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README.md

@@ -1,5 +1,5 @@
 ---
-title: SEC Financial Data Query Assistant
+title: Financial & Market AI Assistant
 emoji: 📊
 colorFrom: blue
 colorTo: green
@@ -10,19 +10,19 @@ pinned: false
 license: mit
 ---
 
-# SEC Financial Data Query Assistant
+# Financial & Market AI Assistant
 
-A Gradio-based AI-powered web application for querying SEC financial data through MCP Server.
+A streamlined Gradio-based AI assistant that integrates **SEC financial data** and **real-time market information** through two MCP servers.
 
 ## ✨ Features
 
-- 🤖 **Intelligent AI Assistant**: Chat naturally with **Qwen/Qwen2.5-72B-Instruct** model (supports tool calling)
-- 🛠️ **Automatic Tool Calling**: AI automatically selects and calls MCP tools based on your questions
-- 🔍 Search companies by name or ticker symbol
-- 📈 View latest financial data
-- 📊 Analyze 3-year and 5-year financial trends
-- 💰 Display revenue, net income, EPS, operating expenses, and cash flow metrics
-- 📝 List company SEC filings
+- 🤖 **Intelligent AI Assistant**: Powered by **Qwen/Qwen2.5-72B-Instruct:novita** (supports tool calling)
+- 📊 **Dual Data Sources**:
+  - **SEC Financial Reports**: Official 10-K/10-Q data (revenue, earnings, cash flow, etc.)
+  - **Market & Stock Data**: Real-time stock quotes and news (powered by Finnhub)
+- 🛠️ **Automatic MCP Tool Calling**: AI automatically uses 6 tools across 2 MCP servers
+- 💬 **Natural Language Interface**: Just ask questions naturally
+- 🔍 **Smart Analysis**: AI provides data-driven insights, not just raw data
 
 ## 🚀 Quick Start
 
@@ -43,69 +43,54 @@ A Gradio-based AI-powered web application for querying SEC financial data throug
 
 ### Usage
 
-No additional configuration needed! Just start asking questions:
+Just start asking questions! The AI will automatically fetch data from the right source:
 
-### AI Assistant Tab
+**Financial Questions:**
+- "What's Apple's latest revenue and profit?"
+- "Show me NVIDIA's 3-year financial trends"
+- "Compare Microsoft's latest earnings with its operating expenses"
 
-Ask natural language questions:
-- "Show me Apple's latest financial data"
-- "What's NVIDIA's 3-year trend?"
-- "Compare Tesla's revenue with expenses"
-- "How is Microsoft performing?"
-- "Give me Alibaba's financial overview"
+**Market Questions:**
+- "How is Tesla's stock performing today?"
+- "Get the latest market news about crypto"
+- "What's the current price of AAPL?"
 
-The AI will automatically:
-1. 🧠 Understand your question
-2. 🔧 Call the appropriate MCP tools
-3. 📊 Analyze the data
-4. 💬 Provide a comprehensive answer
+**Combined Analysis:**
+- "Compare Microsoft's latest earnings with its current stock price"
+- "Show me Amazon's financial performance and recent news"
 
-### Direct Query Tab
+## 🛠️ Available MCP Tools
 
-For structured queries:
-- Enter company name or ticker symbol (e.g., NVIDIA, AAPL, Microsoft)
-- Select query type:
-  - **Latest Financial Data**: Most recent fiscal year data
-  - **3-Year Trends**: Financial trends over 3 years
-  - **5-Year Trends**: Financial trends over 5 years
-  - **Company Filings**: List of SEC filings
+**SEC Financial Reports MCP:**
+1. `advanced_search_company` - Find US companies by name/ticker
+2. `get_latest_financial_data` - Get latest 10-K/10-Q data
+3. `extract_financial_metrics` - Get 3-year or 5-year trends
 
-## 📊 Example Questions
+**Market & Stock Data MCP (Finnhub):**
+4. `get_quote` - Real-time stock price, volume, change
+5. `get_market_news` - Latest market news (general/forex/crypto/merger)
+6. `get_company_news` - Company-specific news with date range
 
-**General inquiries:**
-- "What can you tell me about Apple?"
-- "How is Tesla doing financially?"
+## 💾 Data Sources
 
