fix

app.py

@@ -7,15 +7,15 @@ from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, Dense, Dropout
 from tensorflow.keras.optimizers import Adam
-import matplotlib.pyplot as plt
-import io
-import matplotlib as mpl
-import matplotlib.font_manager as fm
-import tempfile
-import os
+from datetime import datetime, timedelta
+import plotly.graph_objs as go
+import plotly.io as pio
 import yfinance as yf
 import logging
-from datetime import datetime, timedelta
+import tempfile
+import os
+import matplotlib as mpl
+import matplotlib.font_manager as fm
 
 # 設置日志
 logging.basicConfig(level=logging.INFO,
@@ -81,22 +81,21 @@ def fetch_stock_categories():
         logging.error(f"獲取股票類別失敗: {str(e)}")
         return {}
 
-# 股票預測模型類別保持不變...
+# 股票預測模型類別
 class StockPredictor:
     def __init__(self):
         self.model = None
         self.scaler = MinMaxScaler()
         
-    def prepare_data(self, df):
-        features = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
-        scaled_data = self.scaler.fit_transform(df[features])
+    def prepare_data(self, df, selected_features):
+        scaled_data = self.scaler.fit_transform(df[selected_features])
         
         X, y = [], []
         for i in range(len(scaled_data) - 1):
             X.append(scaled_data[i])
-            y.append(scaled_data[i+1, [0, 3]])  # Open和Close的索引
+            y.append(scaled_data[i+1])
         
-        return np.array(X).reshape(-1, 1, len(features)), np.array(y)
+        return np.array(X).reshape(-1, 1, len(selected_features)), np.array(y)
     
     def build_model(self, input_shape):
         model = Sequential([
@@ -104,13 +103,13 @@ class StockPredictor:
             Dropout(0.2),
             LSTM(50, activation='relu'),
             Dropout(0.2),
-            Dense(2)
+            Dense(input_shape[1])
         ])
         model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
         return model
     
-    def train(self, df):
-        X, y = self.prepare_data(df)
+    def train(self, df, selected_features):
+        X, y = self.prepare_data(df, selected_features)
         self.model = self.build_model((1, X.shape[2]))
         history = self.model.fit(
             X, y,
@@ -129,10 +128,7 @@ class StockPredictor:
             next_day = self.model.predict(current_data.reshape(1, 1, -1), verbose=0)
             predictions.append(next_day[0])
             
-            current_data = current_data.flatten()
-            current_data[0] = next_day[0][0]
-            current_data[3] = next_day[0][1]
-            current_data = current_data.reshape(1, -1)
+            current_data = next_day
         
         return np.array(predictions)
 
@@ -170,14 +166,16 @@ def update_category(category):
     return {
         stock_dropdown: gr.update(choices=stocks, value=None),
         stock_item_dropdown: gr.update(choices=[], value=None),
-        stock_plot: gr.update(value=None)
+        stock_plot: gr.update(value=None),
+        status_output: gr.update(value="")
     }
 
 def update_stock(category, stock):
     if not category or not stock:
         return {
             stock_item_dropdown: gr.update(choices=[], value=None),
-            stock_plot: gr.update(value=None)
+            stock_plot: gr.update(value=None),
+            status_output: gr.update(value="")
         }
     
     url = next((item['網址'] for item in category_dict.get(category, [])
@@ -187,77 +185,69 @@ def update_stock(category, stock):
         stock_items = get_stock_items(url)
         return {
             stock_item_dropdown: gr.update(choices=list(stock_items.keys()), value=None),
-            stock_plot: gr.update(value=None)
+            stock_plot: gr.update(value=None),
+            status_output: gr.update(value="")
         }
     return {
         stock_item_dropdown: gr.update(choices=[], value=None),
-        stock_plot: gr.update(value=None)
+        stock_plot: gr.update(value=None),
+        status_output: gr.update(value="")
     }
 
-def predict_stock(category, stock, stock_item):
+def predict_stock(category, stock, stock_item, selected_features):
     if not all([category, stock, stock_item]):
-        return gr.update(value=None)
+        return gr.update(value=None), "請選擇產業類別、類股和股票"
     
     try:
         url = next((item['網址'] for item in category_dict.get(category, [])
                    if item['類股'] == stock), None)
         if not url:
-            return gr.update(value=None)
+            return gr.update(value=None), "無法獲取類股網址"
         
         stock_items = get_stock_items(url)
         stock_code = stock_items.get(stock_item, "")
         
         if not stock_code:
-            return gr.update(value=None)
+            return gr.update(value=None), "無法獲取股票代碼"
         
