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app.py

@@ -16,7 +16,6 @@ import os
 import yfinance as yf
 import logging
 from datetime import datetime, timedelta
-from prophet import Prophet
 
 # 設置日誌
 logging.basicConfig(level=logging.INFO,
@@ -27,9 +26,11 @@ def setup_font():
     try:
         url_font = "https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_"
         response_font = requests.get(url_font)
+        
         with tempfile.NamedTemporaryFile(delete=False, suffix='.ttf') as tmp_file:
             tmp_file.write(response_font.content)
             tmp_file_path = tmp_file.name
+        
         fm.fontManager.addfont(tmp_file_path)
         mpl.rc('font', family='Taipei Sans TC Beta')
     except Exception as e:
@@ -51,25 +52,30 @@ def fetch_stock_categories():
         url = "https://tw.stock.yahoo.com/class/"
         response = requests.get(url, headers=headers, timeout=10)
         response.raise_for_status()
+        
         soup = BeautifulSoup(response.text, 'html.parser')
         main_categories = soup.find_all('div', class_='C($c-link-text)')
+        
         data = []
         for category in main_categories:
             main_category_name = category.find('h2', class_="Fw(b) Fz(24px) Lh(32px)")
             if main_category_name:
                 main_category_name = main_category_name.text.strip()
                 sub_categories = category.find_all('a', class_='Fz(16px) Lh(1.5) C($c-link-text) C($c-active-text):h Fw(b):h Td(n)')
+                
                 for sub_category in sub_categories:
                     data.append({
                         '台股': main_category_name,
                         '類股': sub_category.text.strip(),
                         '網址': "https://tw.stock.yahoo.com" + sub_category['href']
                     })
+        
         category_dict = {}
         for item in data:
             if item['台股'] not in category_dict:
                 category_dict[item['台股']] = []
             category_dict[item['台股']].append({'類股': item['類股'], '網址': item['網址']})
+        
         return category_dict
     except Exception as e:
         logging.error(f"獲取股票類別失敗: {str(e)}")
@@ -78,16 +84,17 @@ def fetch_stock_categories():
 # 股票預測模型類別
 class StockPredictor:
     def __init__(self):
-        self.lstm_model = None
-        self.prophet_model = None
+        self.model = None
         self.scaler = MinMaxScaler()
-    
+        
     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])
+        
         return np.array(X).reshape(-1, 1, len(selected_features)), np.array(y)
     
     def build_model(self, input_shape):
@@ -103,8 +110,8 @@ class StockPredictor:
     
     def train(self, df, selected_features):
         X, y = self.prepare_data(df, selected_features)
-        self.lstm_model = self.build_model((1, X.shape[2]))
-        history = self.lstm_model.fit(
+        self.model = self.build_model((1, X.shape[2]))
+        history = self.model.fit(
             X, y,
             epochs=50,
             batch_size=32,
@@ -116,18 +123,18 @@ class StockPredictor:
     def predict(self, last_data, n_days):
         predictions = []
         current_data = last_data.copy()
+        
         for _ in range(n_days):
-            next_day = self.lstm_model.predict(current_data.reshape(1, 1, -1), verbose=0)
+            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[:len(next_day[0])] = next_day[0]
             current_data = current_data.reshape(1, -1)
+        
         return np.array(predictions)
-    
-    def train_prophet(self, df_prophet):
-        self.prophet_model = Prophet()
-        self.prophet_model.fit(df_prophet)
 
+# Gradio界面函數
 def update_stocks(category):
     if not category or category not in category_dict:
         return []
@@ -137,8 +144,10 @@ def get_stock_items(url):
     try:
         response = requests.get(url, headers=headers, timeout=10)
         response.raise_for_status()
+        
         soup = BeautifulSoup(response.text, 'html.parser')
         stock_items = soup.find_all('li', class_='List(n)')
+        
         stocks_dict = {}
         for item in stock_items:
             stock_name = item.find('div', class_='Lh(20px) Fw(600) Fz(16px) Ell')
@@ -148,6 +157,7 @@ def get_stock_items(url):
                 display_code = full_code.split('.')[0]
                 display_name = f"{stock_name.text.strip()}{display_code}"
                 stocks_dict[display_name] = full_code
+        
         return stocks_dict
     except Exception as e:
         logging.error(f"獲取股票項目失敗: {str(e)}")
@@ -169,8 +179,10 @@ def update_stock(category, stock):
             stock_plot: gr.update(value=None),
             status_output: gr.update(value="")
         }
+    
     url = next((item['網址'] for item in category_dict.get(category, [])
                 if item['類股'] == stock), None)
+    
     if url:
         stock_items = get_stock_items(url)
         return {
@@ -184,84 +196,65 @@ def update_stock(category, stock):
         status_output: gr.update(value="")
     }
 
