初始化

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.graph_objects as go
+import random
+from datetime import date, timedelta
+
+# --- 1. 模擬資料生成 ---
+
+def generate_price_data(stock_id: str) -> pd.DataFrame:
+    """生成模擬的每日股價資料 (開、高、低、收)"""
+    today = date.today()
+    dates = [today - timedelta(days=i) for i in range(90)][::-1]
+    prices = []
+    random.seed(hash(stock_id))
+    current_price = random.uniform(50, 800)
+    for d in dates:
+        open_price = current_price * random.uniform(0.98, 1.02)
+        high_price = open_price * random.uniform(1.0, 1.03)
+        low_price = open_price * random.uniform(0.97, 1.0)
+        close_price = random.uniform(low_price, high_price)
+        prices.append({
+            "Date": d, "Open": round(open_price, 2), "High": round(high_price, 2),
+            "Low": round(low_price, 2), "Close": round(close_price, 2),
+            "Volume": random.randint(1_000_000, 50_000_000)
+        })
+        current_price = close_price
+    return pd.DataFrame(prices)
+
+def generate_search_volume_data(stock_id: str) -> pd.DataFrame:
+    """生成模擬的每日搜尋聲量"""
+    today = date.today()
+    dates = [today - timedelta(days=i) for i in range(90)][::-1]
+    volumes = []
+    random.seed(hash(stock_id) + 1)
+    base_volume = random.randint(20, 60)
+    for d in dates:
+        spike = random.uniform(15, 40) if random.random() > 0.92 else 0
+        volume = base_volume + random.uniform(-5, 5) + spike
+        volumes.append({"Date": d, "Search Volume": max(10, round(volume))})
+    return pd.DataFrame(volumes)
+
+def generate_financial_data(stock_id: str) -> pd.DataFrame:
+    """生成模擬的季度財務報表"""
+    random.seed(hash(stock_id))
+    data = {
+        "項目": ["營業收入 (百萬)", "營業毛利 (百萬)", "稅後淨利 (百萬)", "每股盈餘 (EPS)"],
+        "2024 Q3": [random.randint(8000, 12000), random.randint(3000, 5000), random.randint(1500, 3000), round(random.uniform(1, 5), 2)],
+        "2024 Q2": [random.randint(7500, 11000), random.randint(2800, 4800), random.randint(1400, 2800), round(random.uniform(0.8, 4.5), 2)],
+        "2024 Q1": [random.randint(7000, 10000), random.randint(2500, 4500), random.randint(1200, 2500), round(random.uniform(0.7, 4.0), 2)],
+    }
+    return pd.DataFrame(data)
+
+def generate_institutional_trades(stock_id: str) -> pd.DataFrame:
+    """生成模擬的法人買賣資料"""
+    today = date.today()
+    institutions = ["外資", "投信", "自營商"]
+    actions = ["買超", "賣超"]
+    records = []
+    for i in range(15):
+        records.append({
+            "日期": today - timedelta(days=i), "法人機構": random.choice(institutions),
+            "買賣超": random.choice(actions), "張數": random.randint(100, 5000)
+        })
+    return pd.DataFrame(records)
+
+def generate_recent_news(stock_id: str) -> list:
+    """生成模擬的近期新聞，包含標題和內容"""
+    random.seed(hash(stock_id))
+    headlines = [
+        {
+            "headline": f"【產業動態】分析師看好 {stock_id} 第四季度的市場表現",
+            "content": f"根據最新的市場分析報告，由於全球供應鏈問題緩解以及終端需求回溫，分析師普遍預期 {stock_id} 在第四季度的營收將季增15%，達到歷史新高。報告中特別指出，該公司在新興市場的佈局已見成效，有望成為下一波成長的主要動能。"
+        },
+        {
+            "headline": f"【公司新聞】{stock_id} 宣布將於下月發布新一代 AI 晶片",
+            "content": f"{stock_id} 今日向媒體發出邀請函，確認將於下月15日舉行年度產品發表會。市場預期，會中的最大亮點將是代號為 'Phoenix' 的新一代 AI 運算晶片。據傳該晶片的效能較前代提升了70%，並大幅降低功耗，有望鞏固其在雲端運算市場的領導地位。"
+        },
+        {
+            "headline": f"【法人觀點】某外資機構上調 {stock_id} 評級至「強力買入」",
+            "content": f"知名外資機構今日發布研究報告，將 {stock_id} 的股票評級從「中立」一口氣上調至「強力買入」，目標價也提高了30%。報告認為，市場低估了 {stock_id} 在汽車電子領域的潛力，預計相關業務將在未來三年內貢獻超過20%的總營收。"
+        }
+    ]
+    random.shuffle(headlines)
+    return headlines
+
+# --- 2. 主要處理函式 ---
+
+def get_stock_info(stock_id: str):