-**Specific data:**
-- "Show me NVIDIA's revenue for the last 3 years"
-- "What's Microsoft's latest EPS?"
-
-**Comparisons:**
-- "Compare Amazon's revenue and expenses"
-- "How does Google's cash flow look?"
-
-**Trends:**
-- "Give me a 5-year financial overview of Alibaba"
-- "Show me Meta's financial trends"
-
-## 💾 Data Source
-
-SEC EDGAR data via MCP Server: https://huggingface.co/spaces/JC321/EasyReportDateMCP
+- **SEC Financial Data**: https://huggingface.co/spaces/JC321/EasyReportDateMCP
+- **Market & Stock Data**: https://huggingface.co/spaces/JC321/MarketandStockMCP (Finnhub API)
 
 ## 🛠️ Technology Stack
 
-- **Frontend**: Gradio 6.0.1
-- **Backend**: Python with requests
-- **AI Model**: Qwen/Qwen2.5-72B-Instruct:novita (via Hugging Face Inference API)
-- **MCP Protocol**: FastMCP with HTTP transport (stateless)
-- **Data Source**: SEC EDGAR via MCP Server
+- **Frontend**: Gradio 6.0.1 (ChatInterface)
+- **AI Model**: Qwen/Qwen2.5-72B-Instruct:novita (Hugging Face Inference API)
+- **MCP Protocol**: 2 MCP Servers (HTTP + SSE transports)
+- **Data Sources**: 
+  - SEC EDGAR (official financial filings)
+  - Finnhub API (real-time market data)
 
 ## 💡 Tips
 
-- The AI understands context, so you can ask follow-up questions
-- You can ask about multiple companies in one conversation
-- Both company names and ticker symbols work (e.g., "Apple" or "AAPL")
-- The AI will show which tools it used to answer your question
+- Ask about **financials** (revenue, profit) or **market data** (stock price, news)
+- AI understands context and can combine data from both sources
+- Both company names and ticker symbols work ("Apple" or "AAPL")
+- The AI shows which tools it used for transparency
 
 ## 👍 Supported Companies
 

app.py

@@ -2,73 +2,32 @@ import gradio as gr
 import requests
 import json
 import os
-import time
-from requests.adapters import HTTPAdapter
-from urllib3.util.retry import Retry
 from huggingface_hub import InferenceClient
 
-MCP_SPACE = "JC321/EasyReportDateMCP"
-MCP_URL = "https://jc321-easyreportdatemcp.hf.space"
-MCP_ENDPOINT = "/mcp"  # MCP 工具调用端点
-
-# 设置请求头
-HEADERS = {
-    "Content-Type": "application/json",
-    "User-Agent": "SEC-Query-Assistant/1.0 (jtyxabc@gmail.com)"
+# ========== 配置两个 MCP 服务 ==========
+MCP_SERVICES = {
+    "financial": {
+        "name": "SEC Financial Reports",
+        "url": "https://jc321-easyreportdatemcp.hf.space/mcp"
+    },
+    "market": {
+        "name": "Market & Stock Data (Finnhub)",
+        "url": "https://jc321-marketandstockmcp.hf.space/gradio_api/mcp/sse"
+    }
 }
 
