i),n.child;case 10:return o=n.pendingProps,na(n,n.type,o.value),_t(e,n,o.children,i),n.child;case 9:return u=n.type._context,o=n.pendingProps.children,La(n),u=Ct(u),o=o(u),n.flags|=1,_t(e,n,o,i),n.child;case 14:return _d(e,n,n.type,n.pendingProps,i);case 15:return xd(e,n,n.type,n.pendingProps,i);case 19:return Dd(e,n,i);case 31:return o=n.pendingProps,i=n.mode,o={mode:o.mode,children:o.children},e===null?(i=Zo(o,i),i.ref=n.ref,n.child=i,i.return=n,n=i):(i=Hn(e.child,o),i.ref=n.ref,n.child=i,i.return=n,n=i),n;case 22:return Sd(e,n,i);case 24:return La(n),o=Ct(dt),e===null?(u=Rs(),u===null&&(u=Je,f=Es(),u.pooledCache=f,f.refCount++,f!==null&&(u.pooledCacheLanes|=i),u=f),n.memoizedState={parent:o,cache:u},Os(n),na(n,dt,u)):((e.lanes&i)!==0&&(Ms(e,n),br(n,null,null,i),yr()),u=e.memoizedState,f=n.memoizedState,u.parent!==o?(u={parent:o,cache:o},n.memoizedState=u,n.lanes===0&&(n.memoizedState=n.updateQueue.baseState=u),na(n,dt,o)):(o=f.cache,na(n,dt,o),o!==u.cache&&Ts(n,[dt],i,!0))),_t(e,n,n.pendingProps.children,i),n.child;case 29:throw n.pendingProps}throw Error(l(156,n.tag))}function Kn(e){e.flags|=4}function Md(e,n){if(n.type!=="stylesheet"||(n.state.loading&4)!==0)e.flags&=-16777217;else if(e.flags|=16777216,!Bm(n)){if(n=mn.current,n!==null&&((Me&4194048)===Me?Cn!==null:(Me&62914560)!==Me&&(Me&536870912)===0||n!==Cn))throw hr=Ds,hp;e.flags|=8192}}function Jo(e,n){n!==null&&(e.flags|=4),e.flags&16384&&(n=e.tag!==22?cf():536870912,e.lanes|=n,ki|=n)}function kr(e,n){if(!qe)switch(e.tailMode){case"hidden":n=e.tail;for(var i=null;n!==null;)n.alternate!==null&&(i=n),n=n.sibling;i===null?e.tail=null:i.sibling=null;break;case"collapsed":i=e.tail;for(var o=null;i!==null;)i.alternate!==null&&(o=i),i=i.sibling;o===null?n||e.tail===null?e.tail=null:e.tail.sibling=null:o.sibling=null}}function tt(e){var n=e.alternate!==null&&e.alternate.child===e.child,i=0,o=0;if(n)for(var u=e.child;u!==null;)i|=u.lanes|u.childLanes,o|=u.subtreeFlags&65011712,o|=u.flags&65011712,u.return=e,u=u.sibling;else for(u=e.child;u!==null;)i|=u.lanes|u.childLanes,o|=u.subtreeFlags,o|=u.flags,u.return=e,u=u.sibling;return e.subtreeFlags|=o,e.childLanes=i,n}function vb(e,n,i){var o=n.pendingProps;switch(xs(n),n.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return tt(n),null;case 1:return tt(n),null;case 3:return i=n.stateNode,o=null,e!==null&&(o=e.memoizedState.cache),n.memoizedState.cache!==o&&(n.flags|=2048),Fn(dt),Ot(),i.pendingContext&&(i.context=i.pendingContext,i.pendingContext=null),(e===null||e.child===null)&&(sr(n)?Kn(n):e===null||e.memoizedState.isDehydrated&&(n.flags&256)===0||(n.flags|=1024,cp())),tt(n),null;case 26:return i=n.memoizedState,e===null?(Kn(n),i!==null?(tt(n),Md(n,i)):(tt(n),n.flags&=-16777217)):i?i!==e.memoizedState?(Kn(n),tt(n),Md(n,i)):(tt(n),n.flags&=-16777217):(e.memoizedProps!==o&&Kn(n),tt(n),n.flags&=-16777217),null;case 27:Ln(n),i=re.current;var u=n.type;if(e!==null&&n.stateNode!=null)e.memoizedProps!==o&&Kn(n);else{if(!o){if(n.stateNode===null)throw Error(l(166));return tt(n),null}e=te.current,sr(n)?sp(n):(e=Mm(u,o,i),n.stateNode=e,Kn(n))}return tt(n),null;case 5:if(Ln(n),i=n.type,e!==null&&n.stateNode!=null)e.memoizedProps!==o&&Kn(n);else{if(!o){if(n.stateNode===null)throw Error(l(166));return tt(n),null}if(e=te.current,sr(n))sp(n);else{switch(u=cl(re.current),e){case 1:e=u.createElementNS("http://www.w3.org/2000/svg",i);break;case 2:e=u.createElementNS("http://www.w3.org/1998/Math/MathML",i);break;default:switch(i){case"svg":e=u.createElementNS("http://www.w3.org/2000/svg",i);break;case"math":e=u.createElementNS("http://www.w3.org/1998/Math/MathML",i);break;case"script":e=u.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild);break;case"select":e=typeof 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62async getResponse(a){if(typeof a=="object"&&Array.isArray(a?.choices)&&typeof a?.created=="number"&&typeof a?.id=="string"&&typeof a?.model=="string"&&(a.system_fingerprint===void 0||a.system_fingerprint===null||typeof a.system_fingerprint=="string")&&typeof a?.usage=="object")return a;throw new oe("Expected ChatCompletionOutput")}}class Ta extends Ft{constructor(a,r,l=!1){super(a,r,l)}preparePayload(a){return{...a.args,model:a.model}}makeRoute(){return"v1/completions"}async getResponse(a){const r=Xg(a);if(Array.isArray(r)&&r.length>0&&r.every(l=>typeof l=="object"&&!!l&&"generated_text"in l&&typeof l.generated_text=="string"))return r[0];throw new oe("Expected Array<{generated_text: string}>")}}function rn(t){if(globalThis.Buffer)return globalThis.Buffer.from(t).toString("base64");{const a=[];return t.forEach(r=>{a.push(String.fromCharCode(r))}),globalThis.btoa(a.join(""))}}function e1(t,a){return Object.assign({},...a.map(r=>{if(t[r]!==void 0)return{[r]:t[r]}}))}function Mc(t,a){return t.includes(a)}function ze(t,a){const r=Array.isArray(a)?a:[a],l=Object.keys(t).filter(s=>!Mc(r,s));return e1(t,l)}const Yh=["feature-extraction","sentence-similarity"];class Xe extends Ft{constructor(){super("hf-inference",`${$g}/hf-inference`)}preparePayload(a){return a.args}makeUrl(a){return a.model.startsWith("http://")||a.model.startsWith("https://")?a.model:super.makeUrl(a)}makeRoute(a){return a.task&&["feature-extraction","sentence-similarity"].includes(a.task)?`models/${a.model}/pipeline/${a.task}`:`models/${a.model}`}
62async getResponse(a){return a}}class t1 extends Xe{async getResponse(a,r,l,s){if(!a)throw new oe("Received malformed response from HF-Inference text-to-image API: response is undefined");if(typeof a=="object"){if(s==="json")return{...a};if("data"in a&&Array.isArray(a.data)&&a.data[0].b64_json){const c=a.data[0].b64_json;return s==="url"?`data:image/jpeg;base64,${c}`:await(await fetch(`data:image/jpeg;base64,${c}`)).blob()}if("output"in a&&Array.isArray(a.output))return s==="url"?a.output[0]:await(await fetch(a.output[0])).blob()}if(a instanceof Blob){if(s==="url"||s==="json"){const c=await a.arrayBuffer().then(p=>Buffer.from(p).toString("base64"));return s==="url"?`data:image/jpeg;base64,${c}`:{output:`data:image/jpeg;base64,${c}`}}return a}throw new oe("Received malformed response from HF-Inference text-to-image API: expected a Blob")}}class n1 extends Xe{makeUrl(a){let r;return a.model.startsWith("http://")||a.model.startsWith("https://")?r=a.model.trim():r=`${this.makeBaseUrl(a)}/models/${a.model}`,r=r.replace(/\/+$/,""),r.endsWith("/v1")?r+="/chat/completions":r.endsWith("/chat/completions")||(r+="/v1/chat/completions"),r}preparePayload(a){return{...a.args,model:a.model}}
62async getResponse(a){return a}}class a1 extends Xe{async getResponse(a){const r=Xg(a);if(Array.isArray(r)&&r.every(l=>"generated_text"in l&&typeof l?.generated_text=="string"))return r?.[0];throw new oe("Received malformed response from HF-Inference text generation API: expected Array<{generated_text: string}>")}}class i1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r=="object"&&r!==null&&typeof r.label=="string"&&typeof r.score=="number"))return a;throw new oe("Received malformed response from HF-Inference audio-classification API: expected Array<{label: string, score: number}> but received different format")}}class r1 extends Xe{async getResponse(a){return a}async preparePayloadAsync(a){return"data"in a?a:{...ze(a,"inputs"),data:a.inputs}}}class o1 extends Xe{async getResponse(a){if(!Array.isArray(a))throw new oe("Received malformed response from HF-Inference audio-to-audio API: expected Array");if(!a.every(r=>typeof r=="object"&&r&&"label"in r&&typeof r.label=="string"&&"content-type"in r&&typeof r["content-type"]=="string"&&"blob"in r&&typeof r.blob=="string"))throw new oe("Received malformed response from HF-Inference audio-to-audio API: expected Array<{label: string, audio: Blob}>");return a}}
62class l1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r=="object"&&!!r&&typeof r?.answer=="string"&&(typeof r.end=="number"||typeof r.end>"u")&&(typeof r.score=="number"||typeof r.score>"u")&&(typeof r.start=="number"||typeof r.start>"u")))return a[0];throw new oe("Received malformed response from HF-Inference document-question-answering API: expected Array<{answer: string, end: number, score: number, start: number}>")}}class s1 extends Xe{async getResponse(a){const r=(l,s,c=0)=>c>s?!1:l.every(p=>Array.isArray(p))?l.every(p=>r(p,s,c+1)):l.every(p=>typeof p=="number");if(Array.isArray(a)&&r(a,3,0))return a;throw new oe("Received malformed response from HF-Inference feature-extraction API: expected Array<number[][][] | number[][] | number[] | number>")}}class u1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r.label=="string"&&typeof r.score=="number"))return a;throw new oe("Received malformed response from HF-Inference image-classification API: expected Array<{label: string, score: number}>")}}
62class c1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r.label=="string"&&typeof r.mask=="string"&&(r.score===void 0||typeof r.score=="number")))return a;throw new oe("Received malformed response from HF-Inference image-segmentation API: expected Array<{label: string, mask: string, score: number}>")}async preparePayloadAsync(a){return{...a,inputs:rn(new Uint8Array(a.inputs instanceof ArrayBuffer?a.inputs:await a.inputs.arrayBuffer()))}}}class f1 extends Xe{async getResponse(a){if(typeof a?.generated_text!="string")throw new oe("Received malformed response from HF-Inference image-to-text API: expected {generated_text: string}");return a}}class p1 extends Xe{async preparePayloadAsync(a){return a.parameters?{...a,inputs:rn(new Uint8Array(a.inputs instanceof ArrayBuffer?a.inputs:await a.inputs.arrayBuffer()))}:{...a,model:a.model,data:a.inputs}}async getResponse(a){if(a instanceof Blob)return a;throw new oe("Received malformed response from HF-Inference image-to-image API: expected Blob")}}
62class d1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r.label=="string"&&typeof r.score=="number"&&typeof r.box.xmin=="number"&&typeof r.box.ymin=="number"&&typeof r.box.xmax=="number"&&typeof r.box.ymax=="number"))return a;throw new oe("Received malformed response from HF-Inference object-detection API: expected Array<{label: string, score: number, box: {xmin: number, ymin: number, xmax: number, ymax: number}}>")}}class m1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r.label=="string"&&typeof r.score=="number"))return a;throw new oe("Received malformed response from HF-Inference zero-shot-image-classification API: expected Array<{label: string, score: number}>")}}class h1 extends Xe{async getResponse(a){const r=a?.[0];if(Array.isArray(r)&&r.every(l=>typeof l?.label=="string"&&typeof l.score=="number"))return r;throw new oe("Received malformed response from HF-Inference text-classification API: expected Array<{label: string, score: number}>")}}class g1 extends Xe{async getResponse(a
62){if(Array.isArray(a)?a.every(r=>typeof r=="object"&&!!r&&typeof r.answer=="string"&&typeof r.end=="number"&&typeof r.score=="number"&&typeof r.start=="number"):typeof a=="object"&&a&&typeof a.answer=="string"&&typeof a.end=="number"&&typeof a.score=="number"&&typeof a.start=="number")return Array.isArray(a)?a[0]:a;throw new oe("Received malformed response from HF-Inference question-answering API: expected Array<{answer: string, end: number, score: number, start: number}>")}}class y1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r.score=="number"&&typeof r.sequence=="string"&&typeof r.token=="number"&&typeof r.token_str=="string"))return a;throw new oe("Received malformed response from HF-Inference fill-mask API: expected Array<{score: number, sequence: string, token: number, token_str: string}>")}}class Gc extends Xe{async getResponse(a){if(typeof a=="object"&&a!==null&&"labels"in a&&"scores"in a&&Array.isArray(a.labels)&&Array.isArray(a.scores)&&a.labels.length===a.scores.length&&a.labels.every(r=>typeof r=="string")&&a.scores.every(r=>typeof r=="number")){const r=a.scores;return a.labels.map((l,s)=>({label:l,score:r[s]}))}if(Array.isArray(a)&&a.every(Gc.validateOutputElement))return a;throw new oe("Received malformed response from HF-Inference zero-shot-classification API: expected Array<{label: string, score: number}>")}static validateOutputElement(a){return typeof a=="object"&&!!a&&"label"in a&&"score"in a&&typeof a.label=="string"&&typeof a.score=="number"}}
62class b1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r=="number"))return a;throw new oe("Received malformed response from HF-Inference sentence-similarity API: expected Array<number>")}}class Dl extends Xe{static validate(a){return typeof a=="object"&&!!a&&"aggregator"in a&&typeof a.aggregator=="string"&&"answer"in a&&typeof a.answer=="string"&&"cells"in a&&Array.isArray(a.cells)&&a.cells.every(r=>typeof r=="string")&&"coordinates"in a&&Array.isArray(a.coordinates)&&a.coordinates.every(r=>Array.isArray(r)&&r.every(l=>typeof l=="number"))}async getResponse(a){if(Array.isArray(a)&&Array.isArray(a)?a.every(r=>Dl.validate(r)):Dl.validate(a))return Array.isArray(a)?a[0]:a;throw new oe("Received malformed response from HF-Inference table-question-answering API: expected {aggregator: string, answer: string, cells: string[], coordinates: number[][]}")}}class v1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r.end=="number"&&typeof r.entity_group=="string"&&typeof r.score=="number"&&typeof r.start=="number"&&typeof r.word=="string"))return a;throw new oe("Received malformed response from HF-Inference token-classification API: expected Array<{end: number, entity_group: string, score: number, start: number, word: string}>")}}
62class w1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r?.translation_text=="string"))return a?.length===1?a?.[0]:a;throw new oe("Received malformed response from HF-Inference translation API: expected Array<{translation_text: string}>")}}class _1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r?.summary_text=="string"))return a?.[0];throw new oe("Received malformed response from HF-Inference summarization API: expected Array<{summary_text: string}>")}}class x1 extends Xe{async getResponse(a){return a}}class S1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r=="number"))return a;throw new oe("Received malformed response from HF-Inference tabular-classification API: expected Array<number>")}}class A1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r=="object"&&!!r&&typeof r?.answer=="string"&&typeof r.score=="number"))return a[0];throw new oe("Received malformed response from HF-Inference visual-question-answering API: expected Array<{answer: string, score: number}>")}}
62class k1 extends Xe{async getResponse(a){if(Array.isArray(a)&&a.every(r=>typeof r=="number"))return a;throw new oe("Received malformed response from HF-Inference tabular-regression API: expected Array<number>")}}class T1 extends Xe{async getResponse(a){return a}}let E1=console;function lo(){return E1}const bc=new Map;function C1(t,a){return a?Array.isArray(a)?a:Object.entries(a).map(([r,l])=>({provider:r,hfModelId:t,providerId:l.providerId,status:l.status,task:l.task,adapter:l.adapter,adapterWeightsPath:l.adapterWeightsPath})):[]}async function Yg(t,a,r){let l;if(bc.has(t))l=bc.get(t);else{const s=`${Zc}/api/models/${t}?expand[]=inferenceProviderMapping`,c=await(r?.fetch??fetch)(s,{headers:a?.startsWith("hf_")?{Authorization:`Bearer ${a}`}:{}});if(!c.ok)if(c.headers.get("Content-Type")?.startsWith("application/json")){const m=await c.json();if("error"in m&&typeof m.error=="string")throw new Wr(`Failed to fetch inference provider mapping for model ${t}: ${m.error}`,{url:s,method:"GET"},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:m})}else throw new Wr(`Failed to fetch inference provider mapping for model ${t}`,{url:s,method:"GET"},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:await c.text()});let p=null;try{p=await c.json()}catch{throw new Wr(`Failed to fetch inference provider mapping for model ${t}: malformed API response, invalid JSON`,{url:s,method:"GET"},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:await c.text()})}if(!p?.inferenceProviderMapping)throw new Wr(`We have not been able to find inference provider information for model ${t}.`,{url:s,method:"GET"},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:await c.text()});l=C1(t,p.inferenceProviderMapping),bc.set(t,l)}return l}async function R1(t,a){const r=lo();if(Xh[t.provider][t.modelId])return Xh[t.provider][t.modelId];const s=(await Yg(t.modelId,t.accessToken,a)).find(c=>c.provider===t.provider);if(s){const c=t.provider==="hf-inference"&&Mc(Yh,t.task)?Yh:[t.task];if(!Mc(c,s.task))throw new ft(`Model ${t.modelId} is not supported for task ${t.task} and provider ${t.provider}. Supported task: ${s.task}.`);return s.status==="staging"&&r.warn(`Model ${t.modelId} is in staging mode for provider ${t.provider}. Meant for test purposes only.`),s}return null}async function Ie(t,a,r){const l=lo();if(r){if(t)throw new ft("Specifying both endpointUrl and provider is not supported.");return"hf-inference"}if(t||(l.log("Defaulting to 'auto' which will select the first provider available for the model, sorted by the user's order in https://hf.co/settings/inference-providers."),t="auto"),t==="auto"){if(!a)throw new ft("Specifying a model is required when provider is 'auto'");t=(await Yg(a))[0]?.provider,l.log("Auto selected provider:",t)}if(!t)throw new ft(`No Inference Provider available for model ${a}.`);return t}function Wc(t){return new Promise(a=>{setTimeout(()=>a(),t)})}const D1="https://api.us1.bfl.ai";class O1 extends Ft{constructor(){super("black-forest-labs",D1)}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,prompt:a.args.inputs}}prepareHeaders(a,r){const l={Authorization:a.authMethod!=="provider-key"?`Bearer ${a.accessToken}`:`X-Key ${a.accessToken}`};return r||(l["Content-Type"]="application/json"),l}makeRoute(a){if(!a)throw new ft("Params are required");return`/v1/${a.model}`}async getResponse(a,r,l,s){const c=lo(),p=new URL(a.polling_url);for(let m=0;m<5;m++){await Wc(1e3),c.debug(`Polling Black Forest Labs API for the result... ${m+1}/5`),p.searchParams.set("attempt",m.toString(10));const b=await fetch(p,{headers:{"Content-Type":"application/json"}});if(!b.ok)throw new kt("Failed to fetch result from black forest labs API",{url:p.toString(),method:"GET",headers:{"Content-Type":"application/json"}},{requestId:b.headers.get("x-request-id")??"",status:b.status,body:await b.text()});const d=await b.json();if(typeof d=="object"&&d&&"status"in d&&typeof d.status=="string"&&d.status==="Ready"&&"result"in d&&typeof d.result=="object"&&d.result&&"sample"in d.result&&typeof d.result.sample=="string")return s==="json"?d.result:s==="url"?d.result.sample:await(await fetch(d.result.sample)).blob()}throw new oe("Timed out while waiting for the result from black forest labs API - aborting after 5 attempts")}}class M1 extends Nt{constructor(){super("cerebras","https://api.cerebras.ai")}}class U1 extends Nt{constructor(){super("cohere","https://api.cohere.com")}makeRoute(){return"/compatibility/v1/chat/completions"}}function Xa(t){return/^http(s?):/.test(t)||t.startsWith("/")}const Qh=["audio/mpeg","audio/mp4","audio/wav","audio/x-wav"];
62class Nl extends Ft{constructor(a){super("fal-ai",a||"https://fal.run")}preparePayload(a){return a.args}makeRoute(a){return`/${a.model}`}prepareHeaders(a,r){const l={Authorization:a.authMethod!=="provider-key"?`Bearer ${a.accessToken}`:`Key ${a.accessToken}`};return r||(l["Content-Type"]="application/json"),l}}class Il extends Nl{async getResponseFromQueueApi(a,r,l){if(!r||!l)throw new ft(`URL and headers are required for ${this.task} task`);if(!a.request_id)throw new oe(`Received malformed response from Fal.ai ${this.task} API: no request ID found in the response`);let c=a.status;const p=new URL(r),m=`${p.protocol}//${p.host}${p.host==="router.huggingface.co"?"/fal-ai":""}`,b=new URL(a.response_url).pathname,d=p.search,y=`${m}${b}/status${d}`,w=`${m}${b}${d}`;for(;c!=="COMPLETED";){await Wc(500);const B=await fetch(y,{headers:l});if(!B.ok)throw new kt("Failed to fetch response status from fal-ai API",{url:y,method:"GET"},{requestId:B.headers.get("x-request-id")??"",status:B.status,body:await B.text()});try{c=(await B.json()).status}catch{throw new oe("Failed to parse status response from fal-ai API: received malformed response")}}const S=await fetch(w,{headers:l});let _;try{_=await S.json()}catch{throw new oe("Failed to parse result response from fal-ai API: received malformed response")}return _}}function Qg(t,a){return`${Zc}/${t}/resolve/main/${a}`}class N1 extends Nl{preparePayload(a){const r={...ze(a.args,["inputs","parameters"]),...a.args.parameters,sync_mode:!0,prompt:a.args.inputs};return a.mapping?.adapter==="lora"&&a.mapping.adapterWeightsPath&&(r.loras=[{path:Qg(a.mapping.hfModelId,a.mapping.adapterWeightsPath),scale:1}],a.mapping.providerId==="fal-ai/lora"&&(r.model_name="stabilityai/stable-diffusion-xl-base-1.0")),r}async getResponse(a,r,l,s){if(typeof a=="object"&&"images"in a&&Array.isArray(a.images)&&a.images.length>0&&"url"in a.images[0]&&typeof a.images[0].url=="string")return s==="json"?{...a}:s==="url"?a.images[0].url:await(await fetch(a.images[0].url)).blob();throw new oe("Received malformed response from Fal.ai text-to-image API")}}class I1 extends Il{task;constructor(){super("https://queue.fal.run"),this.task="image-to-image"}makeRoute(a){return a.authMethod!=="provider-key"?`/${a.model}?_subdomain=queue`:`/${a.model}`}preparePayload(a){const r=a.args;return a.mapping?.adapter==="lora"&&a.mapping.adapterWeightsPath&&(r.loras=[{path:Qg(a.mapping.hfModelId,a.mapping.adapterWeightsPath),scale:1}]),r}async preparePayloadAsync(a){const r=a.inputs instanceof Blob?a.inputs.type:"image/png";return{...ze(a,["inputs","parameters"]),image_url:`data:${r};base64,${rn(new Uint8Array(a.inputs instanceof ArrayBuffer?a.inputs:await a.inputs.arrayBuffer()))}`,...a.parameters,...a}}async getResponse(a,r,l){const s=await this.getResponseFromQueueApi(a,r,l);if(typeof s=="object"&&s&&"images"in s&&Array.isArray(s.images)&&s.images.length>0&&typeof s.images[0]=="object"&&s.images[0]&&"url"in s.images[0]&&typeof s.images[0].url=="string"&&Xa(s.images[0].url))return await(await fetch(s.images[0].url)).blob();throw new oe(`Received malformed response from Fal.ai image-to-image API: expected { images: Array<{ url: string }> } result format, got instead: ${JSON.stringify(s)}`)}}class j1 extends Il{task;constructor(){super("https://queue.fal.run"),this.task="text-to-video"}makeRoute(a){return a.authMethod!=="provider-key"?`/${a.model}?_subdomain=queue`:`/${a.model}`}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,prompt:a.args.inputs}}async getResponse(a,r,l){const s=await this.getResponseFromQueueApi(a,r,l);if(typeof s=="object"&&s&&"video"in s&&typeof s.video=="object"&&s.video&&"url"in s.video&&typeof s.video.url=="string"&&Xa(s.video.url))return await(await fetch(s.video.url)).blob();throw new oe(`Received malformed response from Fal.ai text-to-video API: expected { video: { url: string } } result format, got instead: ${JSON.stringify(s)}`)}}class L1 extends Il{task;constructor(){super("https://queue.fal.run"),this.task="image-to-video"}makeRoute(a){return a.authMethod!=="provider-key"?`/${a.model}?_subdomain=queue`:`/${a.model}`}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,image_url:a.args.image_url}}async preparePayloadAsync(a){const r=a.inputs instanceof Blob?a.inputs.type:"image/png";return{...ze(a,["inputs","parameters"]),image_url:`data:${r};base64,${rn(new Uint8Array(a.inputs instanceof ArrayBuffer?a.inputs:await a.inputs.arrayBuffer()))}`,...a.parameters,...a}}async getResponse(a,r,l){const s=await this.getResponseFromQueueApi(a,r,l);if(typeof s=="object"&&s!==null&&"video"in s&&typeof s.video=="object"&&s.video!==null&&"url"in s.video&&typeof s.video.url=="string"&&"url"in s.video&&Xa(s.video.url))return await(await fetch(s.video.url)).blob();throw new oe(`Received malformed response from Fal.ai imageâtoâvideo API: expected { video: { url: string } }, got: ${JSON.stringify(s)}`)}}class z1 extends Nl{prepareHeaders(a,r){const l=super.prepareHeaders(a,r);return l["Content-Type"]="application/json",l}
