Create app.py

app.py

@@ -0,0 +1,115 @@
+# app.py
+import flet as ft
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+import asyncio
+
+async def main(page: ft.Page):
+    page.title = "C14_1 預測器"
+    page.theme_mode = "light"
+    page.padding = 50
+    page.scroll = "adaptive"
+
+    # 載入數據
+    def load_data():
+        data = pd.DataFrame({
+            'C14_1': np.random.rand(100),
+            'blood': np.random.rand(100),
+            'c7': np.random.rand(100),
+            'c17': np.random.rand(100),
+            'c22': np.random.rand(100),
+            'c17_d': np.random.rand(100),
+            'c22_d': np.random.rand(100),
+            'Skinprick': np.random.rand(100),
+            'MAST': np.random.rand(100),
+            'sex': np.random.randint(0, 2, 100),
+            'c12': np.random.rand(100),
+            'c14': np.random.rand(100),
+            'c18': np.random.rand(100),
+            'c20': np.random.rand(100),
+            'f27_1': np.random.rand(100),
+            'h12': np.random.rand(100)
+        })
+        return data
+
+    data = load_data()
+
+    # 準備特徵和目標變量
+    target_column = 'C14_1'
+    X = data.drop([target_column], axis=1)
+    y = data[target_column]
+
+    # 分割數據
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # 訓練模型
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+
+    # 創建輸入控件
+    input_controls = {}
+    for column in X.columns:
+        input_controls[column] = ft.Slider(
+            min=float(X[column].min()),
+            max=float(X[column].max()),
+            value=float(X[column].mean()),
+            label=f"{column}",
+            divisions=100,
+        )
+
+    # 預測結果文本
+    prediction_text = ft.Text(size=20)
+
+    # 模型性能文本
+    mse_text = ft.Text(size=16)
+    r2_text = ft.Text(size=16)
+
+    # 特徵重要性文本
+    feature_importance_text = ft.Text(size=16)
+
+    def predict(e):
+        input_data = {col: control.value for col, control in input_controls.items()}
+        input_df = pd.DataFrame([input_data])
+        prediction = model.predict(input_df)[0]
+        prediction_text.value = f"預測的 C14_1 值為: {prediction:.2f}"
+        page.update()
+
+    def show_model_performance(e):
+        y_pred = model.predict(X_test)
+        mse = mean_squared_error(y_test, y_pred)
+        r2 = r2_score(y_test, y_pred)
+        mse_text.value = f"均方誤差 (MSE): {mse:.2f}"
+        r2_text.value = f"決定係數 (R²): {r2:.2f}"
+        page.update()
+
+    def show_feature_importance(e):
+        feature_importance = pd.DataFrame({
+            'feature': X.columns,
+            'importance': model.feature_importances_
+        }).sort_values('importance', ascending=False)
+        importance_str = "\n".join([f"{row['feature']}: {row['importance']:.4f}" for _, row in feature_importance.iterrows()])
+        feature_importance_text.value = f"特徵重要性:\n{importance_str}"
+        page.update()
+
+    # 創建頁面佈局
+    await page.add_async(
+        ft.Text("C14_1 預測器", size=30, weight="bold"),
+        ft.Column([control for control in input_controls.values()]),
+        ft.ElevatedButton("預測", on_click=predict),
+        prediction_text,
+        ft.ElevatedButton("顯示模型性能", on_click=show_model_performance),
+        mse_text,
+        r2_text,
+        ft.ElevatedButton("顯示特徵重要性", on_click=show_feature_importance),
+        feature_importance_text,
+    )
+
+# 運行應用
+async def run_app():
+    await ft.app_async(target=main, view=ft.WEB_BROWSER)
+
+# 在標準Python環境中運行
+asyncio.run(run_app())