-# 创建带重试的 requests session
-def create_session_with_retry():
-    """创建带重试机制的 requests session"""
-    session = requests.Session()
-    retry = Retry(
-        total=3,  # 最多重试3次
-        backoff_factor=1,  # 重试间隔：1秒, 2秒, 4秒
-        status_forcelist=[500, 502, 503, 504],  # 这些状态码会触发重试
-    )
-    adapter = HTTPAdapter(max_retries=retry)
-    session.mount('http://', adapter)
-    session.mount('https://', adapter)
-    return session
-
-# 创建全局 session
-session = create_session_with_retry()
-
-# 初始化 Hugging Face Inference Client
-# 使用 Qwen/Qwen2.5-72B-Instruct 模型（支持 tool calling）
-try:
-    # Hugging Face Space 会自动提供 HF_TOKEN 环境变量
-    # 也支持手动在 Settings > Secrets 中配置 HF_TOKEN
-    hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
-    
-    if hf_token:
-        client = InferenceClient(api_key=hf_token)
-        print(f"✅ Hugging Face client initialized with Qwen/Qwen2.5-72B-Instruct:novita model")
-        print(f"   Using authenticated access (HF_TOKEN detected)")
-    else:
-        # 如果没有 token，使用无认证模式（免费层，有速率限制）
-        client = InferenceClient()
-        print("⚠️ Using Hugging Face Inference API without authentication (rate limited)")
-        print("💡 To remove rate limits, add HF_TOKEN in Space Settings > Repository secrets")
-        print("   Get your token from: https://huggingface.co/settings/tokens")
-except Exception as e:
-    print(f"❌ Warning: Failed to initialize Hugging Face client: {e}")
-    client = None
-
-# 定义可用的 MCP 工具
+# MCP 工具定义（两个服务的工具合并）
 MCP_TOOLS = [
+    # Financial Reports Tools
     {
         "type": "function",
         "function": {
             "name": "advanced_search_company",
-            "description": "Search for a US listed company by name or stock ticker symbol to get basic company information including CIK, name, and ticker",
+            "description": "Search for US listed companies by name or ticker to get CIK and basic info",
             "parameters": {
                 "type": "object",
                 "properties": {
-                    "company_input": {
-                        "type": "string",
-                        "description": "Company name or stock ticker symbol (e.g., 'Apple', 'AAPL', 'Microsoft', 'TSLA')"
-                    }
+                    "company_input": {"type": "string", "description": "Company name or ticker (e.g., 'Apple', 'AAPL')"}
                 },
                 "required": ["company_input"]
             }
@@ -78,14 +37,11 @@ MCP_TOOLS = [
         "type": "function",
         "function": {
             "name": "get_latest_financial_data",
-            "description": "Get the latest financial data for a company using its CIK number. Returns revenue, net income, EPS, operating expenses, and cash flow for the most recent fiscal period",
+            "description": "Get latest SEC financial data (revenue, net income, EPS, cash flow, etc.)",
             "parameters": {
                 "type": "object",
                 "properties": {
-                    "cik": {
-                        "type": "string",
-                        "description": "10-digit CIK number of the company (must be obtained from advanced_search_company first)"
-                    }
+                    "cik": {"type": "string", "description": "10-digit CIK number"}
                 },
                 "required": ["cik"]
             }
@@ -95,681 +51,202 @@ MCP_TOOLS = [
         "type": "function",
         "function": {
             "name": "extract_financial_metrics",
-            "description": "Extract financial metrics trends over multiple years for a company. Returns historical data including revenue, net income, EPS, operating expenses, and cash flow",
+            "description": "Get multi-year financial trends (3 or 5 years)",
             "parameters": {
                 "type": "object",
                 "properties": {
-                    "cik": {
-                        "type": "string",
-                        "description": "10-digit CIK number of the company"
-                    },
-                    "years": {
-                        "type": "integer",
-                        "description": "Number of years to retrieve (typically 3 or 5)",
-                        "enum": [3, 5]
-                    }
+                    "cik": {"type": "string", "description": "10-digit CIK number"},
+                    "years": {"type": "integer", "enum": [3, 5]}
                 },
                 "required": ["cik", "years"]
             }
         }
-    }
-]
-
-# 格式化数值显示
-def format_value(value, value_type="money"):
-    """
-    格式化数值：0或极小值显示为N/A，其他显示为带单位的格式
-    value_type: "money" (金额), "eps" (每股收益), "number" (普通数字)
-    """
-    # 检查 None 或极小值（阈值设为0.01，即10M，低于此值视为无意义数据）
-    if value is None or abs(value) < 0.01:
-        return "N/A"
-    
-    if value_type == "money":
-        return f"${value:.2f}B"
-    elif value_type == "eps":
-        return f"${value:.2f}"
-    else:  # number
-        return f"{value:.2f}"
-
-def call_mcp_tool(tool_name, arguments):
-    """调用 MCP 工具并返回结果"""
-    try:
-        # 构建完整的 URL