62async getResponse(a){const r=a;if(typeof r?.text!="string")throw new oe(`Received malformed response from Fal.ai Automatic Speech Recognition API: expected { text: string } format, got instead: ${JSON.stringify(a)}`);return{text:r.text}}async preparePayloadAsync(a){const r="data"in a&&a.data instanceof Blob?a.data:"inputs"in a?a.inputs:void 0,l=r?.type;if(!l)throw new ft("Unable to determine the input's content-type. Make sure your are passing a Blob when using provider fal-ai.");if(!Qh.includes(l))throw new ft(`Provider fal-ai does not support blob type ${l} - supported content types are: ${Qh.join(", ")}`);const s=rn(new Uint8Array(await r.arrayBuffer()));return{..."data"in a?ze(a,"data"):ze(a,"inputs"),audio_url:`data:${l};base64,${s}`}}}class q1 extends Nl{preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,text:a.args.inputs}}async getResponse(a){const r=a;if(typeof r?.audio?.url!="string")throw new oe(`Received malformed response from Fal.ai Text-to-Speech API: expected { audio: { url: string } } format, got instead: ${JSON.stringify(a)}`);const l=await fetch(r.audio.url);if(!l.ok)throw new kt(`Failed to fetch audio from ${r.audio.url}: ${l.statusText}`,{url:r.audio.url,method:"GET",headers:{"Content-Type":"application/json"}},{requestId:l.headers.get("x-request-id")??"",status:l.status,body:await l.text()});try{return await l.blob()}catch(s){throw new kt(`Failed to fetch audio from ${r.audio.url}: ${s instanceof Error?s.message:String(s)}`,{url:r.audio.url,method:"GET",headers:{"Content-Type":"application/json"}},{requestId:l.headers.get("x-request-id")??"",status:l.status,body:await l.text()})}}}class B1 extends Il{task;constructor(){super("https://queue.fal.run"),this.task="image-segmentation"}makeRoute(a){return a.authMethod!=="provider-key"?`/${a.model}?_subdomain=queue`:`/${a.model}`}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,sync_mode:!0}}async preparePayloadAsync(a){const r="data"in a&&a.data instanceof Blob?a.data:"inputs"in a?a.inputs:void 0,l=r instanceof Blob?r.type:"image/png",s=rn(new Uint8Array(r instanceof ArrayBuffer?r:await r.arrayBuffer()));return{...ze(a,["inputs","parameters","data"]),...a.parameters,...a,image_url:`data:${l};base64,${s}`,sync_mode:!0}}async getResponse(a,r,l){const s=await this.getResponseFromQueueApi(a,r,l);if(typeof s=="object"&&s!==null&&"image"in s&&typeof s.image=="object"&&s.image!==null&&"url"in s.image&&typeof s.image.url=="string"){const c=await fetch(s.image.url);if(!c.ok)throw new kt(`Failed to fetch segmentation mask from ${s.image.url}`,{url:s.image.url,method:"GET"},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:await c.text()});const m=await(await c.blob()).arrayBuffer();return[{label:"mask",score:1,mask:rn(new Uint8Array(m))}]}throw new oe(`Received malformed response from Fal.ai image-segmentation API: expected { image: { url: string } } format, got instead: ${JSON.stringify(a)}`)}}const Kg="https://api.featherless.ai";class H1 extends Nt{constructor(){super("featherless-ai",Kg)}}class P1 extends Ta{constructor(){super("featherless-ai",Kg)}preparePayload(a){return{model:a.model,...ze(a.args,["inputs","parameters"]),...a.args.parameters?{max_tokens:a.args.parameters.max_new_tokens,...ze(a.args.parameters,"max_new_tokens")}:void 0,prompt:a.args.inputs}}
62async getResponse(a){if(typeof a=="object"&&"choices"in a&&Array.isArray(a?.choices)&&typeof a?.model=="string")return{generated_text:a.choices[0].text};throw new oe("Received malformed response from Featherless AI text generation API")}}class V1 extends Nt{constructor(){super("fireworks-ai","https://api.fireworks.ai")}makeRoute(){return"/inference/v1/chat/completions"}}const Zg="https://api.groq.com";class $1 extends Ta{constructor(){super("groq",Zg)}makeRoute(){return"/openai/v1/chat/completions"}}class F1 extends Nt{constructor(){super("groq",Zg)}makeRoute(){return"/openai/v1/chat/completions"}}const ef="https://api.hyperbolic.xyz";class X1 extends Nt{constructor(){super("hyperbolic",ef)}}class Y1 extends Ta{constructor(){super("hyperbolic",ef)}makeRoute(){return"v1/chat/completions"}preparePayload(a){return{messages:[{content:a.args.inputs,role:"user"}],...a.args.parameters?{max_tokens:a.args.parameters.max_new_tokens,...ze(a.args.parameters,"max_new_tokens")}:void 0,...ze(a.args,["inputs","parameters"]),model:a.model}}
62async getResponse(a){if(typeof a=="object"&&"choices"in a&&Array.isArray(a?.choices)&&typeof a?.model=="string")return{generated_text:a.choices[0].message.content};throw new oe("Received malformed response from Hyperbolic text generation API")}}class Q1 extends Ft{constructor(){super("hyperbolic",ef)}makeRoute(a){return"/v1/images/generations"}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,prompt:a.args.inputs,model_name:a.model}}async getResponse(a,r,l,s){if(typeof a=="object"&&"images"in a&&Array.isArray(a.images)&&a.images[0]&&typeof a.images[0].image=="string")return s==="json"?{...a}:s==="url"?`data:image/jpeg;base64,${a.images[0].image}`:fetch(`data:image/jpeg;base64,${a.images[0].image}`).then(c=>c.blob());throw new oe("Received malformed response from Hyperbolic text-to-image API")}}const jl="https://api.studio.nebius.ai";class K1 extends Nt{constructor(){super("nebius",jl)}preparePayload(a){const r=super.preparePayload(a),l=a.args.response_format;return l?.type==="json_schema"&&l.json_schema?.schema&&(r.guided_json=l.json_schema.schema),r}}class Z1 extends Ta{constructor(){super("nebius",jl)}preparePayload(a){return{...a.args,model:a.model,prompt:a.args.inputs}}
62async getResponse(a){if(typeof a=="object"&&"choices"in a&&Array.isArray(a?.choices)&&a.choices.length>0&&typeof a.choices[0]?.text=="string")return{generated_text:a.choices[0].text};throw new oe("Received malformed response from Nebius text generation API")}}class J1 extends Ft{constructor(){super("nebius",jl)}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,response_format:"b64_json",prompt:a.args.inputs,model:a.model}}makeRoute(){return"v1/images/generations"}async getResponse(a,r,l,s){if(typeof a=="object"&&"data"in a&&Array.isArray(a.data)&&a.data.length>0&&"b64_json"in a.data[0]&&typeof a.data[0].b64_json=="string"){if(s==="json")return{...a};const c=a.data[0].b64_json;return s==="url"?`data:image/jpeg;base64,${c}`:fetch(`data:image/jpeg;base64,${c}`).then(p=>p.blob())}throw new oe("Received malformed response from Nebius text-to-image API")}}class G1 extends Ft{constructor(){super("nebius",jl)}preparePayload(a){return{input:a.args.inputs,model:a.model}}makeRoute(){return"v1/embeddings"}
62async getResponse(a){return a.data.map(r=>r.embedding)}}const tf="https://api.novita.ai";class W1 extends Ta{constructor(){super("novita",tf)}makeRoute(){return"/v3/openai/chat/completions"}}class eS extends Nt{constructor(){super("novita",tf)}makeRoute(){return"/v3/openai/chat/completions"}}class tS extends Ft{constructor(){super("novita",tf)}makeRoute(a){return`/v3/async/${a.model}`}preparePayload(a){const{num_inference_steps:r,...l}=a.args.parameters??{};return{...ze(a.args,["inputs","parameters"]),...l,steps:r,prompt:a.args.inputs}}async getResponse(a,r,l){if(!r||!l)throw new ft("URL and headers are required for text-to-video task");const s=a.task_id;if(!s)throw new oe("Received malformed response from Novita text-to-video API: no task ID found in the response");const c=new URL(r),m=`${`${c.protocol}//${c.host}${c.host==="router.huggingface.co"?"/novita":""}`}/v3/async/task-result?task_id=${s}`;let b="",d;for(;b!=="TASK_STATUS_SUCCEED"&&b!=="TASK_STATUS_FAILED";){await Wc(500);const y=await fetch(m,{headers:l});if(!y.ok)throw new kt("Failed to fetch task result",{url:m,method:"GET",headers:l},{requestId:y.headers.get("x-request-id")??"",status:y.status,body:await y.text()});try{if(d=await y.json(),d&&typeof d=="object"&&"task"in d&&d.task&&typeof d.task=="object"&&"status"in d.task&&typeof d.task.status=="string")b=d.task.status;else throw new oe("Received malformed response from Novita text-to-video API: failed to get task status")}catch{throw new oe("Received malformed response from Novita text-to-video API: failed to parse task result")}}if(b==="TASK_STATUS_FAILED")throw new oe("Novita text-to-video task failed");if(typeof d=="object"&&d&&"videos"in d&&typeof d.videos=="object"&&d.videos&&Array.isArray(d.videos)&&d.videos.length>0&&"video_url"in d.videos[0]&&typeof d.videos[0].video_url=="string"&&Xa(d.videos[0].video_url))return await(await fetch(d.videos[0].video_url)).blob();throw new oe(`Received malformed response from Novita text-to-video API: expected { videos: [{ video_url: string }] } format, got instead: ${JSON.stringify(d)}`)}}const Jg="https://inference.api.nscale.com";class nS extends Nt{constructor(){super("nscale",Jg)}}class aS extends Ft{constructor(){super("nscale",Jg)}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,response_format:"b64_json",prompt:a.args.inputs,model:a.model}}makeRoute(){return"v1/images/generations"}async getResponse(a,r,l,s){if(typeof a=="object"&&"data"in a&&Array.isArray(a.data)&&a.data.length>0&&"b64_json"in a.data[0]&&typeof a.data[0].b64_json=="string"){if(s==="json")return{...a};const c=a.data[0].b64_json;return s==="url"?`data:image/jpeg;base64,${c}`:fetch(`data:image/jpeg;base64,${c}`).then(p=>p.blob())}throw new oe("Received malformed response from Nscale text-to-image API")}}const iS="https://api.openai.com";class rS extends Nt{constructor(){super("openai",iS,!0)}}const Gg="https://oai.endpoints.kepler.ai.cloud.ovh.net";class oS extends Nt{constructor(){super("ovhcloud",Gg)}}class lS extends Ta{constructor(){super("ovhcloud",Gg)}preparePayload(a){return{model:a.model,...ze(a.args,["inputs","parameters"]),...a.args.parameters?{max_tokens:a.args.parameters.max_new_tokens,...ze(a.args.parameters,"max_new_tokens")}:void 0,prompt:a.args.inputs}}
62async getResponse(a){if(typeof a=="object"&&"choices"in a&&Array.isArray(a?.choices)&&typeof a?.model=="string")return{generated_text:a.choices[0].text};throw new oe("Received malformed response from OVHcloud text generation API")}}class sS extends Nt{constructor(){super("publicai","https://api.publicai.co")}}class so extends Ft{constructor(a){super("replicate",a||"https://api.replicate.com")}makeRoute(a){return a.model.includes(":")?"v1/predictions":`v1/models/${a.model}/predictions`}preparePayload(a){return{input:{...ze(a.args,["inputs","parameters"]),...a.args.parameters,prompt:a.args.inputs},version:a.model.includes(":")?a.model.split(":")[1]:void 0}}prepareHeaders(a,r){const l={Authorization:`Bearer ${a.accessToken}`,Prefer:"wait"};return r||(l["Content-Type"]="application/json"),l}makeUrl(a){const r=this.makeBaseUrl(a);return a.model.includes(":")?`${r}/v1/predictions`:`${r}/v1/models/${a.model}/predictions`}}class uS extends so{preparePayload(a){return{input:{...ze(a.args,["inputs","parameters"]),...a.args.parameters,prompt:a.args.inputs,lora_weights:a.mapping?.adapter==="lora"&&a.mapping.adapterWeightsPath?`https://huggingface.co/${a.mapping.hfModelId}`:void 0},version:a.model.includes(":")?a.model.split(":")[1]:void 0}}async getResponse(a,r,l,s){if(typeof a=="object"&&"output"in a&&Array.isArray(a.output)&&a.output.length>0&&typeof a.output[0]=="string")return s==="json"?{...a}:s==="url"?a.output[0]:await(await fetch(a.output[0])).blob();throw new oe("Received malformed response from Replicate text-to-image API")}}class cS extends so{preparePayload(a){const r=super.preparePayload(a),l=r.input;if(typeof l=="object"&&l!==null&&"prompt"in l){const s=l;s.text=s.prompt,delete s.prompt}return r}
62async getResponse(a){if(a instanceof Blob)return a;if(a&&typeof a=="object"&&"output"in a){if(typeof a.output=="string")return await(await fetch(a.output)).blob();if(Array.isArray(a.output))return await(await fetch(a.output[0])).blob()}throw new oe("Received malformed response from Replicate text-to-speech API")}}class fS extends so{async getResponse(a){if(typeof a=="object"&&a&&"output"in a&&typeof a.output=="string"&&Xa(a.output))return await(await fetch(a.output)).blob();throw new oe("Received malformed response from Replicate text-to-video API")}}class pS extends so{preparePayload(a){return{input:{...ze(a.args,["inputs","parameters"]),...a.args.parameters,audio:a.args.inputs},version:a.model.includes(":")?a.model.split(":")[1]:void 0}}async preparePayloadAsync(a){const r="data"in a&&a.data instanceof Blob?a.data:"inputs"in a?a.inputs:void 0;if(!r||!(r instanceof Blob))throw new Error("Audio input must be a Blob");const l=new Uint8Array(await r.arrayBuffer()),s=rn(l),c=`data:${r.type||"audio/wav"};base64,${s}`;return{..."data"in a?ze(a,"data"):ze(a,"inputs"),inputs:c}}
62async getResponse(a){if(typeof a?.output=="string")return{text:a.output};if(Array.isArray(a?.output)&&typeof a.output[0]=="string")return{text:a.output[0]};const r=a?.output;if(r&&typeof r=="object"){if(typeof r.transcription=="string")return{text:r.transcription};if(typeof r.translation=="string")return{text:r.translation};if(typeof r.txt_file=="string")return{text:await(await fetch(r.txt_file)).text()}}throw new oe("Received malformed response from Replicate automatic-speech-recognition API")}}class dS extends so{preparePayload(a){return{input:{...ze(a.args,["inputs","parameters"]),...a.args.parameters,input_image:a.args.inputs,lora_weights:a.mapping?.adapter==="lora"&&a.mapping.adapterWeightsPath?`https://huggingface.co/${a.mapping.hfModelId}`:void 0},version:a.model.includes(":")?a.model.split(":")[1]:void 0}}async preparePayloadAsync(a){const{inputs:r,...l}=a,s=new Uint8Array(await r.arrayBuffer()),c=rn(s),p=`data:${r.type||"image/jpeg"};base64,${c}`;return{...l,inputs:p}}async getResponse(a){if(typeof a=="object"&&a&&"output"in a&&Array.isArray(a.output)&&a.output.length>0&&typeof a.output[0]=="string")return await(await fetch(a.output[0])).blob();if(typeof a=="object"&&a&&"output"in a&&typeof a.output=="string"&&Xa(a.output))return await(await fetch(a.output)).blob();throw new oe("Received malformed response from Replicate image-to-image API")}}class mS extends Nt{constructor(){super("sambanova","https://api.sambanova.ai")}preparePayload(a){const r=a.args.response_format;return r?.type==="json_schema"&&r.json_schema&&(r.json_schema.strict??!0)&&(r.json_schema.strict=!1),super.preparePayload(a)}}class hS extends Ft{constructor(){super("sambanova","https://api.sambanova.ai")}makeRoute(){return"/v1/embeddings"}
62async getResponse(a){if(typeof a=="object"&&"data"in a&&Array.isArray(a.data))return a.data.map(r=>r.embedding);throw new oe("Received malformed response from Sambanova feature-extraction (embeddings) API")}preparePayload(a){return{model:a.model,input:a.args.inputs,...a.args}}}const nf="https://api.scaleway.ai";class gS extends Nt{constructor(){super("scaleway",nf)}}class yS extends Ta{constructor(){super("scaleway",nf)}preparePayload(a){return{model:a.model,...a.args,prompt:a.args.inputs}}async getResponse(a){if(typeof a=="object"&&a!==null&&"choices"in a&&Array.isArray(a.choices)&&a.choices.length>0){const r=a.choices[0];if(typeof r=="object"&&r&&"text"in r&&r.text&&typeof r.text=="string")return{generated_text:r.text}}throw new oe("Received malformed response from Scaleway text generation API")}}class bS extends Ft{constructor(){super("scaleway",nf)}preparePayload(a){return{input:a.args.inputs,model:a.model}}makeRoute(){return"v1/embeddings"}async getResponse(a){return a.data.map(r=>r.embedding)}}const af="https://api.together.xyz";class vS extends Nt{constructor(){super("together",af)}preparePayload(a){const r=super.preparePayload(a),l=r.response_format;return l?.type==="json_schema"&&l?.json_schema?.schema&&(r.response_format={type:"json_schema",schema:l.json_schema.schema}),r}}class wS extends Ta{constructor(){super("together",af)}preparePayload(a){return{model:a.model,...a.args,prompt:a.args.inputs}}
62async getResponse(a){if(typeof a=="object"&&"choices"in a&&Array.isArray(a?.choices)&&typeof a?.model=="string")return{generated_text:a.choices[0].text};throw new oe("Received malformed response from Together text generation API")}}class _S extends Ft{constructor(){super("together",af)}makeRoute(){return"v1/images/generations"}preparePayload(a){return{...ze(a.args,["inputs","parameters"]),...a.args.parameters,prompt:a.args.inputs,response_format:"base64",model:a.model}}async getResponse(a,r,l,s){if(typeof a=="object"&&"data"in a&&Array.isArray(a.data)&&a.data.length>0&&"b64_json"in a.data[0]&&typeof a.data[0].b64_json=="string"){if(s==="json")return{...a};const c=a.data[0].b64_json;return s==="url"?`data:image/jpeg;base64,${c}`:fetch(`data:image/jpeg;base64,${c}`).then(p=>p.blob())}throw new oe("Received malformed response from Together text-to-image API")}}const vc={"black-forest-labs":{"text-to-image":new O1},cerebras:{conversational:new M1},cohere:{conversational:new U1},"fal-ai":{"text-to-image":new N1,"text-to-speech":new q1,"text-to-video":new j1,"image-to-image":new I1,"automatic-speech-recognition":new z1,"image-segmentation":new B1,"image-to-video":new L1},"featherless-ai":{conversational:new H1,"text-generation":new P1},"hf-inference":{"text-to-image":new t1,conversational:new n1,"text-generation":new a1,"text-classification":new h1,"question-answering":new g1,"audio-classification":new i1,"automatic-speech-recognition":new r1,"fill-mask":new y1,"feature-extraction":new s1,"image-classification":new u1,"image-segmentation":new c1,"document-question-answering":new l1,"image-to-text":new f1,"object-detection":new d1,"audio-to-audio":new o1,"zero-shot-image-classification":new m1,"zero-shot-classification":new Gc,"image-to-image":new p1,"sentence-similarity":new b1,"table-question-answering":new Dl,"tabular-classification":new S1,"text-to-speech":new x1,"token-classification":new v1,translation:new w1,summarization:new _1,"visual-question-answering":new A1,"tabular-regression":new k1,"text-to-audio":new T1},"fireworks-ai":{conversational:new V1},groq:{conversational:new F1,"text-generation":new $1},hyperbolic:{"text-to-image":new Q1,conversational:new X1,"text-generation":new Y1},nebius:{"text-to-image":new J1,conversational:new K1,"text-generation":new Z1,"feature-extraction":new G1},novita:{conversational:new eS,"text-generation":new W1,"text-to-video":new tS},nscale:{"text-to-image":new aS,conversational:new nS},openai:{conversational:new rS},ovhcloud:{conversational:new oS,"text-generation":new lS},publicai:{conversational:new sS},replicate:{"text-to-image":new uS,"text-to-speech":new cS,"text-to-video":new fS,"image-to-image":new dS,"automatic-speech-recognition":new pS},sambanova:{conversational:new mS,"feature-extraction":new hS},scaleway:{conversational:new gS,"text-generation":new yS,"feature-extraction":new bS},together:{"text-to-image":new _S,conversational:new vS,"text-generation":new wS}};function je(t,a){if(t==="hf-inference"&&!a||t==="auto")return new Xe;if(!a)throw new ft("you need to provide a task name when using an external provider, e.g. 'text-to-image'");if(!(t in vc))throw new ft(`Provider '${t}' not supported. Available providers: ${Object.keys(vc)}`);const r=vc[t];if(!r||!(a in r))throw new ft(`Task '${a}' not supported for provider '${t}'. Available tasks: ${Object.keys(r??{})}`);return r[a]}const xS="4.8.0",SS="@huggingface/inference";let wc=null;async function Ya(t,a,r){const{model:l}=t,s=a.provider,{task:c}=r??{};if(t.endpointUrl&&s!=="hf-inference")throw new ft("Cannot use endpointUrl with a third-party provider.");if(l&&Xa(l))throw new ft("Model URLs are no longer supported. Use endpointUrl instead.");if(t.endpointUrl)return Kh(l??t.endpointUrl,a,t,void 0,r);if(!l&&!c)throw new ft("No model provided, and no task has been specified.");const p=l??await AS(c);if(a.clientSideRoutingOnly&&!l)throw new ft(`Provider ${s} requires a model ID to be passed directly.`);const m=a.clientSideRoutingOnly?{provider:s,providerId:TS(l,s),hfModelId:l,status:"live",task:c}:await R1({modelId:p,task:c,provider:s,accessToken:t.accessToken},{fetch:r?.fetch});if(!m)throw new ft(`We have not been able to find inference provider information for model ${p}.`);return Kh(m.providerId,a,t,m,r)}function Kh(t,a,r,l,s){const{accessToken:c,endpointUrl:p,provider:m,model:b,...d}=r,y=a.provider,{includeCredentials:w,task:S,signal:_,billTo:B}=s??{},V=(()=>
62{if(a.clientSideRoutingOnly&&c&&c.startsWith("hf_"))throw new ft(`Provider ${y} is closed-source and does not support HF tokens.`);return c?c.startsWith("hf_")?"hf-token":"provider-key":w==="include"?"credentials-include":"none"})(),K=p??t,H=a.makeUrl({authMethod:V,model:K,task:S}),G=a.prepareHeaders({accessToken:c,authMethod:V},"data"in r&&!!r.data);B&&(G[W0]=B);const we=[`${SS}/${xS}`,typeof navigator<"u"?navigator.userAgent:void 0].filter(_e=>_e!==void 0).join(" ");G["User-Agent"]=we;const W=a.makeBody({args:d,model:t,task:S,mapping:l});let L;typeof w=="string"?L=w:w===!0&&(L="include");const ue={headers:G,method:"POST",body:W,...L?{credentials:L}:void 0,signal:_};return{url:H,info:ue}}async function AS(t){wc||(wc=await kS());const a=wc[t];if((a?.models.length??0)<=0)throw new ft(`No default model defined for task ${t}, please define the model explicitly.`);return a.models[0].id}async function kS(){const t=`${Zc}/api/tasks`,a=await fetch(t);if(!a.ok)throw new Wr("Failed to load tasks definitions from Hugging Face Hub.",{url:t,method:"GET"},{requestId:a.headers.get("x-request-id")??"",status:a.status,body:await a.text()});return await a.json()}function TS(t,a){if(!t.startsWith(`${a}/`))throw new ft(`Models from ${a} must be prefixed by "${a}/". Got "${t}".`);return t.slice(a.length+1)}function ES(t){let a,r,l,s=!1;return function(p){a===void 0?(a=p,r=0,l=-1):a=RS(a,p);const m=a.length;let b=0;for(;r<m;){s&&(a[r]===10&&(b=++r),s=!1);let d=-1;for(;r<m&&d===-1;++r)switch(a[r]){case 58:l===-1&&(l=r-b);break;case 13:s=!0;case 10:d=r;break}if(d===-1)break;t(a.subarray(b,d),l),b=r,l=-1}b===m?a=void 0:b!==0&&(a=a.subarray(b),r-=b)}}function CS(t,a,r){let l=Zh();const s=new TextDecoder;return function(p,m){if(p.length===0)r?.(l),l=Zh();else if(m>0){const b=s.decode(p.subarray(0,m)),d=m+(p[m+1]===32?2:1),y=s.decode(p.subarray(d));switch(b){case"data":l.data=l.data?l.data+` 63`+y:y;break;case"event":l.event=y;break;case"id":t(l.id=y);break;case"retry":{const w=parseInt(y,10);isNaN(w)||a(l.retry=w);break}}}}}function RS(t,a){const r=new Uint8Array(t.length+a.length);return r.set(t),r.set(a,t.length),r}function Zh(){return{data:"",event:"",id:"",retry:void 0}}function xn(t){let a=null;if(t instanceof Blob||t instanceof ArrayBuffer)a="[Blob or ArrayBuffer]";else if(typeof t=="string")try{a=JSON.parse(t)}catch{a=t}return a.accessToken&&(a.accessToken="[REDACTED]"),a}async function Pe(t,a,r){const{url:l,info:s}=await Ya(t,a,r),c=await(r?.fetch??fetch)(l,s),p={url:l,info:s};if(r?.retry_on_error!==!1&&c.status===503)return Pe(t,a,r);if(!c.ok){const b=c.headers.get("Content-Type");if(["application/json","application/problem+json"].some(y=>b?.startsWith(y))){const y=await c.json();throw[400,422,404,500].includes(c.status)&&r?.chatCompletion?new kt(`Provider ${t.provider} does not seem to support chat completion for model ${t.model} . Error: ${JSON.stringify(y.error)}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y}):typeof y.error=="string"||typeof y.detail=="string"||typeof y.message=="string"?new kt(`Failed to perform inference: ${y.error??y.detail??y.message}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y}):new kt("Failed to perform inference: an HTTP error occurred when requesting the provider.",{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y})}const d=b?.startsWith("text/plain;")?await c.text():void 0;throw new kt(`Failed to perform inference: ${d??"an HTTP error occurred when requesting the provider"}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:d??""})}return c.headers.get("Content-Type")?.startsWith("application/json")?{data:await c.json(),requestContext:p}:{data:await c.blob(),requestContext:p}}async function*Ll(t,a,r){const{url:l,info:s}=await Ya({...t,stream:!0}
