pyscript_ml/index.html at main · IT-HONGREAT/pyscript_ml · GitHub

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<!DOCTYPE html>

<html>
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-0evHe/X+R7YkIZDRvuzKMRqM+OrBnVFBL6DOitfPri4tjfHxaWutUpFmBp4vmVor" crossorigin="anonymous">
    <link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
    <script defer src="https://pyscript.net/alpha/pyscript.js"></script>
    <py-env>
      - numpy
      - matplotlib
      - scikit-learn
      - pandas
      - panel==0.13.1a2
      - paths:
        - ./test_module.py
    </py-env>
  </head>
  <body>
    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.bundle.min.js" integrity="sha384-pprn3073KE6tl6bjs2QrFaJGz5/SUsLqktiwsUTF55Jfv3qYSDhgCecCxMW52nD2" crossorigin="anonymous"></script>
    <div type="fileinput"></div>
    <py-title> 기본사용방법 결과 </py-title>

    <div id="plot"></div>
    <py-script output="plot">

      import matplotlib.pyplot as plt
      import numpy as np
      import pandas as pd
      from test_module import check
      from sklearn.linear_model import LinearRegression


      print(check(1,100))

      x = np.random.randn(1000)
      y = np.random.randn(1000)

      fig, ax = plt.subplots()
      ax.scatter(x, y)
      fig
    </py-script>

    <py-title> pyscript&ML 비만도 알아보기 </py-title>
    <div id="gridy"></div>

    <div>이곳에 키와 몸무게를 입력해주세요(저장되지 않습니다)</div>


    <div id="height-output"></div>

    <py-script output="gridy">
      from sklearn.linear_model import LogisticRegression
      from sklearn.preprocessing import LabelEncoder, StandardScaler
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.model_selection import train_test_split, cross_val_score
      import pandas as pd


      from js import console

      def info_to_df(height, weight, gender):

        height, weight = height / 100, weight / 100
        check = pd.DataFrame(columns=["Height", "Weight", "gender"])
        data_to_insert = {"Height": height, "Weight": weight, "gender": gender}
        infodf = check.append(data_to_insert, ignore_index=True)

        return infodf


      def make_ml_answer(*args,**kwargs):
        data = pd.DataFrame([['Male',174,96,4],
                            ['Male',189,87,2],
                            ['Female',185,110,4],
                            ['Male',149,61,3],
                            ['Male',189,104,3],
                            ['Male',147,92,5],
                            ['Male',154,111,5],
                            ['Male',174,90,3],
                            ['Female',169,103,4],
                            ['Male',195,81,2],
                            ['Female',159,80,4],
                            ['Female',192,101,3],
                            ['Male',155,51,2],
                            ['Male',191,79,2],
                            ['Female',153,107,5],
                            ['Female',157,110,5],
                            ['Male',140,129,5],
                            ['Male',144,145,5],
                            ['Male',172,139,5],
                            ['Male',157,110,5],
                            ['Female',153,149,5],
                            ['Female',169,97,4],
                            ['Female',195,104,3],
                            ['Male',149,61,3]], columns = ['Gender','Height','Weight','Index'])

        df = data
        le = LabelEncoder()
        df["gender"] = le.fit_transform(df["Gender"])

        df["Height"] = df["Height"] / 100
        df["Weight"] = df["Weight"] / 100


        X = df.drop(["Index", "Gender"], axis="columns")
        y = df["Index"]
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)


        model = KNeighborsClassifier(n_neighbors=5)
        test = model.fit(X_train, y_train)

        string_height = document.getElementById('height').value;
        string_weight = document.getElementById('weight').value;
        string_gender = document.getElementById('gender').value;

        check_height = int(round(float(string_height)))
        check_weight = int(round(float(string_weight)))
        check_gender = int(round(float(string_gender)))

        console.log(check_height);
        console.log(check_weight);
        console.log(check_gender);

        console.log("test",int(check_height));
        height,weight,gender = int(check_height),int(check_weight), int(check_gender)
        print("키몸무게성별 잘나오나??",height,weight,gender)
        print("키몸무게성별 잘나오나??",type(height),type(weight),gender)
        if height:
          print("키 나옴??")
          prediction = test.predict(info_to_df(height, weight, gender))
          print("prediction",prediction)
          prediction_dict = {1: "깡마름", 2: "마름", 3: "보통", 4: "통통", 5: "뚱뚱"}
          prediction_name = prediction_dict[prediction[0]]

          print("결과값:",prediction_name)
          pyscript.write("result",prediction_name)

    </py-script>

    <div class="input-group mb-3">
      <span class="input-group-text">키</span>
      <input id="height" type="text" class="form-control" aria-label="Amount (to the nearest dollar)"/>
      <span class="input-group-text">cm</span>

      <span class="input-group-text">몸무게</span>
      <input id="weight" type="text" class="form-control" aria-label="Amount (to the nearest dollar)"/>
      <span class="input-group-text">kg</span>

      <span class="input-group-text">성별</span>
      <input id="gender" type="text" class="form-control" aria-label="Amount (to the nearest dollar)"/>
      <span class="input-group-text">남자:1 / 여자:2</span>

    </div>

    <button id="make_ml_answer" type="submit" pys-onClick="make_ml_answer" class="btn btn-primary">누르기</button>

    <div class="form-floating">
      <textarea class="form-control" placeholder="Leave a comment here" id="result"></textarea>
      <label for="result">이곳에서 결과가 나옵니다(깡마름/마름/보통/통통/뚱뚱)</label>
    </div>

  </body>
</html>