bug

app.py

@@ -1,6 +1,4 @@
 import gradio as gr
-import aiohttp
-import asyncio
 import requests
 from bs4 import BeautifulSoup
 import pandas as pd
@@ -9,15 +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
-from datetime import datetime, timedelta
-import plotly.graph_objs as go
-import plotly.io as pio
-import yfinance as yf
-import logging
-import tempfile
-import os
+import matplotlib.pyplot as plt
+import io
 import matplotlib as mpl
 import matplotlib.font_manager as fm
+import tempfile
+import os
+import yfinance as yf
+import logging
+from datetime import datetime, timedelta
 
 # 設置日志
 logging.basicConfig(level=logging.INFO,
@@ -49,55 +47,56 @@ headers = {
     'Upgrade-Insecure-Requests': '1'
 }
 
-async def fetch_stock_categories():
+def fetch_stock_categories():
     try:
         url = "https://tw.stock.yahoo.com/class/"
-        async with aiohttp.ClientSession() as session:
-            async with session.get(url, headers=headers) as response:
-                response_text = await response.text()
-                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($c-active-text):h Fw(b):h Td(n)')
-                        
-                        for sub_category in sub_categories:
-                            data.append({
-                                '台股': main_category_name,
-                                '類股': sub_category.text.strip(),
-                                '網址': "https://tw.stock.yahoo.com" + sub_category['href']
-                            })
-                
-                category_dict = {}
-                for item in data:
-                    if item['台股'] not in category_dict:
-                        category_dict[item['台股']] = []
-                    category_dict[item['台股']].append({'類股': item['類股'], '網址': item['網址']})
+        response = requests.get(url, headers=headers, timeout=10)
+        response.raise_for_status()
+        
+        soup = BeautifulSoup(response.text, 'html.parser')
+        main_categories = soup.find_all('div', class_='C($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)')
                 
-                return category_dict
+                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)}")
         return {}
 
-# 股票預測模型類別
+# 股票預測模型類別保持不變...
 class StockPredictor:
     def __init__(self):
         self.model = None
         self.scaler = MinMaxScaler()
         
-    def prepare_data(self, df, selected_features):
-        scaled_data = self.scaler.fit_transform(df[selected_features])
+    def prepare_data(self, df):
+        features = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
+        scaled_data = self.scaler.fit_transform(df[features])
         
         X, y = [], []
         for i in range(len(scaled_data) - 1):
             X.append(scaled_data[i])
-            y.append(scaled_data[i+1])
+            y.append(scaled_data[i+1, [0, 3]])  # Open和Close的索引
         
-        return np.array(X).reshape(-1, 1, len(selected_features)), np.array(y)
+        return np.array(X).reshape(-1, 1, len(features)), np.array(y)
     
     def build_model(self, input_shape):
         model = Sequential([
@@ -105,13 +104,13 @@ class StockPredictor:
             Dropout(0.2),
             LSTM(50, activation='relu'),
             Dropout(0.2),
-            Dense(input_shape[1])
+            Dense(2)
         ])
         model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
         return model
     
-    def train(self, df, selected_features):
-        X, y = self.prepare_data(df, selected_features)
+    def train(self, df):
+        X, y = self.prepare_data(df)
         self.model = self.build_model((1, X.shape[2]))
         history = self.model.fit(
             X, y,
@@ -130,97 +129,139 @@ class StockPredictor:
             next_day = self.model.predict(current_data.reshape(1, 1, -1), verbose=0)
             predictions.append(next_day[0])
             
-            current_data = next_day
+            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)
         
         return np.array(predictions)
 
 # Gradio界面函數
-async def update_stocks(category):
+def update_stocks(category):
     if not category or category not in category_dict:
         return []
     return [item['類股'] for item in category_dict[category]]
 