63,a,r),c=await(r?.fetch??fetch)(l,s);if(r?.retry_on_error!==!1&&c.status===503)return yield*Ll(t,a,r);if(!c.ok){if(c.headers.get("Content-Type")?.startsWith("application/json")){const y=await c.json();if([400,422,404,500].includes(c.status)&&r?.chatCompletion)throw new kt(`Provider ${t.provider} does not seem to support chat completion for model ${t.model} . Error: ${JSON.stringify(y.error)}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y});if(typeof y.error=="string")throw new kt(`Failed to perform inference: ${y.error}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y});if(y.error&&"message"in y.error&&typeof y.error.message=="string")throw new kt(`Failed to perform inference: ${y.error.message}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y});if(typeof y.message=="string")throw new kt(`Failed to perform inference: ${y.message}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:y})}throw new kt("Failed to perform inference: an HTTP error occurred when requesting the provider.",{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:""})}if(!c.headers.get("content-type")?.startsWith("text/event-stream"))throw new kt("Failed to perform inference: server does not support event stream content type, it returned "+c.headers.get("content-type"),{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:""});if(!c.body)return;const p=c.body.getReader();let m=[];const d=ES(CS(()=>{},()=>{},y=>{m.push(y)}));try{for(;;){const{done:y,value:w}=await p.read();if(y)return;d(w);for(const S of m)if(S.data.length>0){if(S.data==="[DONE]")return;const _=JSON.parse(S.data);if(typeof _=="object"&&_!==null&&"error"in _){const B=typeof _.error=="string"?_.error:typeof _.error=="object"&&_.error&&"message"in _.error&&typeof _.error.message=="string"?_.error.message:JSON.stringify(_.error);throw new kt(`Failed to perform inference: an occurred while streaming the response: ${B}`,{url:l,method:s.method??"GET",headers:s.headers,body:xn(s.body)},{requestId:c.headers.get("x-request-id")??"",status:c.status,body:_})}yield _}m=[]}}finally{p.releaseLock()}}async function DS(t,a){lo().warn("The request method is deprecated and will be removed in a future version of huggingface.js. Use specific task functions instead.");const l=await Ie(t.provider,t.model,t.endpointUrl),s=je(l,a?.task);return(await Pe(t,s,a)).data}async function*OS(t,a){lo().warn("The streamingRequest method is deprecated and will be removed in a future version of huggingface.js. Use specific task functions instead.");const l=await Ie(t.provider,t.model,t.endpointUrl),s=je(l,a?.task);yield*Ll(t,s,a)}function Wg(t){return"data"in t?t:{...ze(t,"inputs"),data:t.inputs}}async function MS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"audio-classification"),s=Wg(t),{data:c}=await Pe(s,l,{...a,task:"audio-classification"});return l.getResponse(c)}async function US(t,a){const r="inputs"in t?t.model:void 0,l=await Ie(t.provider,r),s=je(l,"audio-to-audio"),c=Wg(t),{data:p}=await Pe(c,s,{...a,task:"audio-to-audio"});return s.getResponse(p)}async function NS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"automatic-speech-recognition"),s=await l.preparePayloadAsync(t),{data:c}=await Pe(s,l,{...a,task:"automatic-speech-recognition"});return l.getResponse(c)}async function IS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"text-to-speech"),{data:s}=await Pe(t,l,{...a,task:"text-to-speech"});return l.getResponse(s)}function rf(t){return"data"in t?t:{...ze(t,"inputs"),data:t.inputs}}async function jS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"image-classification"),s=rf(t),{data:c}=await Pe(s,l,{...a,task:"image-classification"});return l.getResponse(c)}async function LS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"image-segmentation"),s=await l.preparePayloadAsync(t),{data:c}=await Pe(s,l,{...a,task:"image-segmentation"}),{url:p,info:m}=await Ya(t,l,{...a,task:"image-segmentation"});return l.getResponse(c,p,m.headers)}async function zS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"image-to-image"),s=await l.preparePayloadAsync(t),{data:c}=await Pe(s,l,{...a,task:"image-to-image"}),{url:p,info:m}=await Ya(t,l,{...a,task:"image-to-image"});return l.getResponse(c,p,m.headers)}async function qS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"image-to-text"),s=rf(t),{data:c}=await Pe(s,l,{...a,task:"image-to-text"});return l.getResponse(c[0])}async function BS(t,a){const r=await Ie(t.provider,t.model,t.endpointUrl),l=je(r,"image-to-vi
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At}evaluateSet(t,a){const r=t.value?this.evaluate(t.value,a):this.evaluateBlock(t.body,a);if(t.assignee.type==="Identifier"){const l=t.assignee.value;a.setVariable(l,r)}else if(t.assignee.type==="TupleLiteral"){const l=t.assignee;if(!(r instanceof Be))throw new Error(`Cannot unpack non-iterable type in set: ${r.type}`);const s=r.value;if(s.length!==l.value.length)throw new Error(`Too ${l.value.length>s.length?"few":"many"} items to unpack in set`);for(let c=0;c<l.value.length;++c){const p=l.value[c];if(p.type!=="Identifier")throw new Error(`Cannot unpack to non-identifier in set: ${p.type}`);a.setVariable(p.value,s[c])}}else if(t.assignee.type==="MemberExpression"){const l=t.assignee,s=this.evaluate(l.object,a);if(!(s instanceof Pt))throw new Error("Cannot assign to member of non-object");if(l.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");s.value.set(l.property.value,r)}else throw new Error(`Invalid LHS inside assignment 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c;r.at(-1)?.type==="KeywordArgumentsValue"&&(c=r.pop());for(let p=0;p<t.args.length;++p){const m=t.args[p],b=r[p];if(m.type==="Identifier"){const d=m;if(!b)throw new Error(`Missing positional argument: ${d.value}`);s.setVariable(d.value,b)}else if(m.type==="KeywordArgumentExpression"){const d=m,y=b??c?.value.get(d.key.value)??this.evaluate(d.value,s);s.setVariable(d.key.value,y)}else throw new Error(`Unknown argument type: ${m.type}`)}return this.evaluateBlock(t.body,s)})),new Ut}
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sk={transformers:["audio-classification","automatic-speech-recognition","depth-estimation","document-question-answering","feature-extraction","fill-mask","image-classification","image-feature-extraction","image-segmentation","image-to-image","image-to-text","image-text-to-text","mask-generation","object-detection","question-answering","summarization","table-question-answering","text-classification","text-generation","text-to-audio","text-to-speech","token-classification","translation","video-classification","visual-question-answering","zero-shot-classification","zero-shot-image-classification","zero-shot-object-detection"]},lf={"text-classification":{name:"Text Classification",subtasks:[{type:"acceptability-classification",name:"Acceptability Classification"},{type:"entity-linking-classification",name:"Entity Linking Classification"},{type:"fact-checking",name:"Fact Checking"},{type:"intent-classification",name:"Intent Classification"},{type:"language-identification",name:"Language Identification"},{type:"multi-class-classification",name:"Multi Class Classification"},{type:"multi-label-classification",name:"Multi Label Classification"},{type:"multi-input-text-classification",name:"Multi-input Text Classification"},{type:"natural-language-inference",name:"Natural Language Inference"},{type:"semantic-similarity-classification",name:"Semantic Similarity Classification"},{type:"sentiment-classification",name:"Sentiment Classification"},{type:"topic-classification",name:"Topic Classification"},{type:"semantic-similarity-scoring",name:"Semantic Similarity Scoring"},{type:"sentiment-scoring",name:"Sentiment Scoring"},{type:"sentiment-analysis",name:"Sentiment Analysis"},{type:"hate-speech-detection",name:"Hate Speech Detection"},{type:"text-scoring",name:"Text Scoring"}],modality:"nlp"},"token-classification":{name:"Token Classification",subtasks:[{type:"named-entity-recognition",name:"Named Entity Recognition"},{type:"part-of-speech",name:"Part of Speech"},{type:"parsing",name:"Parsing"},{type:"lemmatization",name:"Lemmatization"},{type:"word-sense-disambiguation",name:"Word Sense Disambiguation"},{type:"coreference-resolution",name:"Coreference-resolution"}],modality:"nlp"},"table-question-answering":{name:"Table Question Answering",modality:"nlp"},"question-answering":{name:"Question Answering",subtasks:[{type:"extractive-qa",name:"Extractive QA"},{type:"open-domain-qa",name:"Open Domain QA"},{type:"closed-domain-qa",name:"Closed Domain QA"}],modality:"nlp"},"zero-shot-classification":{name:"Zero-Shot Classification",modality:"nlp"},translation:{name:"Translation",modality:"nlp"},summarization:{name:"Summarization",subtasks:[{type:"news-articles-summarization",name:"News Articles Summarization"},{type:"news-articles-headline-generation",name:"News Articles Headline Generation"}],modality:"nlp"},"feature-extraction":{name:"Feature Extraction",modality:"nlp"},"text-generation":{name:"Text Generation",subtasks:[{type:"dialogue-modeling",name:"Dialogue Modeling"},{type:"dialogue-generation",name:"Dialogue Generation"},{type:"conversational",name:"Conversational"},{type:"language-modeling",name:"Language Modeling"},{type:"text-simplification",name:"Text simplification"},{type:"explanation-generation",name:"Explanation Generation"},{type:"abstractive-qa",name:"Abstractive QA"},{type:"open-domain-abstractive-qa",name:"Open Domain Abstractive QA"},{type:"closed-domain-qa",name:"Closed Domain QA"},{type:"open-book-qa",name:"Open Book QA"},{type:"closed-book-qa",name:"Closed Book QA"},{type:"text2text-generation",name:"Text2Text Generation"}],modality:"nlp"},"fill-mask":{name:"Fill-Mask",subtasks:[{type:"slot-filling",name:"Slot Filling"},{type:"masked-language-modeling",name:"Masked Language Modeling"}],modality:"nlp"},"sentence-similarity":{name:"Sentence Similarity",modality:"nlp"},"text-to-speech":{name:"Text-to-Speech",modality:"audio"},"text-to-audio":{name:"Text-to-Audio",modality:"audio"},"automatic-speech-recognition":{name:"Automatic Speech Recognition",modality:"audio"},"audio-to-audio":{name:"Audio-to-Audio",modality:"audio"},"audio-classification":{name:"Audio Classification",subtasks:[{type:"keyword-spotting",name:"Keyword Spotting"},{type:"speaker-identification",name:"Speaker Identification"},{type:"audio-intent-classification",name:"Audio Intent Classification"},{type:"audio-emotion-recognition",name:"Audio Emotion Recognition"},{type:"audio-language-identification",name:"Audio Language Identification"}],modality:"audio"},"audio-text-to-text":{name:"Audio-Text-to-Text",modality:"multimodal",hideInDatasets:!0},"voice-activity-detection":{name:"Voice Activity Detection",modality:"audio"},"depth-estimation":{name:"Depth Estimation",modality:"cv"},"image-classification":{name:"Image 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It is calculated as the logarithm of the target signal divided by the background noise, in decibels.",id:"snri"},{description:"The Signal-to-Distortion ratio is the relationship between the target signal and the sum of noise, interference, and artifact errors",id:"sdri"}],models:[{description:"A speech enhancement model.",id:"ResembleAI/resemble-enhance"},{description:"A model that can change the voice in a speech recording.",id:"microsoft/speecht5_vc"}],spaces:[{description:"An application for speech separation.",id:"younver/speechbrain-speech-separation"},{description:"An application for audio style transfer.",id:"nakas/audio-diffusion_style_transfer"}],summary:"Audio-to-Audio is a family of tasks in which the input is an audio and the output is one or multiple generated audios. Some example tasks are speech enhancement and source separation.",widgetModels:["speechbrain/sepformer-wham"],youtubeId:"iohj7nCCYoM"},dk={datasets:[{description:"31,175 hours of multilingual audio-text dataset in 108 languages.",id:"mozilla-foundation/common_voice_17_0"},{description:"Multilingual and diverse audio dataset with 101k hours of audio.",id:"amphion/Emilia-Dataset"},{description:"A dataset with 44.6k hours of English speaker data and 6k hours of other language speakers.",id:"parler-tts/mls_eng"},{description:"A multilingual audio dataset with 370K hours of audio.",id:"espnet/yodas"}],demo:{inputs:[{filename:"input.flac",type:"audio"}],outputs:[{label:"Transcript",content:"Going along slushy country roads and speaking to damp audiences in...",type:"text"}]},metrics:[{description:"",id:"wer"},{description:"",id:"cer"}],models:[{description:"A powerful ASR model by OpenAI.",id:"openai/whisper-large-v3"},{description:"A good generic speech model by MetaAI for fine-tuning.",id:"facebook/w2v-bert-2.0"},{description:"An end-to-end model that performs ASR and Speech Translation by MetaAI.",id:"facebook/seamless-m4t-v2-large"},{description:"A powerful multilingual ASR and Speech Translation model by Nvidia.",id:"nvidia/canary-1b"},{description:"Powerful speaker diarization model.",id:"pyannote/speaker-diarization-3.1"}],spaces:[{description:"A powerful general-purpose speech recognition application.",id:"hf-audio/whisper-large-v3"},{description:"Latest ASR model from Useful Sensors.",id:"mrfakename/Moonshinex"},{description:"A high quality speech and text translation model by Meta.",id:"facebook/seamless_m4t"},{description:"A powerful multilingual ASR and Speech Translation model by Nvidia",id:"nvidia/canary-1b"}],summary:"Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.",widgetModels:["openai/whisper-large-v3"],youtubeId:"TksaY_FDgnk"},mk={datasets:[{description:"Largest document understanding dataset.",id:"HuggingFaceM4/Docmatix"},{description:"Dataset from the 2020 DocVQA challenge. The documents are taken from the UCSF Industry Documents Library.",id:"eliolio/docvqa"}],demo:{inputs:[{label:"Question",content:"What is the idea behind the consumer relations efficie
71ncy team?",type:"text"},{filename:"document-question-answering-input.png",type:"img"}],outputs:[{label:"Answer",content:"Balance cost efficiency with quality customer service",type:"text"}]},metrics:[{description:"The evaluation metric for the DocVQA challenge is the Average Normalized Levenshtein Similarity (ANLS). This metric is flexible to character regognition errors and compares the predicted answer with the ground truth answer.",id:"anls"},{description:"Exact Match is a metric based on the strict character match of the predicted answer and the right answer. For answers predicted correctly, the Exact Match will be 1. Even if only one character is different, Exact Match will be 0",id:"exact-match"}],models:[{description:"A robust document question answering model.",id:"impira/layoutlm-document-qa"},{description:"A document question answering model specialized in invoices.",id:"impira/layoutlm-invoices"},{description:"A special model for OCR-free document question answering.",id:"microsoft/udop-large"},{description:"A powerful model for document question answering.",id:"google/pix2struct-docvqa-large"}],spaces:[{description:"A robust document question answering application.",id:"impira/docquery"},{description:"An application that can answer questions from invoices.",id:"impira/invoices"},{description:"An application to compare different document question answering models.",id:"merve/compare_docvqa_models"}],summary:"Document Question Answering (also known as Document Visual Question Answering) is the task of answering questions on document images. Document question answering models take a (document, question) pair as input and return an answer in natural language. Models usually rely on multi-modal features, combining text, position of words (bounding-boxes) and image.",widgetModels:["impira/layoutlm-invoices"],youtubeId:""},hk={datasets:[{description:"Wikipedia dataset containing cleaned articles of all languages. 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Images are expected to have only one class for each image. Image classification models take an image as input and return a prediction about which class the image belongs to.",widgetModels:["google/vit-base-patch16-224"],youtubeId:"tjAIM7BOYhw"},bk={datasets:[{description:"ImageNet-1K is a image classification dataset in which images are used to train image-feature-extraction models.",id:"imagenet-1k"}],demo:{inputs:[{filename:"mask-generation-input.png",type:"img"}],outputs:[{table:[["Dimension 1","Dimension 2","Dimension 3"],["0.21236686408519745","1.0919708013534546","0.8512550592422485"],["0.809657871723175","-0.18544459342956543","-0.7851548194885254"],["1.3103108406066895","-0.2479034662246704","-0.9107287526130676"],["1.8536205291748047","-0.36419737339019775","0.09717650711536407"]],type:"tabular"}]},metrics:[],models:[{description:"A powerful image feature extraction model.",id:"timm/vit_large_patch14_dinov2.lvd142m"},{description:"A strong image feature extraction model.",id:"nvidia/MambaVision-T-1K"},{description:"A robust image feature extraction model.",id:"facebook/dino-vitb16"},{description:"Cutting-edge image feature extraction model.",id:"apple/aimv2-large-patch14-336-distilled"},{description:"Strong image feature extraction model that can be used on images and documents.",id:"OpenGVLab/InternViT-6B-448px-V1-2"}],spaces:[{description:"A leaderboard to evaluate different image-feature-extraction models on classification performances",id:"timm/leaderboard"}],summary:"Image feature extraction is the task of extracting features learnt in a computer vision model.",widgetModels:[]},vk={datasets:[{description:"Synthetic dataset, for image relighting",id:"VIDIT"},{description:"Multiple images of celebrities, used for facial expression translation",id:"huggan/CelebA-faces"},{description:"12M image-caption pairs.",id:"Spawning/PD12M"}],demo:{inputs:[{filename:"image-to-image-input.jpeg",type:"img"}],outputs:[{filename:"image-to-image-output.png",type:"img"}]},isPlaceholder:!1,metrics:[{description:"Peak Signal to Noise Ratio (PSNR) is an approximation of the human perception, considering the ratio of the absolute intensity with respect to the variations. Measured in dB, a high value indicates a high fidelity.",id:"PSNR"},{description:"Structural Similarity Index (SSIM) is a perceptual metric which compares the luminance, contrast and structure of two images. The values of SSIM range between -1 and 1, and higher values indicate closer resemblance to the original image.",id:"SSIM"},{description:"Inception Score (IS) is an analysis of the labels predicted by an image classification model when presented with a sample of the generated images.",id:"IS"}],models:[{description:"An image-to-image model to improve image resolution.",id:"fal/AuraSR-v2"},{description:"Powerful image editing model.",id:"black-forest-labs/FLUX.1-Kontext-dev"},{description:"Virtual try-on model.",id:"yisol/IDM-VTON"},{description:"Image re-lighting model.",id:"kontext-community/relighting-kontext-dev-lora-v3"},{description:"Strong model for inpainting and outpainting.",id:"black-forest-labs/FLUX.1-Fill-dev"},{description:"Strong model for image editing using depth maps.",id:"black-forest-labs/FLUX.1-Depth-dev-lora"}],spaces:[{description:"Image editing application.",id:"black-forest-labs/FLUX.1-Kontext-Dev"},{description:"Image relighting application.",id:"lllyasviel/iclight-v2-vary"},{description:"An application for image upscaling.",id:"jasperai/Flux.1-dev-Controlnet-Upscaler"}],summary:"Image-to-image is the task of transforming an input image through a variety of possible manipulations and enhancements, such as super-resolution, image inpainting, colorization, and more.",widgetModels:["Qwen/Qwen-Image"],youtubeId:""},wk={datasets:[{description:"Dataset from 12M image-text of Reddit",id:"red_caps"},{description:"Dataset from 3.3M images of Google",id:"datasets/conceptual_captions"}],demo:{inputs:[{filename:"savanna.jpg",type:"img"}],outputs:[{label:"Detailed description",content:"a herd of giraffes and zebras grazing in a field",type:"text"}]},metrics:[],models:[{description:"Strong OCR model.",id:"allenai/olmOCR-7B-0725"},{description:"Powerful image captioning model.",id:"fancyfeast/llama-joycaption-beta-one-hf-llava"}],spaces:[{description:"SVG generator app from images.",id:"multimodalart/OmniSVG-3B"},{description:"An application that converts documents to markdown.",id:"numind/NuMarkdown-8B-Thinking"},{description:"An application that can caption images.",id:"fancyfeast/joy-caption-beta-one"}],summary:"Image to text models output a text from a given image. 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71,metrics:[],models:[{description:"Small and efficient yet powerful vision language model.",id:"HuggingFaceTB/SmolVLM-Instruct"},{description:"Cutting-edge reasoning vision language model.",id:"zai-org/GLM-4.5V"},{description:"Cutting-edge small vision language model to convert documents to text.",id:"rednote-hilab/dots.ocr"},{description:"Small yet powerful model.",id:"Qwen/Qwen2.5-VL-3B-Instruct"},{description:"Image-text-to-text model with agentic capabilities.",id:"microsoft/Magma-8B"}],spaces:[{description:"Leaderboard to evaluate vision language models.",id:"opencompass/open_vlm_leaderboard"},{description:"An application that compares object detection capabilities of different vision language models.",id:"sergiopaniego/vlm_object_understanding"},{description:"An application to compare different OCR models.",id:"prithivMLmods/Multimodal-OCR"}],summary:"Image-text-to-text models take in an image and text prompt and output text. These models are also called vision-language models, or VLMs. The difference from image-to-text models is that these models take an additional text input, not restricting the model to certain use cases like image captioning, and may also be trained to accept a conversation as input.",widgetModels:["zai-org/GLM-4.5V"],youtubeId:"IoGaGfU1CIg"},xk={datasets:[{description:"Scene segmentation dataset.",id:"scene_parse_150"}],demo:{inputs:[{filename:"image-segmentation-input.jpeg",type:"img"}],outputs:[{filename:"image-segmentation-output.png",type:"img"}]},metrics:[{description:"Average Precision (AP) is the Area Under the PR Curve (AUC-PR). It is calculated for each semantic class separately",id:"Average Precision"},{description:"Mean Average Precision (mAP) is the overall average of the AP values",id:"Mean Average Precision"},{description:"Intersection over Union (IoU) is the overlap of segmentation masks. Mean IoU is the average of the IoU of all semantic classes",id:"Mean Intersection over Union"},{description:"APα is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",id:"APα"}],models:[{description:"Solid panoptic segmentation model trained on COCO.",id:"tue-mps/coco_panoptic_eomt_large_640"},{description:"Background removal model.",id:"briaai/RMBG-1.4"},{description:"A multipurpose image segmentation model for high resolution images.",id:"ZhengPeng7/BiRefNet"},{description:"Powerful human-centric image segmentation model.",id:"facebook/sapiens-seg-1b"},{description:"Panoptic segmentation model trained on the COCO (common objects) dataset.",id:"facebook/mask2former-swin-large-coco-panoptic"}],spaces:[{description:"A semantic segmentation application that can predict unseen instances out of the box.",id:"facebook/ov-seg"},{description:"One of the strongest segmentation applications.",id:"jbrinkma/segment-anything"},{description:"A human-centric segmentation model.",id:"facebook/sapiens-pose"},{description:"An instance segmentation application to predict neuronal cell types from microscopy images.",id:"rashmi/sartorius-cell-instance-segmentation"},{description:"An application that segments videos.",id:"ArtGAN/Segment-Anything-Video"},{description:"An panoptic segmentation application built for outdoor environments.",id:"segments/panoptic-segment-anything"}],summary:"Image Segmentation divides an image into segments where each pixel in the image is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.",widgetModels:["nvidia/segformer-b0-finetuned-ade-512-512"],youtubeId:"dKE8SIt9C-w"},Sk={datasets:[{description:"A benchmark dataset for reference image controlled video generation.",id:"ali-vilab/VACE-Benchmark"},{description:"A dataset of video generation style preferences.",id:"Rapidata/sora-video-generation-style-likert-scoring"},{description:"A dataset with videos and captions throughout the videos.",id:"BestWishYsh/ChronoMagic"}],demo:{inputs:[{filename:"image-to-video-input.jpg",type:"img"},{label:"Optional Text Prompt",content:"This penguin is dancing",type:"text"}
