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import gradio as gr
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as fm
import tempfile
import requests
from prophet import Prophet
from datetime import datetime, timedelta
import logging
# 設置日誌
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s')
# 字體設置
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:
logging.error(f"字體設置失敗: {str(e)}")
# 使用備用字體
mpl.rc('font', family='SimHei')
def predict_stock_price(stock_code, period, prediction_days):
try:
# 下載股票數據
df = yf.download(stock_code, period=period)
if df.empty:
return "無法獲取股票數據", None
# 準備數據
data = df.reset_index()
data = data[['Date', 'Close']]
data.columns = ['ds', 'y']
# 訓練 Prophet 模型
model = Prophet(daily_seasonality=True)
model.fit(data)
# 創建未來日期
future = model.make_future_dataframe(periods=prediction_days)
forecast = model.predict(future)
# 繪製圖表
plt.figure(figsize=(12, 6))
# 繪製實際數據
plt.plot(data['ds'], data['y'],
label='實際收盤價',
color='blue')
# 繪製預測數據
plt.plot(forecast['ds'], forecast['yhat'],
label='預測收盤價',
color='orange',
linestyle='--')
# 添加預測區間
plt.fill_between(forecast['ds'],
forecast['yhat_lower'],
forecast['yhat_upper'],
color='orange',
alpha=0.2)
# 設置圖表格式
plt.title(f'{stock_code} 股價預測', pad=20)
plt.xlabel('日期')
plt.ylabel('股價')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True, linestyle='--', alpha=0.7)
plt.tight_layout()
# 返回預測結果和圖表
return forecast.tail(prediction_days).to_string(), plt.gcf()
except Exception as e:
logging.error(f"預測過程發生錯誤: {str(e)}")
return f"預測過程發生錯誤: {str(e)}", None
# 初始化字體
setup_font()
# Gradio 介面
with gr.Blocks() as demo:
gr.Markdown("# 股票價格 Stock Price 預測系統 Prophet")
with gr.Column():
# 輸入區域
with gr.Row():
stock_input = gr.Textbox(
label="股票代碼",
placeholder="例如: 2330.TW",
value="2330.TW"
)
period_dropdown = gr.Dropdown(
choices=["1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "max"],
label="歷史數據期間",
value="1y"
)
prediction_days = gr.Slider(
minimum=5,
maximum=30,
value=5,
step=1,
label="預測天數"
)
predict_button = gr.Button("開始預測", variant="primary")
# 輸出區域（垂直排列）
output_plot = gr.Plot(label="股價預測圖")
output_text = gr.Textbox(label="預測結果", lines=10)
predict_button.click(
predict_stock_price,
inputs=[stock_input, period_dropdown, prediction_days],
outputs=[output_text, output_plot]
)
# 啟動應用
if __name__ == "__main__":
demo.launch() |