import{S as Cl,i as Ll,s as Ml,e as o,k as u,t as s,w as G,M as Rl,c as r,d as a,m as d,a as n,h as l,x as H,b as p,N as Gt,G as t,g as c,y as F,L as Dl,q as B,o as N,B as U,v as Pl}from"../../chunks/vendor-hf-doc-builder.js";import{Y as Ol}from"../../chunks/Youtube-hf-doc-builder.js";import{I as Sl}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Ht}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as Al}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Tl(Cs){let $,at,z,Ft,st,X,lt,k,Bt,be,Ee,Nt,Ut,ke,we,zt,Xt,ot,ne,Wt,rt,q,C,ye,W,Qt,je,Yt,nt,x,Q,Ls,Vt,Y,Ms,it,L,Jt,$e,Kt,Zt,ct,m,ie,qe,ea,ta,aa,ce,xe,sa,la,oa,pe,Se,ra,na,ia,f,Ce,ca,pa,Le,ua,da,Me,fa,ha,Re,ma,ga,De,va,_a,Pe,ba,Ea,Oe,ka,wa,Ae,ya,ja,$a,M,Te,qa,xa,Ie,Sa,Ca,La,R,Ge,Ma,Ra,He,Da,Pa,pt,V,ut,ue,Oa,dt,g,Rs,ft,J,Aa,ht,K,mt,D,Ta,Fe,Ia,Ga,gt,P,Ha,Be,Fa,Ba,vt,S,O,Ne,Z,Na,Ue,Ua,_t,de,za,bt,_,Xa,ze,Wa,Qa,Xe,Ya,Va,ee,Ja,Ka,Et,te,kt,ae,Za,wt,w,es,fe,ts,as,We,ss,ls,yt,b,os,se,rs,ns,le,is,cs,Qe,ps,us,jt,oe,$t,A,ds,Ye,fs,hs,qt,re,xt,v,Ds,St,y,ms,Ve,gs,vs,Je,_s,bs,Ct,j,Es,Ke,ks,ws,Ze,ys,js,Lt,he,$s,Mt;return X=new Al({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter9/section4.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/chapter9/section4.ipynb"}]}}),W=new Sl({}),V=new Ht({props:{code:`title = "Ask Rick a Question"
description = """
The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
"""

article = "Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of."

gr.Interface(
    fn=predict,
    inputs="textbox",
    outputs="text",
    title=title,
    description=description,
    article=article,
    examples=[["What are you doing?"], ["Where should we time travel to?"]],
).launch()`,highlighted:`title = <span class="hljs-string">&quot;Ask Rick a Question&quot;</span>
description = <span class="hljs-string">&quot;&quot;&quot;
The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
&lt;img src=&quot;https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png&quot; width=200px&gt;
&quot;&quot;&quot;</span>

article = <span class="hljs-string">&quot;Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of.&quot;</span>

gr.Interface(
    fn=predict,
    inputs=<span class="hljs-string">&quot;textbox&quot;</span>,
    outputs=<span class="hljs-string">&quot;text&quot;</span>,
    title=title,
    description=description,
    article=article,
    examples=[[<span class="hljs-string">&quot;What are you doing?&quot;</span>], [<span class="hljs-string">&quot;Where should we time travel to?&quot;</span>]],
).launch()`}}),K=new Ht({props:{code:'gr.Interface(classify_image, "image", "label").launch(share=True)',highlighted:'gr.Interface(classify_image, <span class="hljs-string">&quot;image&quot;</span>, <span class="hljs-string">&quot;label&quot;</span>).launch(share=<span class="hljs-literal">True</span>)'}}),Z=new Sl({}),te=new Ol({props:{id:"LS9Y2wDVI0k"}}),oe=new Ht({props:{code:`from pathlib import Path
import torch
import gradio as gr
from torch import nn

LABELS = Path("class_names.txt").read_text().splitlines()

model = nn.Sequential(
    nn.Conv2d(1, 32, 3, padding="same"),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(32, 64, 3, padding="same"),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(64, 128, 3, padding="same"),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Flatten(),
    nn.Linear(1152, 256),
    nn.ReLU(),
    nn.Linear(256, len(LABELS)),
)
state_dict = torch.load("pytorch_model.bin", map_location="cpu")
model.load_state_dict(state_dict, strict=False)
model.eval()


def predict(im):
    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0
    with torch.no_grad():
        out = model(x)
    probabilities = torch.nn.functional.softmax(out[0], dim=0)
    values, indices = torch.topk(probabilities, 5)
    return {LABELS[i]: v.item() for i, v in zip(indices, values)}`,highlighted:`<span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr
<span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn

LABELS = Path(<span class="hljs-string">&quot;class_names.txt&quot;</span>).read_text().splitlines()

model = nn.Sequential(
    nn.Conv2d(<span class="hljs-number">1</span>, <span class="hljs-number">32</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-string">&quot;same&quot;</span>),
    nn.ReLU(),
    nn.MaxPool2d(<span class="hljs-number">2</span>),
    nn.Conv2d(<span class="hljs-number">32</span>, <span class="hljs-number">64</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-string">&quot;same&quot;</span>),
    nn.ReLU(),
    nn.MaxPool2d(<span class="hljs-number">2</span>),
    nn.Conv2d(<span class="hljs-number">64</span>, <span class="hljs-number">128</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-string">&quot;same&quot;</span>),
    nn.ReLU(),
    nn.MaxPool2d(<span class="hljs-number">2</span>),
    nn.Flatten(),
    nn.Linear(<span class="hljs-number">1152</span>, <span class="hljs-number">256</span>),
    nn.ReLU(),
    nn.Linear(<span class="hljs-number">256</span>, <span class="hljs-built_in">len</span>(LABELS)),
)
state_dict = torch.load(<span class="hljs-string">&quot;pytorch_model.bin&quot;</span>, map_location=<span class="hljs-string">&quot;cpu&quot;</span>)
model.load_state_dict(state_dict, strict=<span class="hljs-literal">False</span>)
model.<span class="hljs-built_in">eval</span>()


<span class="hljs-keyword">def</span> <span class="hljs-title function_">predict</span>(<span class="hljs-params">im</span>):
    x = torch.tensor(im, dtype=torch.float32).unsqueeze(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">0</span>) / <span class="hljs-number">255.0</span>
    <span class="hljs-keyword">with</span> torch.no_grad():
        out = model(x)
    probabilities = torch.nn.functional.softmax(out[<span class="hljs-number">0</span>], dim=<span class="hljs-number">0</span>)
    values, indices = torch.topk(probabilities, <span class="hljs-number">5</span>)
    <span class="hljs-keyword">return</span> {LABELS[i]: v.item() <span class="hljs-keyword">for</span> i, v <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(indices, values)}`}}),re=new Ht({props:{code:`interface = gr.Interface(
    predict,
    inputs="sketchpad",
    outputs="label",
    theme="huggingface",
    title="Sketch Recognition",
    description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!",
    article="<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
    live=True,
)
interface.launch(share=True)`,highlighted:`interface = gr.Interface(
    predict,
    inputs=<span class="hljs-string">&quot;sketchpad&quot;</span>,
    outputs=<span class="hljs-string">&quot;label&quot;</span>,
    theme=<span class="hljs-string">&quot;huggingface&quot;</span>,
    title=<span class="hljs-string">&quot;Sketch Recognition&quot;</span>,
    description=<span class="hljs-string">&quot;Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!&quot;</span>,
    article=<span class="hljs-string">&quot;&lt;p style=&#x27;text-align: center&#x27;&gt;Sketch Recognition | Demo Model&lt;/p&gt;&quot;</span>,
    live=<span class="hljs-literal">True</span>,
)
interface.launch(share=<span class="hljs-literal">True</span>)`}}),{c(){$=o("meta"),at=u(),z=o("h1"),Ft=s("\u4E0E\u4ED6\u4EBA\u5206\u4EAB\u6F14\u793A"),st=u(),G(X.$$.fragment),lt=u(),k=o("p"),Bt=s(`\u73B0\u5728\u60A8\u5DF2\u7ECF\u6784\u5EFA\u4E86\u4E00\u4E2A\u6F14\u793A\uFF0C\u60A8\u53EF\u80FD\u5E0C\u671B\u4E0E\u5176\u4ED6\u4EBA\u5206\u4EAB\u5B83\u3002 \u68AF\u5EA6\u6F14\u793A
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