71],outputs:[{filename:"image-to-video-output.gif",type:"img"}]},metrics:[{description:"Fréchet Video Distance (FVD) measures the perceptual similarity between the distributions of generated videos and a set of real videos, assessing overall visual quality and temporal coherence of the video generated from an input image.",id:"fvd"},{description:"CLIP Score measures the semantic similarity between a textual prompt (if provided alongside the input image) and the generated video frames. It evaluates how well the video's generated content and motion align with the textual description, conditioned on the initial image.",id:"clip_score"},{description:"First Frame Fidelity, often measured using LPIPS (Learned Perceptual Image Patch Similarity), PSNR, or SSIM, quantifies how closely the first frame of the generated video matches the input conditioning image.",id:"lpips"},{description:"Identity Preservation Score measures the consistency of identity (e.g., a person's face or a specific object's characteristics) between the input image and throughout the generated video frames, often calculated using features from specialized models like face recognition (e.g., ArcFace) or re-identification models.",id:"identity_preservation"},{description:"Motion Score evaluates the quality, realism, and temporal consistency of motion in the video generated from a static image. This can be based on optical flow analysis (e.g., smoothness, magnitude), consistency of object trajectories, or specific motion plausibility assessments.",id:"motion_score"}],models:[{description:"LTX-Video, a 13B parameter model for high quality video generation",id:"Lightricks/LTX-Video-0.9.7-dev"},{description:"A 14B parameter model for reference image controlled video generation",id:"Wan-AI/Wan2.1-VACE-14B"},{description:"An image-to-video generation model using FramePack F1 methodology with Hunyuan-DiT architecture",id:"lllyasviel/FramePack_F1_I2V_HY_20250503"},{description:"A distilled version of the LTX-Video-0.9.7-dev model for faster inference",id:"Lightricks/LTX-Video-0.9.7-distilled"},{description:"An image-to-video generation model by Skywork AI, 14B parameters, producing 720p videos.",id:"Skywork/SkyReels-V2-I2V-14B-720P"},{description:"Image-to-video variant of Tencent's HunyuanVideo.",id:"tencent/HunyuanVideo-I2V"},{description:"A 14B parameter model for 720p image-to-video generation by Wan-AI.",id:"Wan-AI/Wan2.1-I2V-14B-720P"},{description:"A Diffusers version of the Wan2.1-I2V-14B-720P model for 720p image-to-video generation.",id:"Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"}],spaces:[{description:"An application to generate videos fast.",id:"Lightricks/ltx-video-distilled"},{description:"Generate videos with the FramePack-F1",id:"linoyts/FramePack-F1"},{description:"Generate videos with the FramePack",id:"lisonallen/framepack-i2v"},{description:"Wan2.1 with CausVid LoRA",id:"multimodalart/wan2-1-fast"},{description:"A demo for Stable Video Diffusion",id:"multimodalart/stable-video-diffusion"}],summary:"Image-to-video models take a still image as input and generate a video. These models can be guided by text prompts to influence the content and style of the output video.",widgetModels:[],youtubeId:void 0},Ak={datasets:[{description:"Widely used benchmark dataset for multiple Vision tasks.",id:"merve/coco2017"},{description:"Medical Imaging dataset of the Human Brain for segmentation and mask generating tasks",id:"rocky93/BraTS_segmentation"}],demo:{inputs:[{filename:"mask-generation-input.png",type:"img"}],outputs:[{filename:"mask-generation-output.png",type:"img"}]},metrics:[{description:"IoU is used to measure the overlap between predicted mask and the ground truth mask.",id:"Intersection over Union (IoU)"}],models:[{description:"Small yet powerful mask generation model.",id:"Zigeng/SlimSAM-uniform-50"},{description:"Very strong mask generation model.",id:"facebook/sam2-hiera-large"}],spaces:[{description:"An application that combines a mask generation model with a zero-shot object detection model for text-guided image segmentation.",id:"merve/OWLSAM2"},{description:"An application that compares the performance of a large and a small mask generation model.",id:"merve/slimsam"},{description:"An application based on an improved mask generation model.",id:"SkalskiP/segment-anything-model-2"},{description:"An application to remove objects from videos using mask generation models.",id:"SkalskiP/SAM_and_ProPainter"}],summary:"Mask generation is the task of generating masks that identify a specific object or region of interest in a given image. Masks are often used in segmentation tasks, where they provide a precise way to isolate the object of interest for further processing or analysis.",widgetModels:[],youtubeId:""},kk={datasets:[{description:"Widely used benchmark dataset for multiple vision tasks.",id:"merve/coco2017"},{description:"Multi-task computer vision benchmark.",id:"merve/pascal-voc"}],demo:{inputs:[{filename:"object-detection-input.jpg",type:"img"}],outputs:[{filename:"object-detection-output.jpg",type:"img"}]},metrics:[{description:"The Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It is calculated for each class separately",id:"Average Precision"},{description:"The Mean Average Precision (mAP) metric is the overall average of the AP values",id:"Mean Average Precision"},{description:"The APα metric is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",id:"APα"}],models:[{description:"Solid object detection model pre-trained on the COCO 2017 dataset.",id:"facebook/detr-resnet-50"},{description:"Accurate object detection model.",id:"IDEA-Research/dab-detr-resnet-50"},{description:"Fast and accurate object detection model.",id:"PekingU/rtdetr_v2_r50vd"}
71,{description:"Object detection model for low-lying objects.",id:"StephanST/WALDO30"}],spaces:[{description:"Real-time object detection demo.",id:"Roboflow/RF-DETR"},{description:"An application that contains various object detection models to try from.",id:"Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS"},{description:"A cutting-edge object detection application.",id:"sunsmarterjieleaf/yolov12"},{description:"An object tracking, segmentation and inpainting application.",id:"VIPLab/Track-Anything"},{description:"Very fast object tracking application based on object detection.",id:"merve/RT-DETR-tracking-coco"}],summary:"Object Detection models allow users to identify objects of certain defined classes. Object detection models receive an image as input and output the images with bounding boxes and labels on detected objects.",widgetModels:["facebook/detr-resnet-50"],youtubeId:"WdAeKSOpxhw"},Tk={datasets:[{description:"NYU Depth V2 Dataset: Video dataset containing both RGB and depth sensor data.",id:"sayakpaul/nyu_depth_v2"},{description:"Monocular depth estimation benchmark based without noise and errors.",id:"depth-anything/DA-2K"}],demo:{inputs:[{filename:"depth-estimation-input.jpg",type:"img"}],outputs:[{filename:"depth-estimation-output.png",type:"img"}]},metrics:[],models:[{description:"Cutting-edge depth estimation model.",id:"depth-anything/Depth-Anything-V2-Large"},{description:"A strong monocular depth estimation model.",id:"jingheya/lotus-depth-g-v1-0"},{description:"A depth estimation model that predicts depth in videos.",id:"tencent/DepthCrafter"},{description:"A robust depth estimation model.",id:"apple/DepthPro-hf"}],spaces:[{description:"An application that predicts the depth of an image and then reconstruct the 3D model as voxels.",id:"radames/dpt-depth-estimation-3d-voxels"},{description:"An application for bleeding-edge depth estimation.",id:"akhaliq/depth-pro"},{description:"An application on cutting-edge depth estimation in videos.",id:"tencent/DepthCrafter"},{description:"A human-centric depth estimation application.",id:"facebook/sapiens-depth"}],summary:"Depth estimation is the task of predicting depth of the objects present in an image.",widgetModels:[""],youtubeId:""},Uc={datasets:[],demo:{inputs:[],outputs:[]},isPlaceholder:!0,metrics:[],models:[],spaces:[],summary:"",widgetModels:[],youtubeId:void 0,canonicalId:void 0},Ek={datasets:[{description:"A curation of widely used datasets for Data Driven Deep Reinforcement Learning (D4RL)",id:"edbeeching/decision_transformer_gym_replay"}],demo:{inputs:[{label:"State",content:"Red traffic light, pedestrians are about to pass.",type:"text"}],outputs:[{label:"Action",content:"Stop the car.",type:"text"},{label:"Next State",content:"Yellow light, pedestrians have crossed.",type:"text"}]},metrics:[{description:"Accumulated reward across all time steps discounted by a factor that ranges between 0 and 1 and determines how much the agent optimizes for future relative to immediate rewards. Measures how good is the policy ultimately found by a given algorithm considering uncertainty over the future.",id:"Discounted Total Reward"},{description:"Average return obtained after running the policy for a certain number of evaluation episodes. As opposed to total reward, mean reward considers how much reward a given algorithm receives while learning.",id:"Mean Reward"},{description:"Measures how good a given algorithm is after a predefined time. Some algorithms may be guaranteed to converge to optimal behavior across many time steps. However, an agent that reaches an acceptable level of optimality after a given time horizon may be preferable to one that ultimately reaches optimality but takes a long time.",id:"Level of Performance After Some Time"}],models:[{description:"A Reinforcement Learning model trained on expert data from the Gym Hopper environment",id:"edbeeching/decision-transformer-gym-hopper-expert"},{description:"A PPO agent playing seals/CartPole-v0 using the stable-baselines3 library and the RL Zoo.",id:"HumanCompatibleAI/ppo-seals-CartPole-v0"}],spaces:[{description:"An application for a cute puppy agent learning to catch a stick.",id:"ThomasSimonini/Huggy"},{description:"An application to play Snowball Fight with a reinforcement learning agent.",id:"ThomasSimonini/SnowballFight"}],summary:"Reinforcement learning is the computational approach of learning from action by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback",widgetModels:[],youtubeId:"q0BiUn5LiBc"},Ck={datasets:[{description:"A famous question answering dataset based on English articles from Wikipedia.",id:"squad_v2"},{description:"A dataset of aggregated anonymized actual queries issued to the Google search engine.",id:"natural_questions"}],demo:{inputs:[{label:"Question",content:"Which name is also used to describe the Amazon rainforest in English?",type:"text"},{label:"Context",content:"The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle",type:"text"}],outputs:[{label:"Answer",content:"Amazonia",type:"text"}]},metrics:[{description:"Exact Match is a metric based on the strict character match of the predicted answer and the right answer. For answers predicted correctly, the Exact Match will be 1. Even if only one character is different, Exact Match will be 0",id:"exact-match"},{description:" The F1-Score metric is useful if we value both false positives and false negatives equally. The F1-Score is calculated on each word in the predicted sequence against the correct answer",id:"f1"}],models:[{description:"A robust baseline model for most question answering domains.",id:"deepset/roberta-base-squad2"},{description:"Small yet robust model that can answer questions.",id:"distilbert/distilbert-base-cased-distilled-squad"},{description:"A special model that can answer questions from tables.",id:"google/tapas-base-finetuned-wtq"}],spaces:[{description:"An application that can answer a long question from Wikipedia.",id:"deepset/wikipedia-assistant"}],summary:"Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document. Some question answering models can generate answers without context!",widgetModels:["deepset/roberta-base-squad2"],youtubeId:"ajPx5LwJD-I"},Rk={datasets:[{description:"Bing queries with relevant passages from various web sources.",id:"microsoft/ms_marco"}],demo:{inputs:[{label:"Source sentence",content:"Machine learning is so easy.",type:"text"},{label:"Sentences to compare to",content:"Deep learning is so straightforward.",type:"text"},{label:"",content:"This is so difficult, like rocket science.",type:"text"},{label:"",content:"I can't believe how much I struggled with this.",type:"text"}],outputs:[{type:"chart",data:[{label:"Deep learning is so straightforward.",score:.623},{label:"This is so difficult, like rocket science.",score:.413},{label:"I can't believe how much I struggled with this.",score:.256}]}]},metrics:[{description:"Reciprocal Rank is a measure used to rank the relevancy of documents given a set of documents. Reciprocal Rank is the reciprocal of the rank of the document retrieved, meaning, if the rank is 3, the Reciprocal Rank is 0.33. If the rank is 1, the Reciprocal Rank is 1",id:"Mean Reciprocal Rank"},{description:"The similarity of the embeddings is evaluated mainly on cosine similarity. It is calculated as the cosine of the angle between two vectors. It is particularly useful when your texts are not the same length",id:"Cosine Similarity"}],models:[{description:"This model works well for sentences and paragraphs and can be used for clustering/grouping and semantic searches.",id:"sentence-transformers/all-mpnet-base-v2"},{description:"A multilingual robust sentence similarity model.",id:"BAAI/bge-m3"},{description:"A robust sentence similarity model.",id:"HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5"}],spaces:[{description:"An application that leverages sentence similarity to answer questions from YouTube videos.",id:"Gradio-Blocks/Ask_Questions_To_YouTube_Videos"},{description:"An application that retrieves relevant PubMed abstracts for a given online article which can be used as further references.",id:"Gradio-Blocks/pubmed-abstract-retriever"},{description:"An application that leverages sentence similarity to summarize text.",id:"nickmuchi/article-text-summarizer"},{description:"A guide that explains how Sentence Transformers can be used for semantic search.",id:"sentence-transformers/Sentence_Transformers_for_semantic_search"}],summary:"Sentence Similarity is the task of determining how similar two texts are. Sentence similarity models convert input texts into vectors (embeddings) that capture semantic information and calculate how close (similar) they are between them. This task is particularly useful for information retrieval and clustering/grouping.",widgetModels:["BAAI/bge-small-en-v1.5"],youtubeId:"VCZq5AkbNEU"},Dk={canonicalId:"text-generation",datasets:[{description:"News articles in five different languages along with their summaries. Widely used for benchmarking multilingual summarization models.",id:"mlsum"},{description:"English conversations and their summaries. Useful for benchmarking conversational agents.",id:"samsum"}],demo:{inputs:[{label:"Input",content:"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. It was the first structure to reach a height of 300 metres. Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.",type:"text"}],outputs:[{label:"Output",content:"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building. It was the first structure to reach a height of 300 metres.",type:"text"}]},metrics:[{description:"The generated sequence is compared against its summary, and the overlap of tokens are counted. ROUGE-N refers to overlap of N subsequent tokens, ROUGE-1 refers to overlap of single tokens and ROUGE-2 is the overlap of two subsequent tokens.",id:"rouge"}],models:[{description:"A strong summarization model trained on English news articles. Excels at generating factual summaries.",id:"facebook/bart-large-cnn"},{description:"A summarization model trained on medical articles.",id:"Falconsai/medical_summarization"}],spaces:[{description:"An application that can summarize long paragraphs.",id:"pszemraj/summarize-long-text"},{description:"A much needed summarization application for terms and conditions.",id:"ml6team/distilbart-tos-summarizer-tosdr"},{description:"An application that summarizes long documents.",id:"pszemraj/document-summarization"},{description:"An application that can detect errors in abstractive summarization.",id:"ml6team/post-processing-summarization"}],summary:"Summarization is the task of producing a shorter version of a document while preserving its important information. Some models can extract text from the original input, while other models can generate entirely new text.",widgetModels:["facebook/bart-large-cnn"],youtubeId:"yHnr5Dk2zCI"},Ok={datasets:[{description:"The WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables.",id:"wikitablequestions"},{description:"WikiSQL is a dataset of 80654 hand-annotated examples of questions and
71SQL queries distributed across 24241 tables from Wikipedia.",id:"wikisql"}],demo:{inputs:[{table:[["Rank","Name","No.of reigns","Combined days"],["1","lou Thesz","3","3749"],["2","Ric Flair","8","3103"],["3","Harley Race","7","1799"]],type:"tabular"},{label:"Question",content:"What is the number of reigns for Harley Race?",type:"text"}],outputs:[{label:"Result",content:"7",type:"text"}]},metrics:[{description:"Checks whether the predicted answer(s) is the same as the ground-truth answer(s).",id:"Denotation Accuracy"}],models:[{description:"A table question answering model that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table.",id:"microsoft/tapex-base"},{description:"A robust table question answering model.",id:"google/tapas-base-finetuned-wtq"}],spaces:[{description:"An application that answers questions based on table CSV files.",id:"katanaml/table-query"}],summary:"Table Question Answering (Table QA) is the answering a question about an information on a given table.",widgetModels:["google/tapas-base-finetuned-wtq"]},Mk={datasets:[{description:"A comprehensive curation of datasets covering all benchmarks.",id:"inria-soda/tabular-benchmark"}],demo:{inputs:[{table:[["Glucose","Blood Pressure ","Skin Thickness","Insulin","BMI"],["148","72","35","0","33.6"],["150","50","30","0","35.1"],["141","60","29","1","39.2"]],type:"tabular"}],outputs:[{table:[["Diabetes"],["1"],["1"],["0"]],type:"tabular"}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"",id:"f1"}],models:[{description:"Breast cancer prediction model based on decision trees.",id:"scikit-learn/cancer-prediction-trees"}],spaces:[{description:"An application that can predict defective products on a production line.",id:"scikit-learn/tabular-playground"},{description:"An application that compares various tabular classification techniques on different datasets.",id:"scikit-learn/classification"}],summary:"Tabular classification is the task of classifying a target category (a group) based on set of attributes.",widgetModels:["scikit-learn/tabular-playground"],youtubeId:""},Uk={datasets:[{description:"A comprehensive curation of datasets covering all benchmarks.",id:"inria-soda/tabular-benchmark"}],demo:{inputs:[{table:[["Car Name","Horsepower","Weight"],["ford torino","140","3,449"],["amc hornet","97","2,774"],["toyota corolla","65","1,773"]],type:"tabular"}],outputs:[{table:[["MPG (miles per gallon)"],["17"],["18"],["31"]],type:"tabular"}]},metrics:[{description:"",id:"mse"},{description:"Coefficient of determination (or R-squared) is a measure of how well the model fits the data. Higher R-squared is considered a better fit.",id:"r-squared"}],models:[{description:"Fish weight prediction based on length measurements and species.",id:"scikit-learn/Fish-Weight"}],spaces:[{description:"An application that can predict weight of a fish based on set of attributes.",id:"scikit-learn/fish-weight-prediction"}],summary:"Tabular regression is the task of predicting a numerical value given a set of attributes.",widgetModels:["scikit-learn/Fish-Weight"],youtubeId:""},Nk={datasets:[{description:"RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.",id:"red_caps"},{description:"Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions.",id:"conceptual_captions"},{description:"12M image-caption pairs.",id:"Spawning/PD12M"}],demo:{inputs:[{label:"Input",content:"A city above clouds, pastel colors, Victorian style",type:"text"}],outputs:[{filename:"image.jpeg",type:"img"}]},metrics:[{description:"The Inception Score (IS) measure assesses diversity and meaningfulness. It uses a generated image sample to predict its label. A higher score signifies more diverse and meaningful images.",id:"IS"},{description:"The Fréchet Inception Distance (FID) calculates the distance between distributions between synthetic and real samples. A lower FID score indicates better similarity between the distributions of real and generated images.",id:"FID"},{description:"R-precision assesses how the generated image aligns with the provided text description. It uses the generated images as queries to retrieve relevant text descriptions. The top 'r' relevant descriptions are selected and used to calculate R-precision as r/R, where 'R' is the number of ground truth descriptions associated with the generated images. A higher R-precision value indicates a better model.",id:"R-Precision"}],models:[{description:"One of the most powerful image generation models that can generate realistic outputs.",id:"black-forest-labs/FLUX.1-Krea-dev"}