-        full_url = f"{MCP_URL}{MCP_ENDPOINT}"
-        
-        # FastMCP HTTP Server 使用 /mcp 端点
-        response = session.post(
-            full_url,
-            json={
-                "jsonrpc": "2.0",
-                "method": "tools/call",
-                "params": {
-                    "name": tool_name,
-                    "arguments": arguments
+    },
+    # Market & Stock Tools (Finnhub API)
+    {
+        "type": "function",
+        "function": {
+            "name": "get_quote",
+            "description": "Get real-time stock quote (price, volume, change, etc.) for a ticker symbol",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "symbol": {"type": "string", "description": "Stock ticker symbol (e.g., 'AAPL')"}
                 },
-                "id": 1
-            },
-            headers=HEADERS,
-            timeout=60
-        )
-        
-        # 调试信息
-        print(f"DEBUG: Calling {full_url}")
-        print(f"DEBUG: Tool: {tool_name}, Args: {arguments}")
-        print(f"DEBUG: Status Code: {response.status_code}")
-        print(f"DEBUG: Response: {response.text[:500]}")
-        
-        if response.status_code != 200:
-            return {
-                "error": f"HTTP {response.status_code}",
-                "detail": response.text,
-                "url": full_url
+                "required": ["symbol"]
             }
-        
-        return response.json()
-    except Exception as e:
-        return {
-            "error": str(e),
-            "url": full_url if 'full_url' in locals() else MCP_URL
         }
-
-def normalize_cik(cik):
-    """
-    格式化 CIK 为标准的 10 位格式
-    """
-    if not cik:
-        return None
-    # 转换为字符串并移除非数字字符
-    cik_str = str(cik).replace('-', '').replace(' ', '')
-    # 仅保留数字
-    cik_str = ''.join(c for c in cik_str if c.isdigit())
-    # 填充前导 0 至 10 位
-    return cik_str.zfill(10) if cik_str else None
-
-def parse_mcp_response(response_data):
-    """
-    解析 MCP 协议响应数据
-    支持格式:
-    1. {"result": {"content": [{"type": "text", "text": "{...}"}]}}
-    2. {"content": [{"type": "text", "text": "{...}"}]}
-    3. 直接的 JSON 数据
-    """
-    if not isinstance(response_data, dict):
-        return response_data
-    
-    # 格式 1: {"result": {"content": [...]}}
-    if "result" in response_data and "content" in response_data["result"]:
-        content = response_data["result"]["content"]
-        if content and len(content) > 0:
-            text_content = content[0].get("text", "{}")
-            # 直接解析 JSON（MCP Server 已移除 emoji 前缀）
-            try:
-                return json.loads(text_content)
-            except json.JSONDecodeError:
-                return text_content
-        return {}
-    
-    # 格式 2: {"content": [...]}
-    elif "content" in response_data:
-        content = response_data.get("content", [])
-        if content and len(content) > 0:
-            text_content = content[0].get("text", "{}")
-            # 直接解析 JSON
-            try:
-                return json.loads(text_content)
-            except json.JSONDecodeError:
-                return text_content
-        return {}
-    
-    # 格式 3: 直接返回
-    return response_data
-
-# MCP 工具定义
-def create_mcp_tools():
-    """创建 MCP 工具列表"""
-    return [
-        {
-            "name": "query_financial_data",
-            "description": "Query SEC financial data for US listed companies",
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_market_news",
+            "description": "Get latest market news by category (general, forex, crypto, merger)",
             "parameters": {
                 "type": "object",
                 "properties": {
-                    "company_name": {
-                        "type": "string",
-                        "description": "Company name or stock symbol (e.g., Apple, NVIDIA, AAPL)"
-                    },
-                    "query_type": {
-                        "type": "string",
-                        "enum": ["Latest Financial Data", "3-Year Trends", "5-Year Trends"],
-                        "description": "Type of financial query"
-                    }
+                    "category": {"type": "string", "enum": ["general", "forex", "crypto", "merger"], "description": "News category"},
+                    "min_id": {"type": "integer", "description": "Minimum news ID (optional)"}
                 },
-                "required": ["company_name", "query_type"]
+                "required": ["category"]
             }
         }
-    ]
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_company_news",
+            "description": "Get company-specific news for a stock symbol within a date range",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "symbol": {"type": "string", "description": "Stock ticker symbol (e.g., 'AAPL')"},
+                    "from_date": {"type": "string", "description": "Start date (YYYY-MM-DD, default: 7 days ago)"},
+                    "to_date": {"type": "string", "description": "End date (YYYY-MM-DD, default: today)"}
+                },
+                "required": ["symbol"]
+            }
+        }
+    }
+]
 