+    """根據股票代號獲取所有資訊並生成圖表和資料"""
+    if not stock_id:
+        empty_fig = go.Figure()
+        empty_df = pd.DataFrame()
+        # 返回與輸出元件數量相符的空更新
+        return (
+            empty_fig, empty_fig, empty_df, empty_df,
+            gr.update(visible=False), gr.update(value=""),
+            gr.update(visible=False), gr.update(value=""),
+            gr.update(visible=False), gr.update(value="")
+        )
+
+    # 生成資料
+    price_df = generate_price_data(stock_id)
+    search_volume_df = generate_search_volume_data(stock_id)
+    financial_df = generate_financial_data(stock_id)
+    trades_df = generate_institutional_trades(stock_id)
+    news_list = generate_recent_news(stock_id)
+
+    # 創建 K線圖
+    candlestick_fig = go.Figure(data=[go.Candlestick(
+        x=price_df['Date'], open=price_df['Open'], high=price_df['High'],
+        low=price_df['Low'], close=price_df['Close'], name='K線'
+    )])
+    candlestick_fig.update_layout(
+        xaxis_title='日期', yaxis_title='價格', xaxis_rangeslider_visible=False,
+        template="plotly_dark", margin=dict(l=40, r=40, t=40, b=40)
+    )
+
+    # 創建搜尋聲量折線圖
+    search_volume_fig = go.Figure(data=[go.Scatter(
+        x=search_volume_df['Date'], y=search_volume_df['Search Volume'],
+        mode='lines', name='聲量', line=dict(color='cyan')
+    )])
+    search_volume_fig.update_layout(
+        xaxis_title='日期', yaxis_title='相對搜尋熱度', template="plotly_dark",
+        margin=dict(l=40, r=40, t=40, b=40)
+    )
+
+    # 返回所有元件的更新值
+    return (
+        candlestick_fig, search_volume_fig, financial_df, trades_df,
+        gr.update(label=news_list[0]['headline'], visible=True),
+        gr.update(value=news_list[0]['content']),
+        gr.update(label=news_list[1]['headline'], visible=True),
+        gr.update(value=news_list[1]['content']),
+        gr.update(label=news_list[2]['headline'], visible=True),
+        gr.update(value=news_list[2]['content'])
+    )
+
+# --- 3. Gradio 介面 ---
+
+with gr.Blocks(theme=gr.themes.Soft(), title="股票資訊查詢系統") as demo:
+    gr.Markdown("# 📈 股票資訊查詢系統")
+    gr.Markdown("輸入股票代號 (例如: 2330.TW, AAPL)，然後按 Enter 或點擊送出按鈕來查詢相關資訊。")
+
+    with gr.Row():
+        stock_input = gr.Textbox(label="股票代號", placeholder="例如: 2330.TW 或 AAPL", scale=4)
+        submit_btn = gr.Button("送出查詢", scale=1)
+
+    gr.Markdown("---")
+    gr.Markdown("### 📊 股價 K線圖")
+    price_plot = gr.Plot()
+
+    gr.Markdown("### 📈 網路搜尋聲量")
+    search_volume_plot = gr.Plot()
+
+    gr.Markdown("### 📄 財務報表")
+    financial_output = gr.Dataframe(interactive=False, wrap=True)
+
+    gr.Markdown("### 🏢 法人買賣")
+    trades_output = gr.Dataframe(interactive=False, wrap=True)
+
+    gr.Markdown("### 📰 近期新聞")
+    with gr.Column():
+        news_accordion_1 = gr.Accordion("新聞標題 1", visible=False)
+        with news_accordion_1:
+            news_content_1 = gr.Markdown()
+        
+        news_accordion_2 = gr.Accordion("新聞標題 2", visible=False)
+        with news_accordion_2:
+            news_content_2 = gr.Markdown()
+
+        news_accordion_3 = gr.Accordion("新聞標題 3", visible=False)
+        with news_accordion_3:
+            news_content_3 = gr.Markdown()
+            
+    outputs_list = [
+        price_plot, search_volume_plot, financial_output, trades_output,
+        news_accordion_1, news_content_1,
+        news_accordion_2, news_content_2,
+        news_accordion_3, news_content_3
+    ]
+    
+    submit_btn.click(fn=get_stock_info, inputs=stock_input, outputs=outputs_list)
+    stock_input.submit(fn=get_stock_info, inputs=stock_input, outputs=outputs_list)
+
+if __name__ == "__main__":
+    demo.launch()