71,{description:"A powerful image generation model.",id:"Qwen/Qwen-Image"},{description:"Powerful and fast image generation model.",id:"ByteDance/SDXL-Lightning"},{description:"A powerful text-to-image model.",id:"ByteDance/Hyper-SD"}],spaces:[{description:"A powerful text-to-image application.",id:"stabilityai/stable-diffusion-3-medium"},{description:"A text-to-image application to generate comics.",id:"jbilcke-hf/ai-comic-factory"},{description:"An application to match multiple custom image generation models.",id:"multimodalart/flux-lora-lab"},{description:"A powerful yet very fast image generation application.",id:"latent-consistency/lcm-lora-for-sdxl"},{description:"A gallery to explore various text-to-image models.",id:"multimodalart/LoraTheExplorer"},{description:"An application for `text-to-image`, `image-to-image` and image inpainting.",id:"ArtGAN/Stable-Diffusion-ControlNet-WebUI"},{description:"An application to generate realistic images given photos of a person and a prompt.",id:"InstantX/InstantID"}],summary:"Text-to-image is the task of generating images from input text. These pipelines can also be used to modify and edit images based on text prompts.",widgetModels:["black-forest-labs/FLUX.1-dev"],youtubeId:""},Ik={canonicalId:"text-to-audio",datasets:[{description:"10K hours of multi-speaker English dataset.",id:"parler-tts/mls_eng_10k"},{description:"Multi-speaker English dataset.",id:"mythicinfinity/libritts_r"},{description:"Multi-lingual dataset.",id:"facebook/multilingual_librispeech"}],demo:{inputs:[{label:"Input",content:"I love audio models on the Hub!",type:"text"}],outputs:[{filename:"audio.wav",type:"audio"}]},metrics:[{description:"The Mel Cepstral Distortion (MCD) metric is used to calculate the quality of generated speech.",id:"mel cepstral distortion"}],models:[{description:"Small yet powerful TTS model.",id:"KittenML/kitten-tts-nano-0.1"},{description:"Bleeding edge TTS model.",id:"ResembleAI/chatterbox"},{description:"A massively multi-lingual TTS model.",id:"fishaudio/fish-speech-1.5"},{description:"A text-to-dialogue model.",id:"nari-labs/Dia-1.6B-0626"}],spaces:[{description:"An application for generate high quality speech in different languages.",id:"hexgrad/Kokoro-TTS"},{description:"A multilingual text-to-speech application.",id:"fishaudio/fish-speech-1"},{description:"Performant TTS application.",id:"ResembleAI/Chatterbox"},{description:"An application to compare different TTS models.",id:"TTS-AGI/TTS-Arena-V2"},{description:"An application that generates podcast episodes.",id:"ngxson/kokoro-podcast-generator"}],summary:"Text-to-Speech (TTS) is the task of generating natural sounding speech given text input. TTS models can be extended to have a single model that generates speech for multiple speakers and multiple languages.",widgetModels:["suno/bark"],youtubeId:"NW62DpzJ274"},jk={datasets:[{description:"A widely used dataset useful to benchmark named entity recognition models.",id:"eriktks/conll2003"},{description:"A multilingual dataset of Wikipedia articles annotated for named entity recognition in over 150 different languages.",id:"unimelb-nlp/wikiann"}],demo:{inputs:[{label:"Input",content:"My name is Omar and I live in Zürich.",type:"text"}],outputs:[{text:"My name is Omar and I live in Zürich.",tokens:[{type:"PERSON",start:11,end:15},{type:"GPE",start:30,end:36}],type:"text-with-tokens"}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"",id:"f1"}],models:[{description:"A robust performance model to identify people, locations, organizations and names of miscellaneous entities.",id:"dslim/bert-base-NER"},{description:"A strong model to identify people, locations, organizations and names in multiple languages.",id:"FacebookAI/xlm-roberta-large-finetuned-conll03-english"},{description:"A token classification model specialized on medical entity recognition.",id:"blaze999/Medical-NER"},{description:"Flair models are typically the state of the art in named entity recognition tasks.",id:"flair/ner-english"}],spaces:[{description:"An application that can recognizes entities, extracts noun chunks and recognizes various linguistic features of each token.",id:"spacy/gradio_pipeline_visualizer"}],summary:"Token classification is a natural language understanding task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models could be trained to identify specific entities in a text, such as dates, individuals and places; and PoS tagging would identify, for example, which words in a text are verbs, nouns, and punctuation marks.",widgetModels:["FacebookAI/xlm-roberta-large-finetuned-conll03-english"],youtubeId:"wVHdVlPScxA"},Lk={canonicalId:"text-generation",datasets:[{description:"A dataset of copyright-free books translated into 16 different languages.",id:"Helsinki-NLP/opus_books"},{description:"An example of translation between programming languages. This dataset consists of functions in Java and C#.",id:"google/code_x_glue_cc_code_to_code_trans"}],demo:{inputs:[{label:"Input",content:"My name is Omar and I live in Züric
71h.",type:"text"}],outputs:[{label:"Output",content:"Mein Name ist Omar und ich wohne in Zürich.",type:"text"}]},metrics:[{description:"BLEU score is calculated by counting the number of shared single or subsequent tokens between the generated sequence and the reference. Subsequent n tokens are called ân-gramsâ. Unigram refers to a single token while bi-gram refers to token pairs and n-grams refer to n subsequent tokens. The score ranges from 0 to 1, where 1 means the translation perfectly matched and 0 did not match at all",id:"bleu"},{description:"",id:"sacrebleu"}],models:[{description:"Very powerful model that can translate many languages between each other, especially low-resource languages.",id:"facebook/nllb-200-1.3B"},{description:"A general-purpose Transformer that can be used to translate from English to German, French, or Romanian.",id:"google-t5/t5-base"}],spaces:[{description:"An application that can translate between 100 languages.",id:"Iker/Translate-100-languages"},{description:"An application that can translate between many languages.",id:"Geonmo/nllb-translation-demo"}],summary:"Translation is the task of converting text from one language to another.",widgetModels:["facebook/mbart-large-50-many-to-many-mmt"],youtubeId:"1JvfrvZgi6c"},zk={datasets:[{description:"A widely used dataset used to benchmark multiple variants of text classification.",id:"nyu-mll/glue"},{description:"A text classification dataset used to benchmark natural language inference models",id:"stanfordnlp/snli"}],demo:{inputs:[{label:"Input",content:"I love Hugging Face!",type:"text"}],outputs:[{type:"chart",data:[{label:"POSITIVE",score:.9},{label:"NEUTRAL",score:.1},{label:"NEGATIVE",score:0}]}]},metrics:[{description:"",id:"accuracy"},{description:"",id:"recall"},{description:"",id:"precision"},{description:"The F1 metric is the harmonic mean of the precision and recall. It can be calculated as: F1 = 2 * (precision * recall) / (precision + recall)",id:"f1"}],models:[{description:"A robust model trained for sentiment analysis.",id:"distilbert/distilbert-base-uncased-finetuned-sst-2-english"},{description:"A sentiment analysis model specialized in financial sentiment.",id:"ProsusAI/finbert"},{description:"A sentiment analysis model specialized in analyzing tweets.",id:"cardiffnlp/twitter-roberta-base-sentiment-latest"},{description:"A model that can classify languages.",id:"papluca/xlm-roberta-base-language-detection"},{description:"A model that can classify text generation attacks.",id:"meta-llama/Prompt-Guard-86M"}],spaces:[{description:"An application that can classify financial sentiment.",id:"IoannisTr/Tech_Stocks_Trading_Assistant"},{description:"A dashboard that contains various text classification tasks.",id:"miesnerjacob/Multi-task-NLP"},{description:"An application that analyzes user reviews in healthcare.",id:"spacy/healthsea-demo"}],summary:"Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.",widgetModels:["distilbert/distilbert-base-uncased-finetuned-sst-2-english"],youtubeId:"leNG9fN9FQU"},qk={datasets:[{description:"Multilingual dataset used to evaluate text generation models.",id:"CohereForAI/Global-MMLU"},{description:"High quality multilingual data used to train text-generation models.",id:"HuggingFaceFW/fineweb-2"},{description:"Truly open-source, curated and cleaned dialogue dataset.",id:"HuggingFaceH4/ultrachat_200k"},{description:"A reasoning dataset.",id:"open-r1/OpenThoughts-114k-math"},{description:"A multilingual instruction dataset with preference ratings on responses.",id:"allenai/tulu-3-sft-mixture"},{description:"A large synthetic dataset for alignment of text generation models.",id:"HuggingFaceTB/smoltalk"},{description:"A dataset made for training text generation models solving math questions.",id:"HuggingFaceTB/finemath"}],demo:{inputs:[{label:"Input",content:"Once upon a time,",type:"text"}],outputs:[{label:"Output",content:"Once upon a time, we knew that our ancestors were on the verge of extinction. The great explorers and poets of the Old World, from Alexander the Great to Chaucer, are dead and gone. A good many of our ancient explorers and poets have",type:"text"}]},metrics:[{description:"Cross Entropy is a metric that calculates the difference between two probability distributions. Each probability distribution is the distribution of predicted words",id:"Cross Entropy"},{description:"The Perplexity metric is the exponential of the cross-entropy loss. It evaluates the probabilities assigned to the next word by the model. Lower perplexity indicates better performance",id:"Perplexity"}],models:[{description:"A text-generation model trained to follow instructions.",id:"google/gemma-2-2b-it"},{description:"Powerful text generation model for coding.",id:"Qwen
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",widgetModels:["Wan-AI/Wan2.2-TI2V-5B"],youtubeId:void 0},Pk={datasets:[{description:"The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class.",id:"cifar100"},{description:"Multiple images of celebrities, used for facial expression translation.",id:"CelebA"}],demo:{inputs:[{label:"Seed",content:"42",type:"text"},{label:"Number of images to generate:",content:"4",type:"text"}],outputs:[{filename:"unconditional-image-generation-output.jpeg",type:"img"}]},metrics:[{description:"The inception score (IS) evaluates the quality of generated images. 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These models are also called video-language models.",widgetModels:[""],youtubeId:""},Wk={"audio-classification":["speechbrain","transformers","transformers.js"],"audio-to-audio":["asteroid","fairseq","speechbrain"],"automatic-speech-recognition":["espnet","nemo","speechbrain","transformers","transformers.js"],"audio-text-to-text":[],"depth-estimation":["transformers","transformers.js"],"document-question-answering":["transformers","transformers.js"],"feature-extraction":["sentence-transformers","transformers","transformers.js"],"fill-mask":["transformers","transformers.js"],"graph-ml":["transformers"],"image-classification":["keras","timm","transformers","transformers.js"],"image-feature-extraction":["timm","transformers"],"image-segmentation":["transformers","transformers.js"],"image-text-to-text":["transformers"],"image-to-image":["diffusers","transformers","transformers.js"],"image-to-text":["transformers","transformers.js"],"image-to-vi
71deo":["diffusers"],"keypoint-detection":["transformers"],"video-classification":["transformers"],"mask-generation":["transformers"],"multiple-choice":["transformers"],"object-detection":["transformers","transformers.js","ultralytics"],other:[],"question-answering":["adapter-transformers","allennlp","transformers","transformers.js"],robotics:[],"reinforcement-learning":["transformers","stable-baselines3","ml-agents","sample-factory"],"sentence-similarity":["sentence-transformers","spacy","transformers.js"],summarization:["transformers","transformers.js"],"table-question-answering":["transformers"],"table-to-text":["transformers"],"tabular-classification":["sklearn"],"tabular-regression":["sklearn"],"tabular-to-text":["transformers"],"text-classification":["adapter-transformers","setfit","spacy","transformers","transformers.js"],"text-generation":["transformers","transformers.js"],"text-ranking":["sentence-transformers","transformers"],"text-retrieval":[],"text-to-image":["diffusers"],"text-to-speech":["espnet","tensorflowtts","transformers","transformers.js"],"text-to-audio":["transformers","transformers.js"],"text-to-video":["diffusers"],"time-series-forecasting":[],"token-classification":["adapter-transformers","flair","spacy","span-marker","stanza","transformers","transformers.js"],translation:["transformers","transformers.js"],"unconditional-image-generation":["diffusers"],"video-text-to-text":["transformers"],"visual-question-answering":["transformers","transformers.js"],"voice-activity-detection":[],"zero-shot-classification":["transformers","transformers.js"],"zero-shot-image-classification":["transformers","transformers.js"],"zero-shot-object-detection":["transformers","transformers.js"],"text-to-3d":["diffusers"],"image-to-3d":["diffusers"],"any-to-any":["transformers"],"visual-document-retrieval":["transformers"],"video-to-video":["diffusers"]};function be(t,a=Uc){return{...a,id:t,label:lf[t].name,libraries:Wk[t]}}be("any-to-any",ck),be("audio-classification",fk),be("audio-to-audio",pk),be("audio-text-to-text",Uc),be("automatic-speech-recognition",dk),be("depth-estimation",Tk),be("document-question-answering",mk),be("visual-document-retrieval",$k),be("feature-extraction",hk),be("fill-mask",gk),be("image-classification",yk),be("image-feature-extraction",bk),be("image-segmentation",xk),be("image-to-image",vk),be("image-text-to-text",_k),be("image-to-text",wk),be("image-to-video",Sk),be("keypoint-detection",Jk),be("mask-generation",Ak),be("object-detection",kk),be("video-classification",Vk),be("question-answering",Ck),be("reinfor
71cement-learning",Ek),be("sentence-similarity",Rk),be("summarization",Dk),be("table-question-answering",Ok),be("tabular-classification",Mk),be("tabular-regression",Uk),be("text-classification",zk),be("text-generation",qk),be("text-ranking",Bk),be("text-to-image",Nk),be("text-to-speech",Ik),be("text-to-video",Hk),be("token-classification",jk),be("translation",Lk),be("unconditional-image-generation",Pk),be("video-text-to-text",Gk),be("video-to-video",Uc),be("visual-question-answering",Fk),be("zero-shot-classification",Xk),be("zero-shot-image-classification",Yk),be("zero-shot-object-detection",Qk),be("text-to-3d",Zk),be("image-to-3d",Kk);const eT=()=>'"Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"',tT=()=>'"ÐÐµÐ½Ñ Ð·Ð¾Ð²ÑÑ ÐолÑÑганг и Ñ Ð¶Ð¸Ð²Ñ Ð² ÐеÑлине"',nT=()=>'"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."',aT=()=>`{ 72 "query": "How many stars does the transformers repository have?", 73 "table": { 74 "Repository": ["Transformers", "Datasets", "Tokenizers"], 75 "Stars": ["36542", "4512", "3934"], 76 "Contributors": ["651", "77", "34"], 77 "Programming language": [ 78 "Python", 79 "Python", 80 "Rust, Python and NodeJS" 81 ] 82 } 83}`,iT=()=>`{ 84 "image": "cat.png", 85 "question": "What is in this image?" 86 }`,rT=()=>`{ 87 "question": "What is my name?", 88 "context": "My name is Clara and I live in Berkeley." 89}`,oT=()=>'"I like you. I love you"',lT=()=>'"My name is Sarah Jessica Parker but you can call me Jessica"',ig=t=>t.tags.includes("conversational")?t.pipeline_tag==="text-generation"?[{role:"user",content:"What is the capital of France?"}]:[{role:"user",content:[{type:"text",text:"Describe this image in one sentence."},{type:"image_url",image_url:{url:"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"}}]}]:'"Can you please let us know more details about your "',sT=t=>`"The answer to the universe is ${t.mask_token}."`,uT=()=>`{ 90 "source_sentence": "That is a happy person", 91 "sentences": [ 92 "That is a happy dog", 93 "That is a very happy person", 94 "Today is a sunny day" 95 ] 96}`,cT=()=>'"Today is a sunny day and I will get some ice cream."',fT=()=>'"cats.jpg"',pT=()=>'"cats.jpg"',dT=()=>`{ 97 "image": "cat.png", 98 "prompt": "Turn the cat into a tiger." 99}`,mT=()=>`{ 100 "image": "cat.png", 101 "prompt": "The cat starts to dance" 102}`,hT=()=>'"cats.jpg"',gT=()=>'"cats.jpg"',yT=()=>'"sample1.flac"',bT=()=>'"sample1.flac"',vT=()=>'"Astronaut riding a horse"',wT=()=>'"A young man walking on the street"',_T=()=>'"The answer to the universe is 42"',xT=()=>'"liquid drum and bass, atmospheric synths, airy sounds"',ST=()=>'"sample1.flac"',rg=()=>`'{"Height":[11.52,12.48],"Length1":[23.2,24.0],"Length2":[25.4,26.3],"Species": ["Bream","Bream"]}'`,AT=()=>'"cats.jpg"',kT={"audio-to-audio":yT,"audio-classification":bT,"automatic-speech-recognition":ST,"document-question-answering":iT,"feature-extraction":cT,"fill-mask":sT,"image-classification":fT,"image-to-text":pT,"image-to-image":dT,"image-to-video":mT,"image-segmentation":hT,"object-detection":gT,"question-answering":rT,"sentence-similarity":uT,summarization:nT,"table-question-answering":aT,"tabular-regression":rg,"tabular-classification":rg,"text-classification":oT,"text-generation":ig,"image-text-to-text":ig,"text-to-image":vT,"text-to-vi
102deo":wT,"text-to-speech":_T,"text-to-audio":xT,"token-classification":lT,translation:tT,"zero-shot-classification":eT,"zero-shot-image-classification":AT};function TT(t,a=!1,r=!1){if(t.pipeline_tag){const l=kT[t.pipeline_tag];if(l){let s=l(t);if(typeof s=="string"&&(a&&(s=s.replace(/(?:(?:\r?\n|\r)\t*)|\t+/g," ")),r)){const c=/^"(.+)"$/s,p=s.match(c);s=p?p[1]:s}return s}}return"No input example has been defined for this model task."}function ET(t,a){let r=JSON.stringify(t,null," ");return a?.indent&&(r=r.replaceAll(` 103`,` 104${a.indent}`)),a?.attributeKeyQuotes||(r=r.replace(/"([^"]+)":/g,"$1:")),a?.customContentEscaper&&(r=a.customContentEscaper(r)),r}const ey="custom_code";function Nn(t){const a=t.split("/");return a.length===1?a[0]:a[1]}const CT=t=>JSON.stringify(t).slice(1,-1),RT=t=>[`from adapters import AutoAdapterModel 105 106model = AutoAdapterModel.from_pretrained("${t.config?.adapter_transformers?.model_name}") 107model.load_adapter("${t.id}", set_active=True)`],DT=t=>[`import allennlp_models 108from allennlp.predictors.predictor import Predictor 109 110predictor = Predictor.from_path("hf://${t.id}")`],OT=t=>[`import allennlp_models 111from allennlp.predictors.predictor import Predictor 112 113predictor = Predictor.from_path("hf://${t.id}") 114predictor_input = {"passage": "My name is Wolfgang and I live in Berlin", "question": "Where do I live?"} 115predictions = predictor.predict_json(predictor_input)`],MT=t=>t.tags.includes("question-answering")?OT(t):DT(t),UT=t=>[`from araclip import AraClip 116 117model = AraClip.from_pretrained("${t.id}")`],NT=t=>[`from asteroid.models import BaseModel 118 119model = BaseModel.from_pretrained("${t.id}")`],IT=t=>{const a=`# Watermark Generator 120from audioseal import AudioSeal 121 122model = AudioSeal.load_generator("${t.id}") 123# pass a tensor (tensor_wav) of shape (batch, channels, samples) and a sample rate 124wav, sr = tensor_wav, 16000 125 126watermark = model.get_watermark(wav, sr) 127watermarked_audio = wav + watermark`,r=`# Watermark Detector 128from audioseal import AudioSeal 129 130detector = AudioSeal.load_detector("${t.id}") 131 132result, message = detector.detect_watermark(watermarked_audio, sr)`;return[a,r]};function $i(t){return t.cardData?.base_model?.toString()??"fill-in-base-model"}function Qa(t){const a=t.widgetData?.[0]?.text??t.cardData?.instance_prompt;if(a)return CT(a)}const jT=t=>[`import requests 133from PIL import Image 134from ben2 import AutoModel 135 136url = "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg" 137image = Image.open(requests.get(url, stream=True).raw) 138 139model = AutoModel.from_pretrained("${t.id}") 140model.to("cuda").eval() 141foreground = model.inference(image) 142`],LT=t=>[`from bertopic import BERTopic 143 144model = BERTopic.load("${t.id}")`],zT=t=>[`from bm25s.hf import BM25HF 145 146retriever = BM25HF.load_from_hub("${t.id}")`],qT=()=>[`# pip install chatterbox-tts 147import torchaudio as ta 148from chatterbox.tts import ChatterboxTTS 149 150model = ChatterboxTTS.from_pretrained(device="cuda") 151 152text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill." 153wav = model.generate(text) 154ta.save("test-1.wav", wav, model.sr) 155 156# If you want to synthesize with a different voice, specify the audio prompt 157AUDIO_PROMPT_PATH="YOUR_FILE.wav" 158wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) 159ta.save("test-2.wav", wav, model.sr)`],BT=()=>["pip install git+https://github.com/SAP-samples/contexttab",`# Run a classification task 160from sklearn.datasets import load_breast_cancer 161from sklearn.metrics import accuracy_score 162from sklearn.model_selection import train_test_split 163 164from contexttab import ConTextTabClassifier 165 166# Load sample data 167X, y = load_breast_cancer(return_X_y=True) 168X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) 169 170# Initialize a classifier 171# You can omit checkpoint and checkpoint_revision to use the default model 172clf = ConTextTabClassifier(checkpoint="l2/base.pt", checkpoint_revision="v1.0.0", bagging=1, max_context_size=2048) 173 174clf.fit(X_train, y_train) 175 176# Predict probabilities 177prediction_probabilities = clf.predict_proba(X_test) 178# Predict labels 179predictions = clf.predict(X_test) 180print("Accuracy", accuracy_score(y_test, predictions))`,`# Run a regression task 181from sklearn.datasets import fetch_openml 182from sklearn.metrics import r2_score 183from sklearn.model_selection import train_test_split 184 185from contexttab import ConTextTabRegressor 186 187 188# Load sample data 189df = fetch_openml(data_id=531, as_frame=True) 190X = df.data 191y = df.target.astype(float) 192 193# Train-test split 194X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) 195 196# Initialize the regressor 197# You can omit checkpoint and checkpoint_revision to use the default model 198regressor = ConTextTabRegressor(checkpoint="l2/base.pt", checkpoint_revision="v1.0.0", bagging=1, max_context_size=2048) 199 200regressor.fit(X_train, y_train) 201 202# Predict on the test set 203predictions = regressor.predict(X_test) 204 205r2 = r2_score(y_test, predictions) 206print("R² Score:", r2)`],HT=()=>[`# pip install git+https://github.com/Google-Health/cxr-foundation.git#subdirectory=python 207 208# Load image as grayscale (Stillwaterising, CC0, via Wikimedia Commons) 209import requests 210from PIL import Image 211from io import BytesIO 212image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
213img = Image.open(requests.get(image_url, headers={'User-Agent': 'Demo'}, stream=True).raw).convert('L') 214 215# Run inference 216from clientside.clients import make_hugging_face_client 217cxr_client = make_hugging_face_client('cxr_model') 218print(cxr_client.get_image_embeddings_from_images([img]))`],PT=t=>{let a,r,l;return a="<ENCODER>",r="<NUMBER_OF_FEATURES>",l="<OUT_CHANNELS>",t.id==="depth-anything/Depth-Anything-V2-Small"?(a="vits",r="64",l="[48, 96, 192, 384]"):t.id==="depth-anything/Depth-Anything-V2-Base"?(a="vitb",r="128",l="[96, 192, 384, 768]"):t.id==="depth-anything/Depth-Anything-V2-Large"&&(a="vitl",r="256",l="[256, 512, 1024, 1024"),[` 219# Install from https://github.com/DepthAnything/Depth-Anything-V2 220 221# Load the model and infer depth from an image 222import cv2 223import torch 224 225from depth_anything_v2.dpt import DepthAnythingV2 226 227# instantiate the model 228model = DepthAnythingV2(encoder="${a}", features=${r}, out_channels=${l}) 229 230# load the weights 231filepath = hf_hub_download(repo_id="${t.id}", filename="depth_anything_v2_${a}.pth", repo_type="model") 232state_dict = torch.load(filepath, map_location="cpu") 233model.load_state_dict(state_dict).eval() 234 235raw_img = cv2.imread("your/image/path") 236depth = model.infer_image(raw_img) # HxW raw depth map in numpy 237 `]},VT=t=>[`# Download checkpoint 238pip install huggingface-hub 239huggingface-cli download --local-dir checkpoints ${t.id}`,`import depth_pro 240 241# Load model and preprocessing transform 242model, transform = depth_pro.create_model_and_transforms() 243model.eval() 244 245# Load and preprocess an image. 246image, _, f_px = depth_pro.load_rgb("example.png") 247image = transform(image) 248 249# Run inference. 250prediction = model.infer(image, f_px=f_px) 251 252# Results: 1. Depth in meters 253depth = prediction["depth"] 254# Results: 2. Focal length in pixels 255focallength_px = prediction["focallength_px"]`],$T=()=>[`from huggingface_hub import from_pretrained_keras 256import tensorflow as tf, requests 257 258# Load and format input 259IMAGE_URL = "https://storage.googleapis.com/dx-scin-public-data/dataset/images/3445096909671059178.png" 260input_tensor = tf.train.Example( 261 features=tf.train.Features( 262 feature={