-# 工具执行函数
-def execute_tool(tool_name, **kwargs):
-    """执行 MCP 工具"""
-    if tool_name == "query_financial_data":
-        return query_financial_data(kwargs.get("company_name"), kwargs.get("query_type"))
-    return f"Unknown tool: {tool_name}"
-# 创建超链接
-def create_source_link(source_form, source_url=None):
-    """为Source Form创建超链接，使用MCP后端返回的URL"""
-    if not source_form or source_form == 'N/A':
-        return source_form
-    
-    # 如果后端提供了URL，使用后端的URL
-    if source_url and source_url != 'N/A':
-        return f"[{source_form}]({source_url})"
-    
-    # 如果没有URL，只显示文本
-    return source_form
+# 工具路由：工具名 -> MCP 服务 URL
+TOOL_ROUTING = {
+    "advanced_search_company": MCP_SERVICES["financial"]["url"],
+    "get_latest_financial_data": MCP_SERVICES["financial"]["url"],
+    "extract_financial_metrics": MCP_SERVICES["financial"]["url"],
+    "get_quote": MCP_SERVICES["market"]["url"],
+    "get_market_news": MCP_SERVICES["market"]["url"],
+    "get_company_news": MCP_SERVICES["market"]["url"],
+}
 
-def query_financial_data(company_name, query_type):
-    """查询财务数据的主函数"""
-    
-    if not company_name:
-        return "Please enter a company name or stock symbol"
-    
-    # 翻译英文查询类型为中文（用于后端处理）
-    query_type_mapping = {
-        "Latest Financial Data": "最新财务数据",
-        "3-Year Trends": "3年趋势",
-        "5-Year Trends": "5年趋势",
-        "Company Filings": "公司报表列表"
-    }
-    internal_query_type = query_type_mapping.get(query_type, query_type)
-    
-    try:
-        # 使用 MCP 协议调用工具
-        # 先搜索公司（使用 advanced_search_company）
-        try:
-            search_result = call_mcp_tool("advanced_search_company", {"company_input": company_name})
-        except requests.exceptions.Timeout:
-            return f"❌ MCP Server Timeout: The server took too long to respond (>60s).\n\n**Possible reasons**:\n1. MCP Server is cold starting (first request after idle)\n2. Server is overloaded\n3. Network issues\n\n**Suggestion**: Please try again in a few moments. If the problem persists, the MCP Server at {MCP_URL} may be down."
-        
-        # 检查是否有错误
-        if "error" in search_result:
-            return f"❌ Server Error: {search_result.get('error')}\n\nResponse: {search_result.get('detail', 'N/A')}\n\nURL: {search_result.get('url', MCP_URL)}"
-        
-        # 解析搜索结果
-        company = parse_mcp_response(search_result)
-        
-        if isinstance(company, dict) and company.get("error"):
-            return f"❌ Error: {company['error']}"
-        
-        # advanced_search 返回的字段: cik, name, ticker
-        # 注意: 不是 tickers 和 sic_description
-        company_name = company.get('name', 'Unknown')
-        ticker = company.get('ticker', 'N/A')
-        
-        result = f"# {company_name}\n\n"
-        result += f"**Stock Symbol**: {ticker}\n"
-        # sic_description 需要后续通过 get_company_info 获取，这里暂时不显示
-        result += "\n---\n\n"
-        
-        # 获取并格式化 CIK 为 10 位标准格式
-        cik = normalize_cik(company.get('cik'))
-        if not cik:
-            return result + f"❌ Error: Invalid CIK from company search\n\nDebug: company data = {json.dumps(company, indent=2)}"
-        
-        # 根据查询类型获取数据
-        if internal_query_type == "最新财务数据":
-            data_resp = session.post(
-                f"{MCP_URL}/mcp",
-                json={
-                    "jsonrpc": "2.0",
-                    "method": "tools/call",
-                    "params": {
-                        "name": "get_latest_financial_data",
-                        "arguments": {"cik": cik}
-                    },
-                    "id": 1
-                },
-                headers=HEADERS,
-                timeout=60  # 增加到60秒
-            )
-            
-            if data_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {data_resp.status_code}\n\n{data_resp.text[:500]}"
-            
-            try:
-                data_result = data_resp.json()
-                # 使用统一的 MCP 响应解析函数
-                data = parse_mcp_response(data_result)
-            except (ValueError, KeyError, json.JSONDecodeError) as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\n{data_resp.text[:500]}"
-            
-            if isinstance(data, dict) and data.get("error"):
-                return result + f"❌ {data['error']}"
-            
-            cik = data.get('cik')
