app.py

import streamlit as st
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

# ヘッダー
st.title("Iris Flower Prediction App")

# 説明
st.write("""
This app predicts the **Iris flower** type!
""")

# サイドバーに入力フィールドを作成
st.sidebar.header('User Input Parameters')

def user_input_features():
    sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4)
    sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4)
    petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3)
    petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2)
    data = {'sepal_length': sepal_length,
            'sepal_width': sepal_width,
            'petal_length': petal_length,
            'petal_width': petal_width}
    features = pd.DataFrame(data, index=[0])
    return features

df = user_input_features()

# 入力パラメータの表示
st.subheader('User Input parameters')
st.write(df)

# Irisデータセットの読み込み
iris = load_iris()
X = iris.data
Y = iris.target

# ランダムフォレスト分類器の学習
clf = RandomForestClassifier()
clf.fit(X, Y)

# 予測の表示
prediction = clf.predict(df)
prediction_proba = clf.predict_proba(df)

st.subheader('Class labels and their corresponding index number')
st.write(iris.target_names)

st.subheader('Prediction')
st.write(iris.target_names[prediction])

st.subheader('Prediction Probability')
st.write(prediction_proba)