263 "image/encoded": tf.train.Feature( 264 bytes_list=tf.train.BytesList(value=[requests.get(IMAGE_URL, stream=True).content]) 265 ) 266 } 267 ) 268).SerializeToString() 269 270# Load model and run inference 271loaded_model = from_pretrained_keras("google/derm-foundation") 272infer = loaded_model.signatures["serving_default"] 273print(infer(inputs=tf.constant([input_tensor])))`],FT=t=>[`import soundfile as sf 274from dia.model import Dia 275 276model = Dia.from_pretrained("${t.id}") 277text = "[S1] Dia is an open weights text to dialogue model. [S2] You get full control over scripts and voices. [S1] Wow. Amazing. (laughs) [S2] Try it now on Git hub or Hugging Face." 278output = model.generate(text) 279 280sf.write("simple.mp3", output, 44100)`],XT=t=>[`# pip install git+https://github.com/NVlabs/describe-anything 281from huggingface_hub import snapshot_download 282from dam import DescribeAnythingModel 283 284snapshot_download(${t.id}, local_dir="checkpoints") 285 286dam = DescribeAnythingModel( 287 model_path="checkpoints", 288 conv_mode="v1", 289 prompt_mode="focal_prompt", 290)`],ty="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",ny="Turn this cat into a dog",sf="A man with short gray hair plays a red electric guitar.",YT=t=>[`from diffusers import DiffusionPipeline 291 292pipe = DiffusionPipeline.from_pretrained("${t.id}") 293 294prompt = "${Qa(t)??ty}" 295image = pipe(prompt).images[0]`],QT=t=>[`from diffusers import DiffusionPipeline 296from diffusers.utils import load_image 297 298pipe = DiffusionPipeline.from_pretrained("${t.id}") 299 300prompt = "${Qa(t)??ny}" 301input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") 302 303image = pipe(image=input_image, prompt=prompt).images[0]`],KT=t=>[`import torch 304from diffusers import DiffusionPipeline 305from diffusers.utils import load_image, export_to_video 306 307pipe = DiffusionPipeline.from_pretrained("${t.id}", torch_dtype=torch.float16) 308pipe.to("cuda") 309 310prompt = "${Qa(t)??sf}" 311image = load_image( 312 "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" 313) 314 315output = pipe(image=image, prompt=prompt).frames[0] 316export_to_video(output, "output.mp4")`],ZT=t=>[`from diffusers import ControlNetModel, StableDiffusionControlNetPipeline 317 318controlnet = ControlNetModel.from_pretrained("${t.id}") 319pipe = StableDiffusionControlNetPipeline.from_pretrained( 320 "${$i(t)}", controlnet=controlnet 321)`],JT=t=>[`from diffusers import DiffusionPipeline 322 323pipe = DiffusionPipeline.from_pretrained("${$i(t)}") 324pipe.load_lora_weights("${t.id}") 325 326prompt = "${Qa(t)??ty}" 327image = pipe(prompt).images[0]`],GT=t=>[`from diffusers import DiffusionPipeline 328from diffusers.utils import load_image 329 330pipe = DiffusionPipeline.from_pretrained("${$i(t)}") 331pipe.load_lora_weights("${t.id}") 332 333prompt = "${Qa(t)??ny}" 334input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") 335 336image = pipe(image=input_image, prompt=prompt).images[0]`],WT=t=>[`from diffusers import DiffusionPipeline 337from diffusers.utils import export_to_video 338 339pipe = DiffusionPipeline.from_pretrained("${$i(t)}") 340pipe.load_lora_weights("${t.id}") 341 342prompt = "${Qa(t)??sf}" 343 344output = pipe(prompt=prompt).frames[0] 345export_to_video(output, "output.mp4")`],eE=t=>[`from diffusers import DiffusionPipeline 346from diffusers.utils import load_image, export_to_video 347 348pipe = DiffusionPipeline.from_pretrained("${$i(t)}") 349pipe.load_lora_weights("${t.id}") 350 351prompt = "${Qa(t)??sf}" 352input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") 353 354image = pipe(image=input_image, prompt=prompt).frames[0] 355export_to_video(output, "output.mp4")`],tE=t=>[`from diffusers import DiffusionPipeline 356 357pipe = DiffusionPipeline.from_pretrained("${$i(t)}") 358pipe.load_textual_inversion("${t.id}")`],nE=t=>[`import torch 359from diffusers import FluxFillPipeline 360from diffusers.utils import load_image 361 362image = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup.png") 363mask = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup_mask.png") 364 365pipe = FluxFillPipeline.from_pretrained("${t.id}", torch_dtype=torch.bfloat16).to("cuda") 366image = pipe( 367 prompt="a white paper cup", 368 image=image, 369 mask_image=mask, 370 height=1632, 371 width=1232, 372 guidance_scale=30, 373 num_inference_steps=50, 374 max_sequence_length=512, 375 generator=torch.Generator("cpu").manual_seed(0) 376).images[0] 377image.save(f"flux-fill-dev.png")`],aE=t=>[`import torch 378from diffusers import AutoPipelineForInpainting 379from diffusers.utils import load_image 380 381pipe = AutoPipelineForInpainting.from_pretrained("${t.id}", torch_dtype=torch.float16, variant="fp16").to("cuda") 382 383img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" 384mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" 385 386image = load_image(img_url).resize((1024, 1024)) 387mask_image = load_image(mask_url).resize((1024, 1024)) 388 389prompt = "a tiger sitting on a park bench" 390generator = torch.Generator(device="cuda").manual_seed(0) 391 392image = pipe( 393 prompt=prompt, 394 image=image, 395 mask_image=mask_image, 396 guidance_scale=8.0, 397 num_inference_steps=20, # steps between 15 and 30 work well for us 398 strength=0.99, # make sure to use \`strength\` below 1.0 399 generator=generator, 400).images[0]`],ay=t=>t.tags.includes("StableDiffusionInpaintPipeline")||t.tags.includes("StableDiffusionXLInpaintPipeline")?aE(t):t.tags.includes("controlnet")?ZT(t):t.tags.includes("lora")?t.pipeline_tag==="image-to-image"?GT(t):t.pipeline_tag==="image-to-vi
400deo"?eE(t):t.pipeline_tag==="text-to-video"?WT(t):JT(t):t.tags.includes("textual_inversion")?tE(t):t.tags.includes("FluxFillPipeline")?nE(t):t.pipeline_tag==="image-to-video"?KT(t):t.pipeline_tag==="image-to-image"?QT(t):YT(t),iE=t=>{const a=`# Pipeline for Stable Diffusion 3 401from diffusionkit.mlx import DiffusionPipeline 402 403pipeline = DiffusionPipeline( 404 shift=3.0, 405 use_t5=False, 406 model_version=${t.id}, 407 low_memory_mode=True, 408 a16=True, 409 w16=True, 410)`,r=`# Pipeline for Flux 411from diffusionkit.mlx import FluxPipeline 412 413pipeline = FluxPipeline( 414 shift=1.0, 415 model_version=${t.id}, 416 low_memory_mode=True, 417 a16=True, 418 w16=True, 419)`,l=`# Image Generation 420HEIGHT = 512 421WIDTH = 512 422NUM_STEPS = ${t.tags.includes("flux")?4:50} 423CFG_WEIGHT = ${t.tags.includes("flux")?0:5} 424 425image, _ = pipeline.generate_image( 426 "a photo of a cat", 427 cfg_weight=CFG_WEIGHT, 428 num_steps=NUM_STEPS, 429 latent_size=(HEIGHT // 8, WIDTH // 8), 430)`;return[t.tags.includes("flux")?r:a,l]},rE=t=>[`# pip install --no-binary :all: cartesia-pytorch 431from cartesia_pytorch import ReneLMHeadModel 432from transformers import AutoTokenizer 433 434model = ReneLMHeadModel.from_pretrained("${t.id}") 435tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf") 436 437in_message = ["Rene Descartes was"] 438inputs = tokenizer(in_message, return_tensors="pt") 439 440outputs = model.generate(inputs.input_ids, max_length=50, top_k=100, top_p=0.99) 441out_message = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] 442 443print(out_message) 444)`],oE=t=>[`import mlx.core as mx 445import cartesia_mlx as cmx 446 447model = cmx.from_pretrained("${t.id}") 448model.set_dtype(mx.float32) 449 450prompt = "Rene Descartes was" 451 452for text in model.generate( 453 prompt, 454 max_tokens=500, 455 eval_every_n=5, 456 verbose=True, 457 top_p=0.99, 458 temperature=0.85, 459): 460 print(text, end="", flush=True) 461`],lE=t=>{const a=Nn(t.id).replaceAll("-","_");return[`# Load it from the Hub directly 462import edsnlp 463nlp = edsnlp.load("${t.id}") 464`,`# Or install it as a package 465!pip install git+https://huggingface.co/${t.id} 466 467# and import it as a module 468import ${a} 469 470nlp = ${a}.load() # or edsnlp.load("${a}") 471`]},sE=t=>[`from espnet2.bin.tts_inference import Text2Speech 472 473model = Text2Speech.from_pretrained("${t.id}") 474 475speech, *_ = model("text to generate speech from")`],uE=t=>[`from espnet2.bin.asr_inference import Speech2Text 476 477model = Speech2Text.from_pretrained( 478 "${t.id}" 479) 480 481speech, rate = soundfile.read("speech.wav") 482text, *_ = model(speech)[0]`],cE=()=>["unknown model type (must be text-to-speech or automatic-speech-recognition)"],fE=t=>t.tags.includes("text-to-speech")?sE(t):t.tags.includes("automatic-speech-recognition")?uE(t):cE(),pE=t=>[`from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub 483 484models, cfg, task = load_model_ensemble_and_task_from_hf_hub( 485 "${t.id}" 486)`],dE=t=>[`from flair.models import SequenceTagger 487 488tagger = SequenceTagger.load("${t.id}")`],mE=t=>[`from gliner import GLiNER 489 490model = GLiNER.from_pretrained("${t.id}")`],hE=t=>[`# Download model 491from huggingface_hub import snapshot_download 492 493snapshot_download(${t.id}, local_dir="checkpoints") 494 495from indextts.infer import IndexTTS 496 497# Ensure config.yaml is present in the checkpoints directory 498tts = IndexTTS(model_dir="checkpoints", cfg_path="checkpoints/config.yaml") 499 500voice = "path/to/your/reference_voice.wav" # Path to the voice reference audio file 501text = "Hello, how are you?" 502output_path = "output_index.wav" 503 504tts.infer(voice, text, output_path)`],gE=t=>[`# CLI usage 505# see docs: https://ai-riksarkivet.github.io/htrflow/latest/getting_started/quick_start.html 506htrflow pipeline <path/to/pipeline.yaml> <path/to/image>`,`# Python usage 507from htrflow.pipeline.pipeline import Pipeline 508from htrflow.pipeline.steps import Task 509from htrflow.models.framework.model import ModelClass 510 511pipeline = Pipeline( 512 [ 513 Task( 514 ModelClass, {"model": "${t.id}"}, {} 515 ), 516 ])`],yE=t=>[`# Available backend options are: "jax", "torch", "tensorflow". 517import os 518os.environ["KERAS_BACKEND"] = "jax" 519 520import keras 521 522model = keras.saving.load_model("hf://${t.id}") 523`],bE=t=>` 524import keras_hub 525 526# Load CausalLM model (optional: use half precision for inference) 527causal_lm = keras_hub.models.CausalLM.from_preset("hf://${t}", dtype="bfloat16") 528causal_lm.compile(sampler="greedy") # (optional) specify a sampler 529 530# Generate text 531causal_lm.generate("Keras: deep learning for", max_length=64) 532`,vE=t=>` 533import keras_hub 534 535# Load TextToImage model (optional: use half precision for inference) 536text_to_image = keras_hub.models.TextToImage.from_preset("hf://${t}", dtype="bfloat16") 537 538# Generate images with a TextToImage model. 539text_to_image.generate("Astronaut in a jungle") 540`,wE=t=>` 541import keras_hub 542 543# Load TextClassifier model 544text_classifier = keras_hub.models.TextClassifier.from_preset( 545 "hf://${t}", 546 num_classes=2, 547) 548# Fine-tune 549text_classifier.fit(x=["Thilling adventure!", "Total snoozefest."], y=[1, 0]) 550# Classify text 551text_classifier.predict(["Not my cup of tea."]) 552`,_E=t=>` 553import keras_hub 554import keras 555 556# Load ImageClassifier model 557image_classifier = keras_hub.models.ImageClassifier.from_preset( 558 "hf://${t}", 559 num_classes=2, 560) 561# Fine-tune 562image_classifier.fit( 563 x=keras.random.randint((32, 64, 64, 3), 0, 256), 564 y=keras.random.randint((32, 1), 0, 2), 565) 566# Classify image 567image_classifier.predict(keras.random.randint((1, 64, 64, 3), 0, 256)) 568`,og={CausalLM:bE,TextToImage:vE,TextClassifier:wE,ImageClassifier:_E},xE=(t,a)=>` 569import keras_hub 570 571# Create a ${t} model 572task = keras_hub.models.${t}.from_preset("hf://${a}") 573`,SE=t=>` 574import keras_hub 575 576# Create a Backbone model unspecialized for any task 577backbone = keras_hub.models.Backbone.from_preset("hf://${t}") 578`,AE=t=>{const a=t.id,r=t.config?.keras_hub?.tasks??[],l=[];for(const[s,c]of Object.entries(og))r.includes(s)&&l.push(c(a));for(const s of r)Object.keys(og).includes(s)||l.push(xE(s,a));return l.push(SE(a)),l},kE=t=>[`# Example usage for KimiAudio 579# pip install git+https://github.com/MoonshotAI/Kimi-Audio.git 580 581from kimia_infer.api.kimia import KimiAudio 582 583model = KimiAudio(model_path="${t.id}", load_detokenizer=True) 584 585sampling_params = { 586 "audio_temperature": 0.8, 587 "audio_top_k": 10, 588 "text_temperature": 0.0, 589 "text_top_k": 5, 590} 591 592# For ASR 593asr_audio = "asr_example.wav" 594messages_asr = [ 595 {"role": "user", "message_type": "text", "content": "Please transcribe the following audio:"}, 596 {"role": "user", "message_type": "audio", "content": asr_audio} 597] 598_, text = model.generate(messages_asr, **sampling_params, output_type="text") 599print(text) 600 601# For Q&A 602qa_audio = "qa_example.wav" 603messages_conv = [{"role": "user", "message_type": "audio", "content": qa_audio}] 604wav, text = model.generate(messages_conv, **sampling_params, output_type="both") 605sf.write("output_audio.wav", wav.cpu().view(-1).numpy(), 24000) 606print(text) 607`],TE=t=>[`from kittentts import KittenTTS 608m = KittenTTS("${t.id}") 609 610audio = m.generate("This high quality TTS model works without a GPU") 611 612# Save the audio 613import soundfile as sf 614sf.write('output.wav', audio, 24000)`],EE=t=>t.tags.includes("bi-encoder")?[`#install from https://github.com/webis-de/lightning-ir 615 616from lightning_ir import BiEncoderModule 617model = BiEncoderModule("${t.id}") 618 619model.score("query", ["doc1", "doc2", "doc3"])`]:t.tags.includes("cross-encoder")?[`#install from https://github.com/webis-de/lightning-ir 620 621from lightning_ir import CrossEncoderModule 622model = CrossEncoderModule("${t.id}") 623 624model.score("query", ["doc1", "doc2", "doc3"])`]:[`#install from https://github.com/webis-de/lightning-ir 625 626from lightning_ir import BiEncoderModule, CrossEncoderModule 627 628# depending on the model type, use either BiEncoderModule or CrossEncoderModule 629model = BiEncoderModule("${t.id}") 630# model = CrossEncoderModule("${t.id}") 631 632model.score("query", ["doc1", "doc2", "doc3"])`],CE=t=>{const a=[`# !pip install llama-cpp-python 633 634from llama_cpp import Llama 635 636llm = Llama.from_pretrained( 637 repo_id="${t.id}", 638 filename="{{GGUF_FILE}}", 639) 640`];if(t.tags.includes("conversational")){const r=TT(t);a.push(`llm.create_chat_completion( 641 messages = ${ET(r,{attributeKeyQuotes:!0,indent:" "})} 642)`)}else a.push(`output = llm( 643 "Once upon a time,", 644 max_tokens=512, 645 echo=True 646) 647print(output)`);return a},RE=t=>{if(t.tags.includes("smolvla")){const a=[`# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details 648git clone https://github.com/huggingface/lerobot.git 649cd lerobot 650pip install -e .[smolvla]`,`# Launch finetuning on your dataset 651python lerobot/scripts/train.py \\ 652--policy.path=${t.id} \\ 653--dataset.repo_id=lerobot/svla_so101_pickplace \\ 654--batch_size=64 \\ 655--steps=20000 \\ 656--output_dir=outputs/train/my_smolvla \\ 657--job_name=my_smolvla_training \\ 658--policy.device=cuda \\ 659--wandb.enable=true`];return t.id!=="lerobot/smolvla_base"&&a.push(`# Run the policy using the record function 660python -m lerobot.record \\ 661 --robot.type=so101_follower \\ 662 --robot.port=/dev/ttyACM0 \\ # <- Use your port 663 --robot.id=my_blue_follower_arm \\ # <- Use your robot id 664 --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \\ # <- Use your cameras 665 --dataset.single_task="Grasp a lego block and put it in the bin." \\ # <- Use the same task description you used in your dataset recording 666 --dataset.repo_id=HF_USER/dataset_name \\ # <- This will be the dataset name on HF Hub 667 --dataset.episode_time_s=50 \\ 668 --dataset.num_episodes=10 \\ 669 --policy.path=${t.id}`),a}return[]},DE=t=>[`# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) 670# See https://github.com/keras-team/tf-keras for more details. 671from huggingface_hub import from_pretrained_keras 672 673model = from_pretrained_keras("${t.id}") 674`],OE=t=>[`from mamba_ssm import MambaLMHeadModel 675 676model = MambaLMHeadModel.from_pretrained("${t.id}")`],ME=t=>
676[`# Install from https://github.com/Camb-ai/MARS5-TTS 677 678from inference import Mars5TTS 679mars5 = Mars5TTS.from_pretrained("${t.id}")`],UE=t=>[`# Install from https://github.com/pq-yang/MatAnyone.git 680 681from matanyone.model.matanyone import MatAnyone 682model = MatAnyone.from_pretrained("${t.id}")`,` 683from matanyone import InferenceCore 684processor = InferenceCore("${t.id}")`],NE=()=>[`# Install from https://github.com/buaacyw/MeshAnything.git 685 686from MeshAnything.models.meshanything import MeshAnything 687 688# refer to https://github.com/buaacyw/MeshAnything/blob/main/main.py#L91 on how to define args 689# and https://github.com/buaacyw/MeshAnything/blob/main/app.py regarding usage 690model = MeshAnything(args)`],IE=t=>[`import open_clip 691 692model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:${t.id}') 693tokenizer = open_clip.get_tokenizer('hf-hub:${t.id}')`],jE=t=>{if(t.config?.architectures?.[0]){const a=t.config.architectures[0];return[[`from paddlenlp.transformers import AutoTokenizer, ${a}`,"",`tokenizer = AutoTokenizer.from_pretrained("${t.id}", from_hf_hub=True)`,`model = ${a}.from_pretrained("${t.id}", from_hf_hub=True)`].join(` 694`)]}else return[["# â ï¸ Type of model unknown","from paddlenlp.transformers import AutoTokenizer, AutoModel","",`tokenizer = AutoTokenizer.from_pretrained("${t.id}", from_hf_hub=True)`,`model = AutoModel.from_pretrained("${t.id}", from_hf_hub=True)`].join(` 695`)]},LE=t=>{const a={textline_detection:{className:"TextDetection"},textline_recognition:{className:"TextRecognition"},seal_text_detection:{className:"SealTextDetection"},doc_img_unwarping:{className:"TextImageUnwarping"},doc_img_orientation_classification:{className:"DocImgOrientationClassification"},textline_orientation_classification:{className:"TextLineOrientationClassification"},chart_parsing:{className:"ChartParsing"},formula_recognition:{className:"FormulaRecognition"},layout_detection:{className:"LayoutDetection"},table_cells_detection:{className:"TableCellsDetection"},wired_table_classification:{className:"TableClassification"},table_structure_recognition:{className:"TableStructureRecognition"}};if(t.tags.includes("doc_vlm"))return[`# pip install paddleocr 696from paddleocr import DocVLM 697model = DocVLM(model_name="${Nn(t.id)}") 698output = model.predict( 699 input={"image": "path/to/image.png", "query": "Parsing this image and output the content in Markdown format."}, 700 batch_size=1 701) 702for res in output: 703 res.print() 704 res.save_to_json(save_path="./output/res.json")`];for(const r of t.tags)if(r in a){const{className:l}=a[r];return[`# pip install paddleocr 705from paddleocr import ${l} 706model = ${l}(model_name="${Nn(t.id)}") 707output = model.predict(input="path/to/image.png", batch_size=1) 708for res in output: 709 res.print() 710 res.save_to_img(save_path="./output/") 711 res.save_to_json(save_path="./output/res.json")`]}return[`# Please refer to the document for information on how to use the model. 712# https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/module_overview.html`]},zE=t=>{const a=`# Use PE-Core models as CLIP models 713import core.vision_encoder.pe as pe 714 715model = pe.CLIP.from_config("${t.id}", pretrained=True)`,r=`# Use any PE model as a vision encoder 716import core.vision_encoder.pe as pe 717 718model = pe.VisionTransformer.from_config("${t.id}", pretrained=True)`;return t.id.includes("Core")?[a,r]:[r]},qE=t=>[`from huggingface_hub import snapshot_download 719from phantom_wan import WANI2V, configs 720 721checkpoint_dir = snapshot_download("${t.id}") 722wan_i2v = WanI2V( 723 config=configs.WAN_CONFIGS['i2v-14B'], 724 checkpoint_dir=checkpoint_dir, 725 ) 726 video = wan_i2v.generate(text_prompt, image_prompt)`],BE=t=>[`from pyannote.audio import Pipeline 727 728pipeline = Pipeline.from_pretrained("${t.id}") 729 730# inference on the whole file 731pipeline("file.wav") 732 733# inference on an excerpt 734from pyannote.core import Segment 735excerpt = Segment(start=2.0, end=5.0) 736 737from pyannote.audio import Audio 738waveform, sample_rate = Audio().crop("file.wav", excerpt) 739pipeline({"waveform": waveform, "sample_rate": sample_rate})`],HE=t=>[`from pyannote.audio import Model, Inference 740 741model = Model.from_pretrained("${t.id}") 742inference = Inference(model) 743 744# inference on the whole file 745inference("file.wav") 746 747# inference on an excerpt 748from pyannote.core import Segment 749excerpt = Segment(start=2.0, end=5.0) 750inference.crop("file.wav", excerpt)`],PE=t=>t.tags.includes("pyannote-audio-pipeline")?BE(t):HE(t),VE=t=>[`from relik import Relik 751 752relik = Relik.from_pretrained("${t.id}")`],$E=t=>[`# Install from https://github.com/microsoft/renderformer 753 754from renderformer import RenderFormerRenderingPipeline 755pipeline = RenderFormerRenderingPipeline.from_pretrained("${t.id}")`],FE=t=>[`from tensorflow_tts.inference import AutoProcessor, TFAutoModel 756 757processor = AutoProcessor.from_pretrained("${t.id}") 758model = TFAutoModel.from_pretrained("${t.id}") 759`],XE=t=>[`from tensorflow_tts.inference import TFAutoModel 760 761model = TFAutoModel.from_pretrained("${t.id}") 762audios = model.inference(mels) 763`],YE=t=>[`from tensorflow_tts.inference import TFAutoModel 764 765model = TFAutoModel.from_pretrained("${t.id}") 766`],QE=t=>t.tags.includes("text-to-mel")?FE(t):t.tags.includes("mel-to-wav")?XE(t):YE(t),KE=t=>[`import timm 767 768model = timm.create_model("hf_hub:${t.id}", pretrained=True)`],ZE=()=>[`# pip install sae-lens 769from sae_lens import SAE 770 771sae, cfg_dict, sparsity = SAE.from_pretrained( 772 release = "RELEASE_ID", # e.g., "gpt2-small-res-jb". See other options in https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml 773 sae_id = "SAE_ID", # e.g., "blocks.8.hook_resid_pre". Won't always be a hook point 774)`],JE=()=>[`# seed_story_cfg_path refers to 'https://github.com/TencentARC/SEED-Story/blob/master/configs/clm_models/agent_7b_sft.yaml' 775# llm_cfg_path refers to 'https://github.com/TencentARC/SEED-Story/blob/master/configs/clm_models/llama2chat7b_lora.yaml' 776from omegaconf import OmegaConf 777import hydra 778 779# load Llama2 780llm_cfg = OmegaConf.load(llm_cfg_path) 781llm = hydra.utils.instantiate(llm_cfg, torch_dtype="fp16") 782 783# initialize seed_story 784seed_story_cfg = OmegaConf.load(seed_story_cfg_path) 785seed_story = hydra.utils.instantiate(seed_story_cfg, llm=llm) `],GE=(t,a)=>[`import joblib 786from skops.hub_utils import download 787download("${t.id}", "path_to_folder") 788model = joblib.load( 789 "${a}" 790) 791# only load pickle files from sources you trust 792# read more about it here https://skops.readthedocs.io/en/stable/persistence.html`],WE=(t,a)=>[`from skops.hub_utils import download 793from skops.io import load 794download("${t.id}", "path_to_folder") 795# make sure model file is in skops format 796# if model is a pickle file, make sure it's from a source you trust 797model = load("path_to_folder/${a}")`],e2=t=>[`from huggingface_hub import hf_hub_download 798import joblib 799model = joblib.load( 800 hf_hub_download("${t.id}", "sklearn_model.joblib") 801) 802# only load pickle files from sources you trust 803# read more about it here https://skops.readthedocs.io/en/stable/persistence.html`],t2=t=>{if(t.tags.includes("skops")){const a=t.config?.sklearn?.model?.file,r=t.config?.sklearn?.model_format;return a?r==="pickle"?GE(t,a):WE(t,a):["# â ï¸ Model filename not specified in config.json"]}else return e2(t)},n2=t=>[`import torch 804import torchaudio 805from einops import rearrange 806from stable_audio_tools import get_pretrained_model 807from stable_audio_tools.inference.generation import generate_diffusion_cond 808 809device = "cuda" if torch.cuda.is_available() else "cpu" 810 811# Download model 812model, model_config = get_pretrained_model("${t.id}") 813sample_rate = model_config["sample_rate"] 814sample_size = model_config["sample_size"] 815 816model = model.to(device) 817 818# Set up text and timing conditioning 819conditioning = [{ 820 "prompt": "128 BPM tech house drum loop", 821}] 822 823# Generate stereo audio 824output = generate_diffusion_cond( 825 model, 826 conditioning=conditioning, 827 sample_size=sample_size, 828 device=device 829) 830 831# Rearrange audio batch to a single sequence 832output = rearrange(output, "b d n -> d (b n)") 833 834# Peak normalize, clip, convert to int16, and save to file 835output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu() 836torchaudio.save("output.wav", output, sample_rate)`],a2=t=>[`from huggingface_hub import from_pretrained_fastai 837 838learn = from_pretrained_fastai("${t.id}")`],i2=t=>{const a=`# Use SAM2 with images 839import torch 840from sam2.sam2_image_predictor import SAM2ImagePredictor 841 842predictor = SAM2ImagePredictor.from_pretrained(${t.id}) 843 844with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): 845 predictor.set_image(<your_image>) 846 masks, _, _ = predictor.predict(<input_prompts>)`,r=`# Use SAM2 with videos 847import torch 848from sam2.sam2_video_predictor import SAM2VideoPredictor 849 850predictor = SAM2VideoPredictor.from_pretrained(${t.id}) 851 852with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): 853 state = predictor.init_state(<your_video>) 854 855 # add new prompts and instantly get the output on the same frame 856 frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): 857 858 # propagate the prompts to get masklets throughout the video 859 for frame_idx, object_ids, masks in predictor.propagate_in_video(state): 860 ...`;return[a,r]},r2=t=>[`python -m sample_factory.huggingface.load_from_hub -r ${t.id} -d ./train_dir`];function o2(t){const a=t.widgetData?.[0];if(a?.source_sentence&&a?.sentences?.length)return[a.source_sentence,...a.sentences]}const l2=t=>{const a=t.tags.includes(ey)?", trust_remote_code=True":"";if(t.tags.includes("cross-encoder")||t.pipeline_tag=="text-ranking")return[`from sentence_transformers import CrossEncoder 861 862model = CrossEncoder("${t.id}"${a}) 863 864query = "Which planet is known as the Red Planet?" 865passages = [ 866 "Venus is often called Earth's twin because of its similar size and proximity.", 867 "Mars, known for its reddish appearance, is often referred to as the Red Planet.", 868 "Jupiter, the largest planet in our solar system, has a prominent red spot.", 869 "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." 870] 871 872scores = model.predict([(query, passage) for passage in passages]) 873print(scores)`];const r=o2(t)??["The weather is lovely today.","It's so sunny outside!","He drove to the stadium."];return[`from sentence_transformers import SentenceTransformer 874 875model = SentenceTransformer("${t.id}"${a}) 876 877sentences = ${JSON.stringify(r,null,4)} 878embeddings = model.encode(sentences) 879 880similarities = model.similarity(embeddings, embeddings) 881print(similarities.shape) 882# [${r.length}, ${r.length}]`]},s2=t=>[`from setfit import SetFitModel 883 884model = SetFitModel.from_pretrained("${t.id}")`],u2=t=>[`!pip install https://huggingface.co/${t.id}/resolve/main/${Nn(t.id)}-any-py3-none-any.whl 885 886# Using spacy.load(). 887import spacy 888nlp = spacy.load("${Nn(t.id)}") 889 890# Importing as module. 891import ${Nn(t.id)} 892nlp = ${Nn(t.id)}.load()`],c2=t=>[`from span_marker import SpanMarkerModel 893 894model = SpanMarkerModel.from_pretrained("${t.id}")`],f2=t=>[`import stanza 895 896stanza.download("${Nn(t.id).replace("stanza-","")}") 897nlp = stanza.Pipeline("${Nn(t.id).replace("stanza-","")}")`],p2=t=>{switch(t){case"EncoderClassifier":return"classify_file";case"EncoderDecoderASR":case"EncoderASR":return"transcribe_file";case"SpectralMaskEnhancement":return"enhance_file";case"SepformerSeparation":return"separate_file";default:return}},d2=t=>{const a=t.config?.speechbrain?.speechbrain_interface;if(a===void 0)return["# interface not specified in config.json"];const r=p2(a);
