import{S as ca,i as pa,s as ga,e,k as p,w as z,t as r,M as ua,c as s,d as n,m as g,a as h,x as R,h as o,b as c,N as oa,G as a,g as l,y as V,L as ma,q as W,o as Y,B as U,v as da}from"../../chunks/vendor-hf-doc-builder.js";import{I as Pn}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as la}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as fa}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function ba(Gn){let v,mt,w,j,Q,x,Ot,Z,Bt,dt,I,ft,M,Jt,bt,_,E,tt,S,Ft,nt,Kt,yt,q,Xt,at,zt,Rt,vt,D,Vt,wt,b,C,Wt,et,Yt,Ut,Qt,P,Zt,st,tn,nn,an,G,en,it,sn,rn,_t,O,hn,kt,N,jt,d,Nn,Et,B,on,qt,k,$,rt,A,ln,ht,cn,$t,J,pn,xt,L,It,F,gn,St,f,An,Ct,u,un,ot,mn,dn,lt,fn,bn,ct,yn,vn,H,wn,_n,pt,kn,jn,T,En,qn,Pt,y,$n,gt,xn,In,ut,Sn,Cn,Gt;return x=new Pn({}),I=new fa({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/vi/chapter9/section6.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/vi/chapter9/section6.ipynb"}]}}),S=new Pn({}),N=new la({props:{code:`import random

import gradio as gr


def chat(message, history):
    history = history or []
    if message.startswith("How many"):
        response = random.randint(1, 10)
    elif message.startswith("How"):
        response = random.choice(["Great", "Good", "Okay", "Bad"])
    elif message.startswith("Where"):
        response = random.choice(["Here", "There", "Somewhere"])
    else:
        response = "I don't know"
    history.append((message, response))
    return history, history


iface = gr.Interface(
    chat,
    ["text", "state"],
    ["chatbot", "state"],
    allow_screenshot=False,
    allow_flagging="never",
)
iface.launch()`,highlighted:`<span class="hljs-keyword">import</span> random

<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr


<span class="hljs-keyword">def</span> <span class="hljs-title function_">chat</span>(<span class="hljs-params">message, history</span>):
    history = history <span class="hljs-keyword">or</span> []
    <span class="hljs-keyword">if</span> message.startswith(<span class="hljs-string">&quot;How many&quot;</span>):
        response = random.randint(<span class="hljs-number">1</span>, <span class="hljs-number">10</span>)
    <span class="hljs-keyword">elif</span> message.startswith(<span class="hljs-string">&quot;How&quot;</span>):
        response = random.choice([<span class="hljs-string">&quot;Great&quot;</span>, <span class="hljs-string">&quot;Good&quot;</span>, <span class="hljs-string">&quot;Okay&quot;</span>, <span class="hljs-string">&quot;Bad&quot;</span>])
    <span class="hljs-keyword">elif</span> message.startswith(<span class="hljs-string">&quot;Where&quot;</span>):
        response = random.choice([<span class="hljs-string">&quot;Here&quot;</span>, <span class="hljs-string">&quot;There&quot;</span>, <span class="hljs-string">&quot;Somewhere&quot;</span>])
    <span class="hljs-keyword">else</span>:
        response = <span class="hljs-string">&quot;I don&#x27;t know&quot;</span>
    history.append((message, response))
    <span class="hljs-keyword">return</span> history, history


iface = gr.Interface(
    chat,
    [<span class="hljs-string">&quot;text&quot;</span>, <span class="hljs-string">&quot;state&quot;</span>],
    [<span class="hljs-string">&quot;chatbot&quot;</span>, <span class="hljs-string">&quot;state&quot;</span>],
    allow_screenshot=<span class="hljs-literal">False</span>,
    allow_flagging=<span class="hljs-string">&quot;never&quot;</span>,
)
iface.launch()`}}),A=new Pn({}),L=new la({props:{code:`import requests
import tensorflow as tf

import gradio as gr

inception_net = tf.keras.applications.MobileNetV2()  # t\u1EA3i m\xF4 h\xECnh

# T\u1EA3i nh\xE3n con ng\u01B0\u1EDDi \u0111\u1ECDc \u0111\u01B0\u1EE3c cho ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\\n")


def classify_image(inp):
    inp = inp.reshape((-1, 224, 224, 3))
    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    return {labels[i]: float(prediction[i]) for i in range(1000)}


image = gr.Image(shape=(224, 224))
label = gr.Label(num_top_classes=3)

title = "Gradio Image Classifiction + Interpretation Example"
gr.Interface(
    fn=classify_image, inputs=image, outputs=label, interpretation="default", title=title
).launch()`,highlighted:`<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr

inception_net = tf.keras.applications.MobileNetV2()  <span class="hljs-comment"># t\u1EA3i m\xF4 h\xECnh</span>