-def predict_stock(category, stock, stock_item, period, selected_features, model_type):
+def predict_stock(category, stock, stock_item, period, selected_features):
     if not all([category, stock, stock_item]):
         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 item['類股'] == stock), None)
         if not url:
             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), "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=period)
         if df.empty:
             raise ValueError("無法獲取股票數據")
         
+        # 預測
         predictor = StockPredictor()
+        predictor.train(df, selected_features)
+        
+        last_data = predictor.scaler.transform(df[selected_features].iloc[-1:].values)
+        predictions = predictor.predict(last_data[0], 5)
+        
+        # 反轉預測結果
+        last_original = df[selected_features].iloc[-1].values
+        predictions_original = predictor.scaler.inverse_transform(
+            np.vstack([last_data, predictions])
+        )
+        all_predictions = np.vstack([last_original, predictions_original[1:]])
         
-        if model_type == "LSTM":
-            predictor.train(df, selected_features)
-            last_data = predictor.scaler.transform(df[selected_features].iloc[-1:].values)
-            predictions = predictor.predict(last_data[0], 5)
-            last_original = df[selected_features].iloc[-1].values
-            predictions_original = predictor.scaler.inverse_transform(
-                np.vstack([last_data, predictions])
-            )
-            all_predictions = np.vstack([last_original, predictions_original[1:]])
-            
-        elif model_type == "Prophet":
-            target_feature = selected_features[0]  # 使用第一個選擇的特徵
-            df_prophet = df.reset_index()
-            df_prophet = df_prophet[['Date', target_feature]].rename(
-                columns={'Date': 'ds', target_feature: 'y'})
-            
-            predictor.train_prophet(df_prophet)
-            future_dates = pd.date_range(
-                start=df_prophet['ds'].iloc[-1] + pd.Timedelta(days=1),
-                periods=5,
-                freq='D'
-            )
-            future = pd.DataFrame({'ds': future_dates})
-            forecast = predictor.prophet_model.predict(future)
-            all_predictions = forecast['yhat'].values
-            
         # 創建日期索引
         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 = [f'預測{feature}' for feature in selected_features]
         
-        if model_type == "LSTM":
-            colors = ['#FF9999', '#66B2FF']
-            labels = [f'預測{feature}' for feature in selected_features]
-            for i, (label, color) in enumerate(zip(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')
-        elif model_type == "Prophet":
-            ax.plot(date_labels[1:], all_predictions, label=f'預測{target_feature}',
-                    marker='o', color='#FF9999', linewidth=2)
-            for j, value in enumerate(all_predictions):
-                ax.annotate(f'{value:.2f}', (date_labels[j+1], value),
-                            textcoords="offset points", xytext=(0,10),
-                            ha='center', va='bottom')
+        for i, (label, color) in enumerate(zip(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')
         
         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)
-        plt.tight_layout()
         
+        plt.tight_layout()
         return gr.update(value=fig), "預測成功"
         
     except Exception as e:
@@ -303,33 +296,31 @@ with gr.Blocks() as demo:
                 label="選擇要用於預測的特徵",
                 value=['Open', 'Close']
             )
-            model_type_radio = gr.Radio(
-                choices=["LSTM", "Prophet"],
-                label="選擇模型類型",
-                value="LSTM"
-            )
             predict_button = gr.Button("開始預測", variant="primary")
             status_output = gr.Textbox(label="狀態", interactive=False)
-        with gr.Column():
-            stock_plot = gr.Plot(label="股價預測圖")
-
+    
+    with gr.Row():
+        stock_plot = gr.Plot(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, status_output]
     )
+    
     stock_dropdown.change(
         update_stock,
         inputs=[category_dropdown, stock_dropdown],
         outputs=[stock_item_dropdown, stock_plot, status_output]
     )
+    
     predict_button.click(
         predict_stock,
-        inputs=[category_dropdown, stock_dropdown, stock_item_dropdown, 
-                period_dropdown, features_checkbox, model_type_radio],
+        inputs=[category_dropdown, stock_dropdown, stock_item_dropdown, period_dropdown, features_checkbox],
         outputs=[stock_plot, status_output]
     )
-    
+
+# 啟動應用
 if __name__ == "__main__":
-    demo.launch(share=False)
+    demo.launch(share=False)