897return r===void 0?["# interface in config.json invalid"]:[`from speechbrain.pretrained import ${a} 898model = ${a}.from_hparams( 899 "${t.id}" 900) 901model.${r}("file.wav")`]},m2=t=>[`from terratorch.registry import BACKBONE_REGISTRY 902 903model = BACKBONE_REGISTRY.build("${t.id}")`],h2=t=>t.config?.tokenizer_config?.chat_template!==void 0||t.config?.processor_config?.chat_template!==void 0||t.config?.chat_template_jinja!==void 0,iy=t=>{const a=t.transformersInfo;if(!a)return["# â ï¸ Type of model unknown"];const r=t.tags.includes(ey)?", trust_remote_code=True":"",l=[];if(a.processor){const s=a.processor==="AutoTokenizer"?"tokenizer":a.processor==="AutoFeatureExtractor"?"extractor":"processor";l.push("# Load model directly",`from transformers import ${a.processor}, ${a.auto_model}`,"",`${s} = ${a.processor}.from_pretrained("${t.id}"`+r+")",`model = ${a.auto_model}.from_pretrained("${t.id}"`+r+")"),t.tags.includes("conversational")&&h2(t)&&(t.tags.includes("image-text-to-text")?l.push("messages = [",[" {",' "role": "user",',' "content": [',' {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},',' {"type": "text", "text": "What animal is on the candy?"}'," ]"," },"].join(` 904`),"]"):l.push("messages = [",' {"role": "user", "content": "Who are you?"},',"]"),l.push(`inputs = ${s}.apply_chat_template(`," messages,"," add_generation_prompt=True,"," tokenize=True,"," return_dict=True,",' return_tensors="pt",',").to(model.device)","","outputs = model.generate(**inputs, max_new_tokens=40)",`print(${s}.decode(outputs[0][inputs["input_ids"].shape[-1]:]))`))}else l.push("# Load model directly",`from transformers import ${a.auto_model}`,`model = ${a.auto_model}.from_pretrained("${t.id}"`+r+', torch_dtype="auto")');if(t.pipeline_tag&&sk.transformers?.includes(t.pipeline_tag)){const s=["# Use a pipeline as a high-level helper","from transformers import pipeline","",`pipe = pipeline("${t.pipeline_tag}", model="${t.id}"`+r+")"];return t.tags.includes("conversational")?t.tags.includes("image-text-to-text")?(s.push("messages = [",[" {",' "role": "user",',' "content": [',' {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},',' {"type": "text", "text": "What animal is on the candy?"}'," ]"," },"].join(` 905`),"]"),s.push("pipe(text=messages)")):(s.push("messages = [",' {"role": "user", "content": "Who are you?"},',"]"),s.push("pipe(messages)")):t.pipeline_tag==="zero-shot-image-classification"?s.push("pipe(",' "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png",',' candidate_labels=["animals", "humans", "landscape"],',")"):t.pipeline_tag==="image-classification"&&s.push('pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")'),[s.join(` 906`),l.join(` 907`)]}return[l.join(` 908`)]},g2=t=>{if(!t.pipeline_tag)return["// â ï¸ Unknown pipeline tag"];const a="@huggingface/transformers";return[`// npm i ${a} 909import { pipeline } from '${a}'; 910 911// Allocate pipeline 912const pipe = await pipeline('${t.pipeline_tag}', '${t.id}');`]},y2=t=>{switch(t){case"CAUSAL_LM":return"CausalLM";case"SEQ_2_SEQ_LM":return"Seq2SeqLM";case"TOKEN_CLS":return"TokenClassification";case"SEQ_CLS":return"SequenceClassification";default:return}},b2=t=>{const{base_model_name_or_path:a,task_type:r}=t.config?.peft??{},l=y2(r);return l?a?[`from peft import PeftModel 913from transformers import AutoModelFor${l} 914 915base_model = AutoModelFor${l}.from_pretrained("${a}") 916model = PeftModel.from_pretrained(base_model, "${t.id}")`]:["Base model is not found."]:["Task type is invalid."]},v2=t=>[`from huggingface_hub import hf_hub_download 917import fasttext 918 919model = fasttext.load_model(hf_hub_download("${t.id}", "model.bin"))`],w2=t=>[`from huggingface_sb3 import load_from_hub 920checkpoint = load_from_hub( 921 repo_id="${t.id}", 922 filename="{MODEL FILENAME}.zip", 923)`],_2=(t,a)=>{switch(t){case"ASR":return[`import nemo.collections.asr as nemo_asr 924asr_model = nemo_asr.models.ASRModel.from_pretrained("${a.id}") 925 926transcriptions = asr_model.transcribe(["file.wav"])`];default:return}},x2=t=>[`mlagents-load-from-hf --repo-id="${t.id}" --local-dir="./download: string[]s"`],S2=()=>[`string modelName = "[Your model name here].sentis"; 927Model model = ModelLoader.Load(Application.streamingAssetsPath + "/" + modelName); 928IWorker engine = WorkerFactory.CreateWorker(BackendType.GPUCompute, model); 929// Please see provided C# file for more details 930`],A2=t=>[` 931# Load the model and infer image from text 932import torch 933from app.sana_pipeline import SanaPipeline 934from torchvision.utils import save_image 935 936sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") 937sana.from_pretrained("hf://${t.id}") 938 939image = sana( 940 prompt='a cyberpunk cat with a neon sign that says "Sana"', 941 height=1024, 942 width=1024, 943 guidance_scale=5.0, 944 pag_guidance_scale=2.0, 945 num_inference_steps=18, 946) `],k2=t=>[`# Install from https://github.com/google-deepmind/videoprism 947import jax 948from videoprism import models as vp 949 950flax_model = vp.get_model("${t.id}") 951loaded_state = vp.load_pretrained_weights("${t.id}") 952 953@jax.jit 954def forward_fn(inputs, train=False): 955 return flax_model.apply(loaded_state, inputs, train=train)`],T2=t=>[`from Trainer_finetune import Model 956 957model = Model.from_pretrained("${t.id}")`],E2=t=>[`from huggingface_hub import hf_hub_download 958 from inference_onnx import LVFaceONNXInferencer 959 960model_path = hf_hub_download("${t.id}", "LVFace-L_Glint360K/LVFace-L_Glint360K.onnx") 961inferencer = LVFaceONNXInferencer(model_path, use_gpu=True, timeout=300) 962img_path = 'path/to/image1.jpg' 963embedding = inferencer.infer_from_image(img_path)`],C2=t=>[`from voicecraft import VoiceCraft 964 965model = VoiceCraft.from_pretrained("${t.id}")`],R2=()=>[`# !pip install git+https://github.com/fluxions-ai/vui 966 967import torchaudio 968 969from vui.inference import render 970from vui.model import Vui, 971 972model = Vui.from_pretrained().cuda() 973waveform = render( 974 model, 975 "Hey, here is some random stuff, usually something quite long as the shorter the text the less likely the model can cope!", 976) 977print(waveform.shape) 978torchaudio.save("out.opus", waveform[0], 22050) 979`],D2=()=>[`import ChatTTS 980import torchaudio 981 982chat = ChatTTS.Chat() 983chat.load_models(compile=False) # Set to True for better performance 984 985texts = ["PUT YOUR TEXT HERE",] 986 987wavs = chat.infer(texts, ) 988 989torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000)`],lg=t=>{const a=t.tags.find(s=>s.match(/^yolov\d+$/)),r=a?`YOLOv${a.slice(4)}`:"YOLOvXX";return[(a?"":`# Couldn't find a valid YOLO version tag. 990# Replace XX with the correct version. 991`)+`from ultralytics import ${r} 992 993model = ${r}.from_pretrained("${t.id}") 994source = 'http://images.cocodataset.org/val2017/000000039769.jpg' 995model.predict(source=source, save=True)`]},O2=t=>[`# Option 1: use with transformers 996 997from transformers import AutoModelForImageSegmentation 998birefnet = AutoModelForImageSegmentation.from_pretrained("${t.id}", trust_remote_code=True) 999`,`# Option 2: use with BiRefNet 1000 1001# Install from https://github.com/ZhengPeng7/BiRefNet 1002 1003from models.birefnet import BiRefNet 1004model = BiRefNet.from_pretrained("${t.id}")`],M2=t=>[`from swarmformer import SwarmFormerModel 1005 1006model = SwarmFormerModel.from_pretrained("${t.id}") 1007`],U2=t=>[`# Follow installation instructions at https://github.com/PKU-YuanGroup/UniWorld-V1 1008 1009from univa.models.qwen2p5vl.modeling_univa_qwen2p5vl import UnivaQwen2p5VLForConditionalGeneration 1010 model = UnivaQwen2p5VLForConditionalGeneration.from_pretrained( 1011 "${t.id}", 1012 torch_dtype=torch.bfloat16, 1013 attn_implementation="flash_attention_2", 1014 ).to("cuda") 1015 processor = AutoProcessor.from_pretrained("${t.id}") 1016`],N2=t=>[`# Download the model from the Hub 1017pip install huggingface_hub[hf_xet] 1018 1019huggingface-cli download --local-dir ${Nn(t.id)} ${t.id}`],I2=t=>[`# Make sure mlx-lm is installed 1020# pip install --upgrade mlx-lm 1021# if on a CUDA device, also pip install mlx[cuda] 1022 1023# Generate text with mlx-lm 1024from mlx_lm import load, generate 1025 1026model, tokenizer = load("${t.id}") 1027 1028prompt = "Once upon a time in" 1029text = generate(model, tokenizer, prompt=prompt, verbose=True)`],j2=t=>[`# Make sure mlx-lm is installed 1030# pip install --upgrade mlx-lm 1031 1032# Generate text with mlx-lm 1033from mlx_lm import load, generate 1034 1035model, tokenizer = load("${t.id}") 1036 1037prompt = "Write a story about Einstein" 1038messages = [{"role": "user", "content": prompt}] 1039prompt = tokenizer.apply_chat_template( 1040 messages, add_generation_prompt=True 1041) 1042 1043text = generate(model, tokenizer, prompt=prompt, verbose=True)`],L2=t=>[`# Make sure mlx-vlm is installed 1044# pip install --upgrade mlx-vlm 1045 1046from mlx_vlm import load, generate 1047from mlx_vlm.prompt_utils import apply_chat_template 1048from mlx_vlm.utils import load_config 1049 1050# Load the model 1051model, processor = load("${t.id}") 1052config = load_config("${t.id}") 1053 1054# Prepare input 1055image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] 1056prompt = "Describe this image." 1057 1058# Apply chat template 1059formatted_prompt = apply_chat_template( 1060 processor, config, prompt, num_images=1 1061) 1062 1063# Generate output 1064output = generate(model, processor, formatted_prompt, image) 1065print(output)`],z2=t=>
1065[`from mlxim.model import create_model 1066 1067model = create_model(${t.id})`],q2=t=>t.pipeline_tag==="image-text-to-text"?L2(t):t.pipeline_tag==="text-generation"?t.tags.includes("conversational")?j2(t):I2(t):N2(t),B2=t=>[`from model2vec import StaticModel 1068 1069model = StaticModel.from_pretrained("${t.id}")`],H2=t=>{let a;t.tags.includes("diffusers")?a=P2(t):t.tags.includes("transformers")?a=V2(t):a=$2(t);const r=l=>/^from pruna import PrunaModel/m.test(l)?l:`from pruna import PrunaModel 1070${l}`;return a=a.map(r),t.tags.includes("pruna_pro-ai")?a.map(l=>l.replace(/\bpruna\b/g,"pruna_pro").replace(/\bPrunaModel\b/g,"PrunaProModel")):a},P2=t=>ay(t).map(r=>r.replace(/\b\w*Pipeline\w*\b/g,"PrunaModel").replace(/from diffusers import ([^,\n]*PrunaModel[^,\n]*)/g,"").replace(/from diffusers import ([^,\n]+),?\s*([^,\n]*PrunaModel[^,\n]*)/g,"from diffusers import $1").replace(/from diffusers import\s*(\n|$)/g,"").replace(/from diffusers import PrunaModel/g,"from pruna import PrunaModel").replace(/from diffusers import ([^,\n]+), PrunaModel/g,"from diffusers import $1").replace(/from diffusers import PrunaModel, ([^,\n]+)/g,"from diffusers import $1").replace(/\n\n+/g,` 1071`).trim()),V2=t=>{const a=t.transformersInfo;let l=iy(t).map(s=>s.replace(/from transformers import pipeline/g,"from pruna import PrunaModel").replace(/pipeline\([^)]*\)/g,`PrunaModel.from_pretrained("${t.id}")`));return a?.auto_model&&(l=l.map(s=>s.replace(new RegExp(`from transformers import ${a.auto_model} 1072?`,"g"),"").replace(new RegExp(`${a.auto_model}.from_pretrained`,"g"),"PrunaModel.from_pretrained").replace(new RegExp(`^.*from.*import.*(, *${a.auto_model})+.*$`,"gm"),c=>c.replace(new RegExp(`, *${a.auto_model}`,"g"),"")))),l},$2=t=>[`from pruna import PrunaModel 1073model = PrunaModel.from_pretrained("${t.id}") 1074`],F2=t=>{let a;return t.tags.includes("automatic-speech-recognition")&&(a=_2("ASR",t)),a??["# tag did not correspond to a valid NeMo domain."]},X2=t=>{const a=t.tags??[];return a.includes("gguf")||a.includes("onnx")?[]:[` 1075 import outetts 1076 1077 enum = outetts.Models("${t.id}".split("/", 1)[1]) # VERSION_1_0_SIZE_1B 1078 cfg = outetts.ModelConfig.auto_config(enum, outetts.Backend.HF) 1079 tts = outetts.Interface(cfg) 1080 1081 speaker = tts.load_default_speaker("EN-FEMALE-1-NEUTRAL") 1082 tts.generate( 1083 outetts.GenerationConfig( 1084 text="Hello there, how are you doing?", 1085 speaker=speaker, 1086 ) 1087 ).save("output.wav") 1088 `]},Y2=t=>[`from pxia import AutoModel 1089 1090model = AutoModel.from_pretrained("${t.id}")`],Q2=t=>[`from pythae.models import AutoModel 1091 1092model = AutoModel.load_from_hf_hub("${t.id}")`],K2=t=>[`from audiocraft.models import MusicGen 1093 1094model = MusicGen.get_pretrained("${t.id}") 1095 1096descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] 1097wav = model.generate(descriptions) # generates 3 samples.`],Z2=t=>[`from audiocraft.models import MAGNeT 1098 1099model = MAGNeT.get_pretrained("${t.id}") 1100 1101descriptions = ['disco beat', 'energetic EDM', 'funky groove'] 1102wav = model.generate(descriptions) # generates 3 samples.`],J2=t=>[`from audiocraft.models import AudioGen 1103 1104model = AudioGen.get_pretrained("${t.id}") 1105model.set_generation_params(duration=5) # generate 5 seconds. 1106descriptions = ['dog barking', 'sirene of an emergency vehicle', 'footsteps in a corridor'] 1107wav = model.generate(descriptions) # generates 3 samples.`],G2=t=>[`from anemoi.inference.runners.default import DefaultRunner 1108from anemoi.inference.config.run import RunConfiguration 1109# Create Configuration 1110config = RunConfiguration(checkpoint = {"huggingface":"${t.id}"}) 1111# Load Runner 1112runner = DefaultRunner(config)`],W2=t=>t.tags.includes("musicgen")?K2(t):t.tags.includes("audiogen")?J2(t):t.tags.includes("magnet")?Z2(t):["# Type of model unknown."],eC=()=>[`# Install CLI with Homebrew on macOS device 1113brew install whisperkit-cli 1114 1115# View all available inference options 1116whisperkit-cli transcribe --help 1117 1118# Download and run inference using whisper base model 1119whisperkit-cli transcribe --audio-path /path/to/audio.mp3 1120 1121# Or use your preferred model variant 1122whisperkit-cli transcribe --model "large-v3" --model-prefix "distil" --audio-path /path/to/audio.mp3 --verbose`],tC=t=>[`from threedtopia_xl.models import threedtopia_xl 1123 1124model = threedtopia_xl.from_pretrained("${t.id}") 1125model.generate(cond="path/to/image.png")`],nC=t=>[`# pip install git+https://github.com/Zyphra/Zonos.git 1126import torchaudio 1127from zonos.model import Zonos 1128from zonos.conditioning import make_cond_dict 1129 1130model = Zonos.from_pretrained("${t.id}", device="cuda") 1131 1132wav, sr = torchaudio.load("speaker.wav") # 5-10s reference clip 1133speaker = model.make_speaker_embedding(wav, sr) 1134 1135cond = make_cond_dict(text="Hello, world!", speaker=speaker, language="en-us") 1136codes = model.generate(model.prepare_conditioning(cond)) 1137 1138audio = model.autoencoder.decode(codes)[0].cpu() 1139torchaudio.save("sample.wav", audio, model.autoencoder.sampling_rate) 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1143Q3_S",t[t.IQ2_S=22]="IQ2_S",t[t.IQ4_XS=23]="IQ4_XS",t[t.I8=24]="I8",t[t.I16=25]="I16",t[t.I32=26]="I32",t[t.I64=27]="I64",t[t.F64=28]="F64",t[t.IQ1_M=29]="IQ1_M",t[t.BF16=30]="BF16",t[t.TQ1_0=34]="TQ1_0",t[t.TQ2_0=35]="TQ2_0",t[t.MXFP4=39]="MXFP4"})(sg||(sg={}));const rC={js:{fetch:{basic:`async function query(data) { 1144 const response = await fetch( 1145 "{{ fullUrl }}", 1146 { 1147 headers: { 1148 Authorization: "{{ authorizationHeader }}", 1149 "Content-Type": "application/json", 1150{% if billTo %} 1151 "X-HF-Bill-To": "{{ billTo }}", 1152{% endif %} }, 1153 method: "POST", 1154 body: JSON.stringify(data), 1155 } 1156 ); 1157 const result = await response.json(); 1158 return result; 1159} 1160 1161query({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => { 1162 console.log(JSON.stringify(response)); 1163});`,basicAudio:`async function query(data) { 1164 const response = await fetch( 1165 "{{ fullUrl }}", 1166 { 1167 headers: { 1168 Authorization: "{{ authorizationHeader }}", 1169 "Content-Type": "audio/flac", 1170{% if billTo %} 1171 "X-HF-Bill-To": "{{ billTo }}", 1172{% endif %} }, 1173 method: "POST", 1174 body: JSON.stringify(data), 1175 } 1176 ); 1177 const result = await response.json(); 1178 return result; 1179} 1180 1181query({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => { 1182 console.log(JSON.stringify(response)); 1183});`,basicImage:`async function query(data) { 1184 const response = await fetch( 1185 "{{ fullUrl }}", 1186 { 1187 headers: { 1188 Authorization: "{{ authorizationHeader }}", 1189 "Content-Type": "image/jpeg", 1190{% if billTo %} 1191 "X-HF-Bill-To": "{{ billTo }}", 1192{% endif %} }, 1193 method: "POST", 1194 body: JSON.stringify(data), 1195 } 1196 ); 1197 const result = await response.json(); 1198 return result; 1199} 1200 1201query({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => { 1202 console.log(JSON.stringify(response)); 1203});`,conversational:`async function query(data) { 1204 const response = await fetch( 1205 "{{ fullUrl }}", 1206 { 1207 headers: { 1208 Authorization: "{{ authorizationHeader }}", 1209 "Content-Type": "application/json", 1210{% if billTo %} 1211 "X-HF-Bill-To": "{{ billTo }}", 1212{% endif %} }, 1213 method: "POST", 1214 body: JSON.stringify(data), 1215 } 1216 ); 1217 const result = await response.json(); 1218 return result; 1219} 1220 1221query({ 1222{{ autoInputs.asTsString }} 1223}).then((response) => { 1224 console.log(JSON.stringify(response)); 1225});`,imageToImage:`const image = fs.readFileSync("{{inputs.asObj.inputs}}"); 1226 1227async function query(data) { 1228 const response = await fetch( 1229 "{{ fullUrl }}", 1230 { 1231 headers: { 1232 Authorization: "{{ authorizationHeader }}", 1233 "Content-Type": "image/jpeg", 1234{% if billTo %} 1235 "X-HF-Bill-To": "{{ billTo }}", 1236{% endif %} }, 1237 method: "POST", 1238 body: { 1239 "inputs": \`data:image/png;base64,\${data.inputs.encode("base64")}\`, 1240 "parameters": data.parameters, 1241 } 1242 } 1243 ); 1244 const result = await response.json(); 1245 return result; 1246} 1247 1248query({ 1249 inputs: image, 1250 parameters: { 1251 prompt: "{{ inputs.asObj.parameters.prompt }}", 1252 } 1253}).then((response) => { 1254 console.log(JSON.stringify(response)); 1255});`,imageToVideo:`const image = fs.readFileSync("{{inputs.asObj.inputs}}"); 1256 1257async function query(data) { 1258 const response = await fetch( 1259 "{{ fullUrl }}", 1260 { 1261 headers: { 1262 Authorization: "{{ authorizationHeader }}", 1263 "Content-Type": "image/jpeg", 1264{% if billTo %} 1265 "X-HF-Bill-To": "{{ billTo }}", 1266{% endif %} }, 1267 method: "POST", 1268 body: { 1269 "image_url": \`data:image/png;base64,\${data.image.encode("base64")}\`, 1270 "prompt": data.prompt, 1271 } 1272 } 1273 ); 1274 const result = await response.json(); 1275 return result; 1276} 1277 1278query({ 1279 "image": image, 1280 "prompt": "{{inputs.asObj.parameters.prompt}}", 1281}).then((response) => { 1282 // Use video 1283});`,textToAudio:`{% if model.library_name == "transformers" %} 1284async function query(data) { 1285 const response = await fetch( 1286 "{{ fullUrl }}", 1287 { 1288 headers: { 1289 Authorization: "{{ authorizationHeader }}", 1290 "Content-Type": "application/json", 1291{% if billTo %} 1292 "X-HF-Bill-To": "{{ billTo }}", 1293{% endif %} }, 1294 method: "POST", 1295 body: JSON.stringify(data), 1296 } 1297 ); 1298 const result = await response.blob(); 1299 return result; 1300} 1301 1302query({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => { 1303 // Returns a byte object of the Audio wavform. Use it directly! 1304}); 1305{% else %} 1306async function query(data) { 1307 const response = await fetch( 1308 "{{ fullUrl }}", 1309 { 1310 headers: { 1311 Authorization: "{{ authorizationHeader }}", 1312 "Content-Type": "application/json", 1313 }, 1314 method: "POST", 1315 body: JSON.stringify(data), 1316 } 1317 ); 1318 const result = await response.json(); 1319 return result; 1320} 1321 1322query({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => { 1323 console.log(JSON.stringify(response)); 1324}); 1325{% endif %} `,textToImage:`async function query(data) { 1326 const response = await fetch( 1327 "{{ fullUrl }}", 1328 { 1329 headers: { 1330 Authorization: "{{ authorizationHeader }}", 1331 "Content-Type": "application/json", 1332{% if billTo %} 1333 "X-HF-Bill-To": "{{ billTo }}", 1334{% endif %} }, 1335 method: "POST", 1336 body: JSON.stringify(data), 1337 } 1338 ); 1339 const result = await response.blob(); 1340 return result; 1341} 1342 1343 1344query({ {{ providerInputs.asTsString }} }).then((response) => { 1345 // Use image 1346});`,textToSpeech:`{% if model.library_name == "transformers" %} 1347async function query(data) { 1348 const response = await fetch( 1349 "{{ fullUrl }}", 1350 { 1351 headers: { 1352 Authorization: "{{ authorizationHeader }}", 1353 "Content-Type": "application/json", 1354{% if billTo %} 1355 "X-HF-Bill-To": "{{ billTo }}", 1356{% endif %} }, 1357 method: "POST", 1358 body: JSON.stringify(data), 1359 } 1360 ); 1361 const result = await response.blob(); 1362 return result; 1363} 1364 1365query({ text: {{ inputs.asObj.inputs }} }).then((response) => { 1366 // Returns a byte object of the Audio wavform. Use it directly! 1367}); 1368{% else %} 1369async function query(data) { 1370 const response = await fetch( 1371 "{{ fullUrl }}", 1372 { 1373 headers: { 1374 Authorization: "{{ authorizationHeader }}", 1375 "Content-Type": "application/json", 1376 }, 1377 method: "POST", 1378 body: JSON.stringify(data), 1379 } 1380 ); 1381 const result = await response.json(); 1382 return result; 1383} 1384 1385query({ text: {{ inputs.asObj.inputs }} }).then((response) => { 1386 console.log(JSON.stringify(response)); 1387}); 1388{% endif %} `,zeroShotClassification:`async function query(data) { 1389 const response = await fetch( 1390 "{{ fullUrl }}", 1391 { 1392 headers: { 1393 Authorization: "{{ authorizationHeader }}", 1394 "Content-Type": "application/json", 1395{% if billTo %} 1396 "X-HF-Bill-To": "{{ billTo }}", 1397{% endif %} }, 1398 method: "POST", 1399 body: JSON.stringify(data), 1400 } 1401 ); 1402 const result = await response.json(); 1403 return result; 1404} 1405 1406query({ 1407 inputs: {{ providerInputs.asObj.inputs }}, 1408 parameters: { candidate_labels: ["refund", "legal", "faq"] } 1409}).then((response) => { 1410 console.log(JSON.stringify(response)); 1411});`},"huggingface.js":{basic:`import { InferenceClient } from "@huggingface/inference"; 1412 1413const client = new InferenceClient("{{ accessToken }}"); 1414 1415const output = await client.{{ methodName }}({ 1416{% if endpointUrl %} 1417 endpointUrl: "{{ endpointUrl }}", 1418{% endif %} 1419 model: "{{ model.id }}", 1420 inputs: {{ inputs.asObj.inputs }}, 1421 provider: "{{ provider }}", 1422}{% if billTo %}, { 1423 billTo: "{{ billTo }}", 1424}{% endif %}); 1425 1426console.log(output);`,basicAudio:`import { InferenceClient } from "@huggingface/inference"; 1427 1428const client = new InferenceClient("{{ accessToken }}"); 1429 1430const data = fs.readFileSync({{inputs.asObj.inputs}}); 1431 1432const output = await client.{{ methodName }}({ 1433{% if endpointUrl %} 1434 endpointUrl: "{{ endpointUrl }}", 1435{% endif %} 1436 data, 1437 model: "{{ model.id }}", 1438 provider: "{{ provider }}", 1439}{% if billTo %}, { 1440 billTo: "{{ billTo }}", 1441}{% endif %}); 1442 1443console.log(output);`,basicImage:`import { InferenceClient } from "@huggingface/inference"; 1444 1445const client = new InferenceClient("{{ accessToken }}"); 1446 1447const data = fs.readFileSync({{inputs.asObj.inputs}}); 1448 1449const output = await client.{{ methodName }}({ 1450{% if endpointUrl %} 1451 endpointUrl: "{{ endpointUrl }}", 1452{% endif %} 1453 data, 1454 model: "{{ model.id }}", 1455 provider: "{{ provider }}", 1456}{% if billTo %}, { 1457 billTo: "{{ billTo }}", 1458}{% endif %}); 1459 1460console.log(output);`,conversational:`import { InferenceClient } from "@huggingface/inference"; 1461 1462const client = new InferenceClient("{{ accessToken }}"); 1463 1464const chatCompletion = await client.chatCompletion({ 1465{% if endpointUrl %} 1466 endpointUrl: "{{ endpointUrl }}", 1467{% endif %} 1468 provider: "{{ provider }}", 1469 model: "{{ model.id }}", 1470{{ inputs.asTsString }} 1471}{% if billTo %}, { 1472 billTo: "{{ billTo }}", 1473}{% endif %}); 1474 1475console.log(chatCompletion.choices[0].message);