-            result += f"## Fiscal Year {data.get('period', 'N/A')}\n\n"
-            
-            total_revenue = data.get('total_revenue', 0) / 1e9 if data.get('total_revenue') else 0
-            net_income = data.get('net_income', 0) / 1e9 if data.get('net_income') else 0
-            eps = data.get('earnings_per_share', 0) if data.get('earnings_per_share') else 0
-            opex = data.get('operating_expenses', 0) / 1e9 if data.get('operating_expenses') else 0
-            ocf = data.get('operating_cash_flow', 0) / 1e9 if data.get('operating_cash_flow') else 0
-            
-            result += f"- **Total Revenue**: {format_value(total_revenue)}\n"
-            result += f"- **Net Income**: {format_value(net_income)}\n"
-            result += f"- **Earnings Per Share**: {format_value(eps, 'eps')}\n"
-            result += f"- **Operating Expenses**: {format_value(opex)}\n"
-            result += f"- **Operating Cash Flow**: {format_value(ocf)}\n"
-            # 使用后端返回的 source_url
-            source_form = data.get('source_form', 'N/A')
-            source_url = data.get('source_url', None)  # 从后端获取URL
-            result += f"- **Source Form**: {create_source_link(source_form, source_url)}\n"
-        
-        elif internal_query_type == "3年趋势":
-            metrics_resp = session.post(
-                f"{MCP_URL}/mcp",
-                json={
-                    "jsonrpc": "2.0",
-                    "method": "tools/call",
-                    "params": {
-                        "name": "extract_financial_metrics",
-                        "arguments": {"cik": cik, "years": 3}
-                    },
-                    "id": 1
-                },
-                headers=HEADERS,
-                timeout=120  # 3年趋势需要更长时间，增加到120秒
-            )
-            
-            if metrics_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {metrics_resp.status_code}\n\n{metrics_resp.text[:500]}"
-            
-            try:
-                metrics_result = metrics_resp.json()
-                # 使用统一的 MCP 响应解析函数
-                metrics = parse_mcp_response(metrics_result)
-            except (ValueError, KeyError, json.JSONDecodeError) as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\nResponse: {metrics_resp.text[:500]}"
-            
-            if isinstance(metrics, dict) and metrics.get("error"):
-                return result + f"❌ {metrics['error']}"
-            
-            result += f"## 3-Year Financial Trends ({metrics.get('periods', 0)} periods)\n\n"
-            
-            # 显示所有数据（包括年度和季度）
-            all_data = metrics.get('data', [])  # MCP Server 返回的字段是 'data'
-            
-            # 去重：根据period和source_form去重
-            seen = set()
-            unique_data = []
-            for m in all_data:
-                key = (m.get('period', 'N/A'), m.get('source_form', 'N/A'))
-                if key not in seen:
-                    seen.add(key)
-                    unique_data.append(m)
-            
-            # 按期间降序排序，确保显示最近的3年数据
-            # 使用更智能的排序：先按年份，再按是否是季度
-            # 正确顺序：FY2024 → 2024Q3 → 2024Q2 → 2024Q1 → FY2023
-            def sort_key(x):
-                period = x.get('period', '0000')
-                # 提取年份（前4位）
-                year = period[:4] if len(period) >= 4 else '0000'
-                # 如果有Q，提取季度号
-                if 'Q' in period:
-                    quarter = period[period.index('Q')+1] if period.index('Q')+1 < len(period) else '0'
-                    return (year, 1, 4 - int(quarter))  # Q在FY后面：Q3, Q2, Q1 (4-3=1, 4-2=2, 4-1=3)
-                else:
-                    return (year, 0, 0)  # FY 排在同年的所有Q之前
-            
-            unique_data = sorted(unique_data, key=sort_key, reverse=True)
-            
-            result += "| Period | Revenue (B) | Net Income (B) | EPS | Operating Expenses (B) | Operating Cash Flow (B) | Source Form |\n"
-            result += "|--------|-------------|----------------|-----|------------------------|-------------------------|-------------|\n"
-            
-            for m in unique_data:
-                period = m.get('period', 'N/A')
-                rev = (m.get('total_revenue') or 0) / 1e9
-                inc = (m.get('net_income') or 0) / 1e9
-                eps_val = m.get('earnings_per_share') or 0
-                opex = (m.get('operating_expenses') or 0) / 1e9