-async def get_stock_items(url):
+def get_stock_items(url):
     try:
-        async with aiohttp.ClientSession() as session:
-            async with session.get(url, headers=headers) as response:
-                response_text = await response.text()
-                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')
-                    stock_code = item.find('span', class_='Fz(14px) C(#979ba7) Ell')
-                    if stock_name and stock_code:
-                        full_code = stock_code.text.strip()
-                        display_code = full_code.split('.')[0]
-                        display_name = f"{stock_name.text.strip()}{display_code}"
-                        stocks_dict[display_name] = full_code
-                
-                return stocks_dict
+        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')
+            stock_code = item.find('span', class_='Fz(14px) C(#979ba7) Ell')
+            if stock_name and stock_code:
+                full_code = stock_code.text.strip()
+                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)}")
         return {}
 
-async def predict_stock(category, stock, stock_item, period, selected_features):
+def update_category(category):
+    stocks = update_stocks(category)
+    return {
+        stock_dropdown: gr.update(choices=stocks, value=None),
+        stock_item_dropdown: gr.update(choices=[], value=None),
+        stock_plot: gr.update(value=None)
+    }
+
+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)
+        }
+    
+    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 {
+            stock_item_dropdown: gr.update(choices=list(stock_items.keys()), value=None),
+            stock_plot: gr.update(value=None)
+        }
+    return {
+        stock_item_dropdown: gr.update(choices=[], value=None),
+        stock_plot: gr.update(value=None)
+    }
+
+def predict_stock(category, stock, stock_item):
     if not all([category, stock, stock_item]):
-        return gr.update(value=None), "請選擇產業類別、類股和股票"
+        return gr.update(value=None)
     
     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), "無法獲取類股網址"
+            return gr.update(value=None)
         
-        stock_items = await get_stock_items(url)
+        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"
+            return gr.update(value=None)
         
-        # 下載股票數據，根據用戶選擇的時間範圍
-        df = yf.download(stock_code, period=period)
+        # 下載股票數據
+        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, selected_features)
+        predictor.train(df)
         
-        last_data = predictor.scaler.transform(df.iloc[-1:][selected_features])
+        last_data = predictor.scaler.transform(df.iloc[-1:][['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']])
         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]
         
-        # 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}'
-            ))
+        # 繪圖
+        fig, ax = plt.subplots(figsize=(14, 7))
+        colors = ['#FF9999', '#66B2FF']
+        labels = ['預測開盤價', '預測收盤價']
         
-        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'
-        )
+        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')
+        
+        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)
         
-        return gr.update(value=pio.to_html(fig, full_html=False)), "預測成功"
+        plt.tight_layout()
+        return gr.update(value=fig)
         
     except Exception as e:
         logging.error(f"預測過程發生錯誤: {str(e)}")
-        return gr.update(value=None), f"預測過程發生錯誤: {str(e)}"
+        return gr.update(value=None)
 
 # 初始化
 setup_font()
-category_dict = asyncio.run(fetch_stock_categories())
+category_dict = fetch_stock_categories()
 categories = list(category_dict.keys())
 
 # Gradio界面
@@ -243,41 +284,31 @@ 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.HTML(label="股價預測圖")
+        stock_plot = gr.Plot(label="股價預測圖")
     
     # 事件綁定
     category_dropdown.change(
-        update_stocks,
+        update_category,
         inputs=[category_dropdown],
-        outputs=[stock_dropdown]
+        outputs=[stock_dropdown, stock_item_dropdown, stock_plot]
     )
     
     stock_dropdown.change(
-        update_stocks,
-        inputs=[category_dropdown],
-        outputs=[stock_item_dropdown]
+        update_stock,
+        inputs=[category_dropdown, stock_dropdown],
+        outputs=[stock_item_dropdown, stock_plot]
     )
     
     predict_button.click(
         predict_stock,
-        inputs=[category_dropdown, stock_dropdown, stock_item_dropdown, period_dropdown, features_checkbox],
-        outputs=[stock_plot, status_output]
+        inputs=[category_dropdown, stock_dropdown, stock_item_dropdown],
+        outputs=[stock_plot]
     )
 
 # 啟動應用
 if __name__ == "__main__":
     demo.launch(share=False)
+