<span class="hljs-comment"># T\u1EA3i nh\xE3n con ng\u01B0\u1EDDi \u0111\u1ECDc \u0111\u01B0\u1EE3c cho ImageNet.</span>
response = requests.get(<span class="hljs-string">&quot;https://git.io/JJkYN&quot;</span>)
labels = response.text.split(<span class="hljs-string">&quot;\\n&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">classify_image</span>(<span class="hljs-params">inp</span>):
    inp = inp.reshape((-<span class="hljs-number">1</span>, <span class="hljs-number">224</span>, <span class="hljs-number">224</span>, <span class="hljs-number">3</span>))
    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    <span class="hljs-keyword">return</span> {labels[i]: <span class="hljs-built_in">float</span>(prediction[i]) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>)}


image = gr.Image(shape=(<span class="hljs-number">224</span>, <span class="hljs-number">224</span>))
label = gr.Label(num_top_classes=<span class="hljs-number">3</span>)

title = <span class="hljs-string">&quot;Gradio Image Classifiction + Interpretation Example&quot;</span>
gr.Interface(
    fn=classify_image, inputs=image, outputs=label, interpretation=<span class="hljs-string">&quot;default&quot;</span>, title=title
).launch()`}}),{c(){v=e("meta"),mt=p(),w=e("h1"),j=e("a"),Q=e("span"),z(x.$$.fragment),Ot=p(),Z=e("span"),Bt=r("C\xE1c t\xEDnh n\u0103ng n\xE2ng cao c\u1EE7a Interface"),dt=p(),z(I.$$.fragment),ft=p(),M=e("p"),Jt=r("B\xE2y gi\u1EDD ch\xFAng ta c\xF3 th\u1EC3 x\xE2y d\u1EF1ng v\xE0 chia s\u1EBB giao di\u1EC7n c\u01A1 b\u1EA3n, h\xE3y c\xF9ng kh\xE1m ph\xE1 m\u1ED9t s\u1ED1 t\xEDnh n\u0103ng n\xE2ng cao h\u01A1n nh\u01B0 tr\u1EA1ng th\xE1i v\xE0 di\u1EC5n gi\u1EA3i."),bt=p(),_=e("h3"),E=e("a"),tt=e("span"),z(S.$$.fragment),Ft=p(),nt=e("span"),Kt=r("S\u1EED d\u1EE5ng tr\u1EA1ng th\xE1i \u0111\u1EC3 duy tr\xEC d\u1EEF li\u1EC7u"),yt=p(),q=e("p"),Xt=r("Gradio h\u1ED7 tr\u1EE3 "),at=e("em"),zt=r("tr\u1EA1ng th\xE1i phi\xEAn"),Rt=r(", n\u01A1i d\u1EEF li\u1EC7u t\u1ED3n t\u1EA1i qua nhi\u1EC1u l\u1EA7n g\u1EEDi trong m\u1ED9t t\u1EA3i trang. Tr\u1EA1ng th\xE1i phi\xEAn h\u1EEFu \xEDch khi x\xE2y d\u1EF1ng c\xE1c b\u1EA3n demo, ch\u1EB3ng h\u1EA1n nh\u01B0 chatbot \u1EDF n\u01A1i b\u1EA1n mu\u1ED1n gi\u1EEF nguy\xEAn d\u1EEF li\u1EC7u khi ng\u01B0\u1EDDi d\xF9ng t\u01B0\u01A1ng t\xE1c v\u1EDBi m\xF4 h\xECnh. L\u01B0u \xFD r\u1EB1ng tr\u1EA1ng th\xE1i phi\xEAn kh\xF4ng chia s\u1EBB d\u1EEF li\u1EC7u gi\u1EEFa nh\u1EEFng ng\u01B0\u1EDDi d\xF9ng kh\xE1c nhau trong m\xF4 h\xECnh c\u1EE7a b\u1EA1n."),vt=p(),D=e("p"),Vt=r("\u0110\u1EC3 l\u01B0u tr\u1EEF d\u1EEF li\u1EC7u \u1EDF tr\u1EA1ng th\xE1i phi\xEAn, b\u1EA1n c\u1EA7n th\u1EF1c hi\u1EC7n ba vi\u1EC7c:"),wt=p(),b=e("ol"),C=e("li"),Wt=r("Truy\u1EC1n m\u1ED9t "),et=e("em"),Yt=r("tham s\u1ED1 b\u1ED5 sung"),Ut=r(" v\xE0o h\xE0m c\u1EE7a b\u1EA1n, n\xF3 th\u1EC3 hi\u1EC7n tr\u1EA1ng th\xE1i c\u1EE7a giao di\u1EC7n."),Qt=p(),P=e("li"),Zt=r("Khi k\u1EBFt th\xFAc h\xE0m, tr\u1EA3 v\u1EC1 gi\xE1 tr\u1ECB \u0111\xE3 c\u1EADp nh\u1EADt c\u1EE7a tr\u1EA1ng th\xE1i d\u01B0\u1EDBi d\u1EA1ng "),st=e("em"),tn=r("gi\xE1 tr\u1ECB tr\u1EA3 v\u1EC1 b\u1ED5 sung"),nn=r("."),an=p(),G=e("li"),en=r("Th\xEAm th\xE0nh ph\u1EA7n \u0111\u1EA7u v\xE0o \u2018state\u2019 v\xE0 \u0111\u1EA7u ra \u2018state\u2019 khi t\u1EA1o "),it=e("code"),sn=r("Interface"),rn=r(" c\u1EE7a b\u1EA1n."),_t=p(),O=e("p"),hn=r("Xem v\xED d\u1EE5 v\u1EC1 chatbot b\xEAn d\u01B0\u1EDBi:"),kt=p(),z(N.$$.fragment),jt=p(),d=e("iframe"),Et=p(),B=e("p"),on=r(`L\u01B0u \xFD tr\u1EA1ng th\xE1i c\u1EE7a th\xE0nh ph\u1EA7n \u0111\u1EA7u ra v\u1EABn t\u1ED3n t\u1EA1i qua c\xE1c l\u1EA7n g\u1EEDi.
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