1475`,conversationalStream:`import { InferenceClient } from "@huggingface/inference"; 1476 1477const client = new InferenceClient("{{ accessToken }}"); 1478 1479let out = ""; 1480 1481const stream = client.chatCompletionStream({ 1482{% if endpointUrl %} 1483 endpointUrl: "{{ endpointUrl }}", 1484{% endif %} 1485 provider: "{{ provider }}", 1486 model: "{{ model.id }}", 1487{{ inputs.asTsString }} 1488}{% if billTo %}, { 1489 billTo: "{{ billTo }}", 1490}{% endif %}); 1491 1492for await (const chunk of stream) { 1493 if (chunk.choices && chunk.choices.length > 0) { 1494 const newContent = chunk.choices[0].delta.content; 1495 out += newContent; 1496 console.log(newContent); 1497 } 1498}`,imageToImage:`import { InferenceClient } from "@huggingface/inference"; 1499 1500const client = new InferenceClient("{{ accessToken }}"); 1501 1502const data = fs.readFileSync("{{inputs.asObj.inputs}}"); 1503 1504const image = await client.imageToImage({ 1505{% if endpointUrl %} 1506 endpointUrl: "{{ endpointUrl }}", 1507{% endif %} 1508 provider: "{{provider}}", 1509 model: "{{model.id}}", 1510 inputs: data, 1511 parameters: { prompt: "{{inputs.asObj.parameters.prompt}}", }, 1512}{% if billTo %}, { 1513 billTo: "{{ billTo }}", 1514}{% endif %}); 1515/// Use the generated image (it's a Blob) 1516// For example, you can save it to a file or display it in an image element 1517`,imageToVideo:`import { InferenceClient } from "@huggingface/inference"; 1518 1519const client = new InferenceClient("{{ accessToken }}"); 1520 1521const data = fs.readFileSync("{{inputs.asObj.inputs}}"); 1522 1523const video = await client.imageToVideo({ 1524{% if endpointUrl %} 1525 endpointUrl: "{{ endpointUrl }}", 1526{% endif %} 1527 provider: "{{provider}}", 1528 model: "{{model.id}}", 1529 inputs: data, 1530 parameters: { prompt: "{{inputs.asObj.parameters.prompt}}", }, 1531}{% if billTo %}, { 1532 billTo: "{{ billTo }}", 1533}{% endif %}); 1534 1535/// Use the generated video (it's a Blob) 1536// For example, you can save it to a file or display it in a video element 1537`,textToImage:`import { InferenceClient } from "@huggingface/inference"; 1538 1539const client = new InferenceClient("{{ accessToken }}"); 1540 1541const image = await client.textToImage({ 1542{% if endpointUrl %} 1543 endpointUrl: "{{ endpointUrl }}", 1544{% endif %} 1545 provider: "{{ provider }}", 1546 model: "{{ model.id }}", 1547 inputs: {{ inputs.asObj.inputs }}, 1548 parameters: { num_inference_steps: 5 }, 1549}{% if billTo %}, { 1550 billTo: "{{ billTo }}", 1551}{% endif %}); 1552/// Use the generated image (it's a Blob)`,textToSpeech:`import { InferenceClient } from "@huggingface/inference"; 1553 1554const client = new InferenceClient("{{ accessToken }}"); 1555 1556const audio = await client.textToSpeech({ 1557{% if endpointUrl %} 1558 endpointUrl: "{{ endpointUrl }}", 1559{% endif %} 1560 provider: "{{ provider }}", 1561 model: "{{ model.id }}", 1562 inputs: {{ inputs.asObj.inputs }}, 1563}{% if billTo %}, { 1564 billTo: "{{ billTo }}", 1565}{% endif %}); 1566// Use the generated audio (it's a Blob)`,textToVideo:`import { InferenceClient } from "@huggingface/inference"; 1567 1568const client = new InferenceClient("{{ accessToken }}"); 1569 1570const video = await client.textToVideo({ 1571{% if endpointUrl %} 1572 endpointUrl: "{{ endpointUrl }}", 1573{% endif %} 1574 provider: "{{ provider }}", 1575 model: "{{ model.id }}", 1576 inputs: {{ inputs.asObj.inputs }}, 1577}{% if billTo %}, { 1578 billTo: "{{ billTo }}", 1579}{% endif %}); 1580// Use the generated video (it's a Blob)`},openai:{conversational:`import { OpenAI } from "openai"; 1581 1582const client = new OpenAI({ 1583 baseURL: "{{ baseUrl }}", 1584 apiKey: "{{ accessToken }}", 1585{% if billTo %} 1586 defaultHeaders: { 1587 "X-HF-Bill-To": "{{ billTo }}" 1588 } 1589{% endif %} 1590}); 1591 1592const chatCompletion = await client.chat.completions.create({ 1593 model: "{{ providerModelId }}", 1594{{ inputs.asTsString }} 1595}); 1596 1597console.log(chatCompletion.choices[0].message);`,conversationalStream:`import { OpenAI } from "openai"; 1598 1599const client = new OpenAI({ 1600 baseURL: "{{ baseUrl }}", 1601 apiKey: "{{ accessToken }}", 1602{% if billTo %} 1603 defaultHeaders: { 1604 "X-HF-Bill-To": "{{ billTo }}" 1605 } 1606{% endif %} 1607}); 1608 1609const stream = await client.chat.completions.create({ 1610 model: "{{ providerModelId }}", 1611{{ inputs.asTsString }} 1612 stream: true, 1613}); 1614 1615for await (const chunk of stream) { 1616 process.stdout.write(chunk.choices[0]?.delta?.content || ""); 1617}`}},python:{fal_client:{imageToImage:`{%if provider == "fal-ai" %} 1618import fal_client 1619import base64 1620 1621def on_queue_update(update): 1622 if isinstance(update, fal_client.InProgress): 1623 for log in update.logs: 1624 print(log["message"]) 1625 1626with open("{{inputs.asObj.inputs}}", "rb") as image_file: 1627 image_base_64 = base64.b64encode(image_file.read()).decode('utf-8') 1628 1629result = fal_client.subscribe( 1630 "fal-ai/flux-kontext/dev", 1631 arguments={ 1632 "prompt": f"data:image/png;base64,{image_base_64}", 1633 "image_url": "{{ providerInputs.asObj.inputs }}", 1634 }, 1635 with_logs=True, 1636 on_queue_update=on_queue_update, 1637) 1638print(result) 1639{%endif%} 1640`,imageToVideo:`{%if provider == "fal-ai" %} 1641import fal_client 1642import base64 1643 1644def on_queue_update(update): 1645 if isinstance(update, fal_client.InProgress): 1646 for log in update.logs: 1647 print(log["message"]) 1648 1649with open("{{inputs.asObj.inputs}}", "rb") as image_file: 1650 image_base_64 = base64.b64encode(image_file.read()).decode('utf-8') 1651 1652result = fal_client.subscribe( 1653 "{{model.id}}", 1654 arguments={ 1655 "image_url": f"data:image/png;base64,{image_base_64}", 1656 "prompt": "{{inputs.asObj.parameters.prompt}}", 1657 }, 1658 with_logs=True, 1659 on_queue_update=on_queue_update, 1660) 1661print(result) 1662{%endif%} 1663`,textToImage:`{% if provider == "fal-ai" %} 1664import fal_client 1665 1666{% if providerInputs.asObj.loras is defined and providerInputs.asObj.loras != none %} 1667result = fal_client.subscribe( 1668 "{{ providerModelId }}", 1669 arguments={ 1670 "prompt": {{ inputs.asObj.inputs }}, 1671 "loras":{{ providerInputs.asObj.loras | tojson }}, 1672 }, 1673) 1674{% else %} 1675result = fal_client.subscribe( 1676 "{{ providerModelId }}", 1677 arguments={ 1678 "prompt": {{ inputs.asObj.inputs }}, 1679 }, 1680) 1681{% endif %} 1682print(result) 1683{% endif %} `},huggingface_hub:{basic:`result = client.{{ methodName }}( 1684 {{ inputs.asObj.inputs }}, 1685 model="{{ model.id }}", 1686)`,basicAudio:'output = client.{{ methodName }}({{ inputs.asObj.inputs }}, model="{{ model.id }}")',basicImage:'output = client.{{ methodName }}({{ inputs.asObj.inputs }}, model="{{ model.id }}
1686")',conversational:`completion = client.chat.completions.create( 1687 model="{{ model.id }}", 1688{{ inputs.asPythonString }} 1689) 1690 1691print(completion.choices[0].message) `,conversationalStream:`stream = client.chat.completions.create( 1692 model="{{ model.id }}", 1693{{ inputs.asPythonString }} 1694 stream=True, 1695) 1696 1697for chunk in stream: 1698 print(chunk.choices[0].delta.content, end="") `,documentQuestionAnswering:`output = client.document_question_answering( 1699 "{{ inputs.asObj.image }}", 1700 question="{{ inputs.asObj.question }}", 1701 model="{{ model.id }}", 1702) `,imageToImage:`with open("{{ inputs.asObj.inputs }}", "rb") as image_file: 1703 input_image = image_file.read() 1704 1705# output is a PIL.Image object 1706image = client.image_to_image( 1707 input_image, 1708 prompt="{{ inputs.asObj.parameters.prompt }}", 1709 model="{{ model.id }}", 1710) 1711`,imageToVideo:`with open("{{ inputs.asObj.inputs }}", "rb") as image_file: 1712 input_image = image_file.read() 1713 1714video = client.image_to_video( 1715 input_image, 1716 prompt="{{ inputs.asObj.parameters.prompt }}", 1717 model="{{ model.id }}", 1718) 1719`,importInferenceClient:`from huggingface_hub import InferenceClient 1720 1721client = InferenceClient( 1722{% if endpointUrl %} 1723 base_url="{{ baseUrl }}", 1724{% endif %} 1725 provider="{{ provider }}", 1726 api_key="{{ accessToken }}", 1727{% if billTo %} 1728 bill_to="{{ billTo }}", 1729{% endif %} 1730)`,questionAnswering:`answer = client.question_answering( 1731 question="{{ inputs.asObj.question }}", 1732 context="{{ inputs.asObj.context }}", 1733 model="{{ model.id }}", 1734) `,tableQuestionAnswering:`answer = client.table_question_answering( 1735 query="{{ inputs.asObj.query }}", 1736 table={{ inputs.asObj.table }}, 1737 model="{{ model.id }}", 1738) `,textToImage:`# output is a PIL.Image object 1739image = client.text_to_image( 1740 {{ inputs.asObj.inputs }}, 1741 model="{{ model.id }}", 1742) `,textToSpeech:`# audio is returned as bytes 1743audio = client.text_to_speech( 1744 {{ inputs.asObj.inputs }}, 1745 model="{{ model.id }}", 1746) 1747`,textToVideo:`video = client.text_to_video( 1748 {{ inputs.asObj.inputs }}, 1749 model="{{ model.id }}", 1750) `},openai:{conversational:`from openai import OpenAI 1751 1752client = OpenAI( 1753 base_url="{{ baseUrl }}", 1754 api_key="{{ accessToken }}", 1755{% if billTo %} 1756 default_headers={ 1757 "X-HF-Bill-To": "{{ billTo }}" 1758 } 1759{% endif %} 1760) 1761 1762completion = client.chat.completions.create( 1763 model="{{ providerModelId }}", 1764{{ inputs.asPythonString }} 1765) 1766 1767print(completion.choices[0].message) `,conversationalStream:`from openai import OpenAI 1768 1769client = OpenAI( 1770 base_url="{{ baseUrl }}", 1771 api_key="{{ accessToken }}", 1772{% if billTo %} 1773 default_headers={ 1774 "X-HF-Bill-To": "{{ billTo }}" 1775 } 1776{% endif %} 1777) 1778 1779stream = client.chat.completions.create( 1780 model="{{ providerModelId }}", 1781{{ inputs.asPythonString }} 1782 stream=True, 1783) 1784 1785for chunk in stream: 1786 print(chunk.choices[0].delta.content, end="")`},requests:{basic:`def query(payload): 1787 response = requests.post(API_URL, headers=headers, json=payload) 1788 return response.json() 1789 1790output = query({ 1791 "inputs": {{ providerInputs.asObj.inputs }}, 1792}) `,basicAudio:`def query(filename): 1793 with open(filename, "rb") as f: 1794 data = f.read() 1795 response = requests.post(API_URL, headers={"Content-Type": "audio/flac", **headers}, data=data) 1796 return response.json() 1797 1798output = query({{ providerInputs.asObj.inputs }})`,basicImage:`def query(filename): 1799 with open(filename, "rb") as f: 1800 data = f.read() 1801 response = requests.post(API_URL, headers={"Content-Type": "image/jpeg", **headers}, data=data) 1802 return response.json() 1803 1804output = query({{ providerInputs.asObj.inputs }})`,conversational:`def query(payload): 1805 response = requests.post(API_URL, headers=headers, json=payload) 1806 return response.json() 1807 1808response = query({ 1809{{ autoInputs.asJsonString }} 1810}) 1811 1812print(response["choices"][0]["message"])`,conversationalStream:`def query(payload): 1813 response = requests.post(API_URL, headers=headers, json=payload, stream=True) 1814 for line in response.iter_lines(): 1815 if not line.startswith(b"data:"): 1816 continue 1817 if line.strip() == b"data: [DONE]": 1818 return 1819 yield json.loads(line.decode("utf-8").lstrip("data:").rstrip("/n")) 1820 1821chunks = query({ 1822{{ autoInputs.asJsonString }}, 1823 "stream": True, 1824}) 1825 1826for chunk in chunks: 1827 print(chunk["choices"][0]["delta"]["content"], end="")`,documentQuestionAnswering:`def query(payload): 1828 with open(payload["image"], "rb") as f: 1829 img = f.read() 1830 payload["image"] = base64.b64encode(img).decode("utf-8") 1831 response = requests.post(API_URL, headers=headers, json=payload) 1832 return response.json() 1833 1834output = query({ 1835 "inputs": { 1836 "image": "{{ inputs.asObj.image }}", 1837 "question": "{{ inputs.asObj.question }}", 1838 }, 1839}) `,imageToImage:` 1840def query(payload):
1841 with open(payload["inputs"], "rb") as f: 1842 img = f.read() 1843 payload["inputs"] = base64.b64encode(img).decode("utf-8") 1844 response = requests.post(API_URL, headers=headers, json=payload) 1845 return response.content 1846 1847image_bytes = query({ 1848{{ providerInputs.asJsonString }} 1849}) 1850 1851# You can access the image with PIL.Image for example 1852import io 1853from PIL import Image 1854image = Image.open(io.BytesIO(image_bytes)) `,imageToVideo:` 1855def query(payload): 1856 with open(payload["inputs"], "rb") as f: 1857 img = f.read() 1858 payload["inputs"] = base64.b64encode(img).decode("utf-8") 1859 response = requests.post(API_URL, headers=headers, json=payload) 1860 return response.content 1861 1862video_bytes = query({ 1863{{ inputs.asJsonString }} 1864}) 1865`,importRequests:`{% if importBase64 %} 1866import base64 1867{% endif %} 1868{% if importJson %} 1869import json 1870{% endif %} 1871import requests 1872 1873API_URL = "{{ fullUrl }}" 1874headers = { 1875 "Authorization": "{{ authorizationHeader }}", 1876{% if billTo %} 1877 "X-HF-Bill-To": "{{ billTo }}" 1878{% endif %} 1879}`,tabular:`def query(payload): 1880 response = requests.post(API_URL, headers=headers, json=payload) 1881 return response.content 1882 1883response = query({ 1884 "inputs": { 1885 "data": {{ providerInputs.asObj.inputs }} 1886 }, 1887}) `,textToAudio:`{% if model.library_name == "transformers" %} 1888def query(payload): 1889 response = requests.post(API_URL, headers=headers, json=payload) 1890 return response.content 1891 1892audio_bytes = query({ 1893 "inputs": {{ inputs.asObj.inputs }}, 1894}) 1895# You can access the audio with IPython.display for example 1896from IPython.display import Audio 1897Audio(audio_bytes) 1898{% else %} 1899def query(payload): 1900 response = requests.post(API_URL, headers=headers, json=payload) 1901 return response.json() 1902 1903audio, sampling_rate = query({ 1904 "inputs": {{ inputs.asObj.inputs }}, 1905}) 1906# You can access the audio with IPython.display for example 1907from IPython.display import Audio 1908Audio(audio, rate=sampling_rate) 1909{% endif %} `,textToImage:`{% if provider == "hf-inference" %} 1910def query(payload): 1911 response = requests.post(API_URL, headers=headers, json=payload) 1912 return response.content 1913 1914image_bytes = query({ 1915 "inputs": {{ providerInputs.asObj.inputs }}, 1916}) 1917 1918# You can access the image with PIL.Image for example 1919import io 1920from PIL import Image 1921image = Image.open(io.BytesIO(image_bytes)) 1922{% endif %}`,textToSpeech:`{% if model.library_name == "transformers" %} 1923def query(payload): 1924 response = requests.post(API_URL, headers=headers, json=payload) 1925 return response.content 1926 1927audio_bytes = query({ 1928 "text": {{ inputs.asObj.inputs }}, 1929}) 1930# You can access the audio with IPython.display for example 1931from IPython.display import Audio 1932Audio(audio_bytes) 1933{% else %} 1934def query(payload): 1935 response = requests.post(API_URL, headers=headers, json=payload) 1936 return response.json() 1937 1938audio, sampling_rate = query({ 1939 "text": {{ inputs.asObj.inputs }}, 1940}) 1941# You can access the audio with IPython.display for example 1942from IPython.display import Audio 1943Audio(audio, rate=sampling_rate) 1944{% endif %} `,zeroShotClassification:`def query(payload): 1945 response = requests.post(API_URL, headers=headers, json=payload) 1946 return response.json() 1947 1948output = query({ 1949 "inputs": {{ providerInputs.asObj.inputs }}, 1950 "parameters": {"candidate_labels": ["refund", "legal", "faq"]}, 1951}) `,zeroShotImageClassification:`def query(data): 1952 with open(data["image_path"], "rb") as f: 1953 img = f.read() 1954 payload={ 1955 "parameters": data["parameters"], 1956 "inputs": base64.b64encode(img).decode("utf-8") 1957 } 1958 response = requests.post(API_URL, headers=headers, json=payload) 1959 return response.json() 1960 1961output = query({ 1962 "image_path": {{ providerInputs.asObj.inputs }}, 1963 "parameters": {"candidate_labels": ["cat", "dog", "llama"]}, 1964}) `}},sh:{curl:{basic:`curl {{ fullUrl }} \\ 1965 -X POST \\ 1966 -H 'Authorization: {{ authorizationHeader }}' \\ 1967 -H 'Content-Type: application/json' \\ 1968{% if billTo %} 1969 -H 'X-HF-Bill-To: {{ billTo }}' \\ 1970{% endif %} 1971 -d '{ 1972{{ providerInputs.asCurlString }} 1973 }'`,basicAudio:`curl {{ fullUrl }} \\ 1974 -X POST \\ 1975 -H 'Authorization: {{ authorizationHeader }}' \\ 1976 -H 'Content-Type: audio/flac' \\ 1977{% if billTo %} 1978 -H 'X-HF-Bill-To: {{ billTo }}' \\ 1979{% endif %} 1980 --data-binary @{{ providerInputs.asObj.inputs }}`,basicImage:`curl {{ fullUrl }} \\ 1981 -X POST \\ 1982 -H 'Authorization: {{ authorizationHeader }}' \\ 1983 -H 'Content-Type: image/jpeg' \\ 1984{% if billTo %} 1985 -H 'X-HF-Bill-To: {{ billTo }}' \\ 1986{% endif %} 1987 --data-binary @{{ providerInputs.asObj.inputs }}`,conversational:`curl {{ fullUrl }} \\ 1988 -H 'Authorization: {{ authorizationHeader }}' \\ 1989 -H 'Content-Type: application/json' \\ 1990{% if billTo %} 1991 -H 'X-HF-Bill-To: {{ billTo }}' \\ 1992{% endif %} 1993 -d '{ 1994{{ autoInputs.asCurlString }}, 1995 "stream": false 1996 }
1996'`,conversationalStream:`curl {{ fullUrl }} \\ 1997 -H 'Authorization: {{ authorizationHeader }}' \\ 1998 -H 'Content-Type: application/json' \\ 1999{% if billTo %} 2000 -H 'X-HF-Bill-To: {{ billTo }}' \\ 2001{% endif %} 2002 -d '{ 2003{{ autoInputs.asCurlString }}, 2004 "stream": true 2005 }'`,zeroShotClassification:`curl {{ fullUrl }} \\ 2006 -X POST \\ 2007 -d '{"inputs": {{ providerInputs.asObj.inputs }}, "parameters": {"candidate_labels": ["refund", "legal", "faq"]}}' \\ 2008 -H 'Content-Type: application/json' \\ 2009 -H 'Authorization: {{ authorizationHeader }}' 2010{% if billTo %} \\ 2011 -H 'X-HF-Bill-To: {{ billTo }}' 2012{% endif %}`}}},ry=(t,a,r)=>{const l=rC[t]?.[a]?.[r];if(!l)throw new Error(`Template not found: ${t}/${a}/${r}`);return s=>new lk(l).render({...s})};ry("python","huggingface_hub","importInferenceClient");ry("python","requests","importRequests");const oC="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",lC=` 2013You are an assistant that receives a list of ingredients that a user has and suggests a recipe they could make with some or all of those ingredients. You don't need to use every ingredient they mention in your recipe. The recipe can include additional ingredients they didn't mention, but try not to include too many extra ingredients. Format your response in markdown to make it easier to render to a web page 2014`,sC=new pA(oC);async function uC(t){const a=t.join(", ");try{return(await sC.chatCompletion({model:"mistralai/Mixtral-8x7B-Instruct-v0.1",messages:[{role:"system",content:lC},{role:"user",content:`I have ${a}. Please give me a recipe you'd recommend I make!`}],max_tokens:1024})).choices[0].message.content}catch(r){console.error(r.message)}}function cC(){const[t,a]=zi.useState([]),[r,l]=zi.useState(null),s=zi.useRef(null);zi.useEffect(()=>{r&&s.current&&s.current.scrollIntoView({behavior:"smooth"})},[r]);function c(b){const d=b.get("ingredient");d.trim()!==""&&a(y=>[...y,d])}async function p(){l(null);const b=await uC(t);l(b)}function m(){a([]),l(null)}return ve.jsxs("main",{className:"main-main",children:[ve.jsx("h4",{children:"Add at least four ingredients"}),ve.jsxs("form",{action:c,className:"add-ingredient-form",children:[ve.jsx("input",{type:"text",placeholder:"e.g. oregano","aria-label":"Add ingredient",name:"ingredient"}),ve.jsx("button",{children:"Add ingredient"})]}),t.length>0&&ve.jsx(G0,{ref:s,ingredients:t,getRecipe:p}),r&&ve.jsx(J0,{recipe:r,resetApp:m})]})}const fC="/assets/user-DOZ_BC_f.png",pC="/assets/star-filled-B7I4siQ-.png",dC="/assets/star-empty-CosESN_E.png";function mC(t){let a=t.isFilled?"Remove from favorites":"Add to favorites",r=t.isFilled?pC:dC,l=t.isFilled?"filled star icon":"empty star icon";return console.log(a),console.log(l),ve.jsx("button",{onClick:t.handleClick,"aria-pressed":t.isFilled,"aria-label":a,className:"favorite-button",children:ve.jsx("img",{src:r,alt:l,className:"favorite"})})}function hC(){const[t,a]=_v.useState({firstName:"John",lastName:"Doe",phone:"+1 (212) 555-1212",email:"[email protected]",isFavorite:!0});function r(){a(l=>({...l,isFavorite:!l.isFavorite}))}return ve.jsx("main",{className:"main-test",children:ve.jsxs("article",{className:"card",children:[ve.jsx("img",{src:fC,className:"avatar",alt:"User profile picture of John Doe"}),ve.jsx("div",{className:"info",children:ve.jsxs("div",{className:"info-container",children:[ve.jsx(mC,{isFilled:t.isFavorite,handleClick:r}),ve.jsxs("h2",{className:"name",children:[t.firstName," ",t.lastName]}),ve.jsx("p",{className:"contact",children:t.phone}),ve.jsx("p",{className:"contact",children:t.email})]})})]})})}function gC(){return ve.jsxs(ve.Fragment,{children:[ve.jsx(Dv,{}),ve.jsxs("div",{className:"flex-container",children:[ve.jsx(cC,{}),ve.jsx(hC,{})]})]})}Cv.createRoot(document.getElementById("root")).render(ve.jsx(zi.StrictMode,{children:ve.jsx(gC,{})}));
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.