-        # 下載股票數據
+        # 下載股票數據，根據用戶選擇的時間範圍
         df = yf.download(stock_code, period="1y")
         if df.empty:
             raise ValueError("無法獲取股票數據")
         
         # 預測
         predictor = StockPredictor()
-        predictor.train(df)
+        predictor.train(df, selected_features)
         
-        last_data = predictor.scaler.transform(df.iloc[-1:][['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']])
+        last_data = predictor.scaler.transform(df.iloc[-1:][selected_features])
         predictions = predictor.predict(last_data[0], 5)
         
-        # 反轉預測結果
-        last_original = df[['Open', 'Close']].iloc[-1].values
-        predictions_original = predictor.scaler.inverse_transform(
-            np.hstack([predictions, np.zeros((predictions.shape[0], 4))])
-        )[:, :2]
-        all_predictions = np.vstack([last_original, predictions_original])
-        
         # 創建日期指標
         dates = [datetime.now() + timedelta(days=i) for i in range(6)]
         date_labels = [d.strftime('%m/%d') for d in dates]
         
-        # 繪圖
-        fig, ax = plt.subplots(figsize=(14, 7))
-        colors = ['#FF9999', '#66B2FF']
-        labels = ['預測開盤價', '預測收盤價']
-        
-        for i, (col, label, color) in enumerate(zip(['Open', 'Close'], labels, colors)):
-            ax.plot(date_labels, all_predictions[:, i], label=label,
-                   marker='o', color=color, linewidth=2)
-            for j, value in enumerate(all_predictions[:, i]):
-                ax.annotate(f'{value:.2f}', (date_labels[j], value),
-                           textcoords="offset points", xytext=(0,10),
-                           ha='center', va='bottom')
+        # 用 Plotly 繪圖
+        fig = go.Figure()
+        for i, feature in enumerate(selected_features):
+            fig.add_trace(go.Scatter(
+                x=date_labels,
+                y=np.hstack([df[feature].iloc[-1], predictions[:, i]]),
+                mode='lines+markers',
+                name=f'預測{feature}'
+            ))
         
-        ax.set_title(f'{stock_item} 股價預測 (未來5天)', pad=20, fontsize=14)
-        ax.set_xlabel('日期', labelpad=10)
-        ax.set_ylabel('股價', labelpad=10)
-        ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
-        ax.grid(True, linestyle='--', alpha=0.7)
+        fig.update_layout(
+            title=f'{stock_item} 股價預測 (未來5天)',
+            xaxis_title='日期',
+            yaxis_title='股價',
+            template='plotly_dark'
+        )
         
-        plt.tight_layout()
-        return gr.update(value=fig)
+        return gr.update(value=pio.to_html(fig, full_html=False)), "預測成功"
         
     except Exception as e:
         logging.error(f"預測過程發生錯誤: {str(e)}")
-        return gr.update(value=None)
+        return gr.update(value=None), f"預測過程發生錯誤: {str(e)}"
 
 # 初始化
 setup_font()
@@ -284,28 +274,39 @@ with gr.Blocks() as demo:
                 label="股票",
                 value=None
             )
+            period_dropdown = gr.Dropdown(
+                choices=["1y", "6mo", "3mo", "1mo"],
+                label="抓取時間範圍",
+                value="1y"
+            )
+            features_checkbox = gr.CheckboxGroup(
+                choices=['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'],
+                label="選擇要用於預測的特徵",
+                value=['Open', 'Close']
+            )
             predict_button = gr.Button("開始預測", variant="primary")
+            status_output = gr.Textbox(label="狀態", interactive=False)
     
     with gr.Row():
-        stock_plot = gr.Plot(label="股價預測圖")
+        stock_plot = gr.HTML(label="股價預測圖")
     
     # 事件綁定
     category_dropdown.change(
         update_category,
         inputs=[category_dropdown],
-        outputs=[stock_dropdown, stock_item_dropdown, stock_plot]
+        outputs=[stock_dropdown, stock_item_dropdown, stock_plot, status_output]
     )
     
     stock_dropdown.change(
         update_stock,
         inputs=[category_dropdown, stock_dropdown],
-        outputs=[stock_item_dropdown, stock_plot]
+        outputs=[stock_item_dropdown, stock_plot, status_output]
     )
     
     predict_button.click(
         predict_stock,
-        inputs=[category_dropdown, stock_dropdown, stock_item_dropdown],
-        outputs=[stock_plot]
+        inputs=[category_dropdown, stock_dropdown, stock_item_dropdown, features_checkbox],
+        outputs=[stock_plot, status_output]
     )
 
 # 啟動應用