-                ocf = (m.get('operating_cash_flow') or 0) / 1e9
-                source_form = m.get('source_form', 'N/A')
-                source_url = m.get('source_url', None)  # 从后端获取URL
-                
-                # 区分年度和季度，修复双重FY前缀问题
-                if 'Q' in period:
-                    # 季度数据，不添加前缀
-                    display_period = period
-                else:
-                    # 年度数据，只在没有FY的情况下添加
-                    display_period = period if period.startswith('FY') else f"FY{period}"
-                
-                source_link = create_source_link(source_form, source_url)
-                
-                result += f"| {display_period} | {format_value(rev)} | {format_value(inc)} | {format_value(eps_val, 'eps')} | {format_value(opex)} | {format_value(ocf)} | {source_link} |\n"
-        
-        elif internal_query_type == "5年趋势":
-            metrics_resp = session.post(
-                f"{MCP_URL}/mcp",
-                json={
-                    "jsonrpc": "2.0",
-                    "method": "tools/call",
-                    "params": {
-                        "name": "extract_financial_metrics",
-                        "arguments": {"cik": cik, "years": 5}
-                    },
-                    "id": 1
-                },
-                headers=HEADERS,
-                timeout=180  # 5年趋势需要更长时间，增加到180秒
-            )
-            
-            if metrics_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {metrics_resp.status_code}\n\n{metrics_resp.text[:500]}"
-            
-            try:
-                metrics_result = metrics_resp.json()
-                # 使用统一的 MCP 响应解析函数
-                metrics = parse_mcp_response(metrics_result)
-            except (ValueError, KeyError, json.JSONDecodeError) as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\nResponse: {metrics_resp.text[:500]}"
-            
-            if isinstance(metrics, dict) and metrics.get("error"):
-                return result + f"❌ {metrics['error']}"
-            
-            # 显示所有数据（包括年度和季度）
-            all_data = metrics.get('data', [])  # MCP Server 返回的字段是 'data'
-            
-            # 去重：根据period和source_form去重
-            seen = set()
-            unique_data = []
-            for m in all_data:
-                key = (m.get('period', 'N/A'), m.get('source_form', 'N/A'))
-                if key not in seen:
-                    seen.add(key)
-                    unique_data.append(m)
-            
-            # 按期间降序排序，确保显示最近的5年数据
-            # 使用更智能的排序：先按年份，再按是否是季度
-            # 正确顺序：FY2024 → 2024Q3 → 2024Q2 → 2024Q1 → FY2023
-            def sort_key(x):
-                period = x.get('period', '0000')
-                # 提取年份（前4位）
-                year = period[:4] if len(period) >= 4 else '0000'
-                # 如果有Q，提取季度号
-                if 'Q' in period:
-                    quarter = period[period.index('Q')+1] if period.index('Q')+1 < len(period) else '0'
-                    return (year, 1, 4 - int(quarter))  # Q在FY后面：Q3, Q2, Q1 (4-3=1, 4-2=2, 4-1=3)
-                else:
-                    return (year, 0, 0)  # FY 排在同年的所有Q之前
-            
-            unique_data = sorted(unique_data, key=sort_key, reverse=True)
-            
-            result += f"## 5-Year Financial Trends ({metrics.get('periods', 0)} periods)\n\n"
-            result += "| Period | Revenue (B) | Net Income (B) | EPS | Operating Expenses (B) | Operating Cash Flow (B) | Source Form |\n"
-            result += "|--------|-------------|----------------|-----|------------------------|-------------------------|-------------|\n"
-            
-            for m in unique_data:
-                period = m.get('period', 'N/A')
-                rev = (m.get('total_revenue') or 0) / 1e9
-                inc = (m.get('net_income') or 0) / 1e9
-                eps_val = m.get('earnings_per_share') or 0
-                opex = (m.get('operating_expenses') or 0) / 1e9
-                ocf = (m.get('operating_cash_flow') or 0) / 1e9
-                source_form = m.get('source_form', 'N/A')
-                source_url = m.get('source_url', None)  # 从后端获取URL
-                
-                # 区分年度和季度，修复双重FY前缀问题
-                if 'Q' in period:
-                    # 季度数据，不添加前缀
-                    display_period = period
-                else:
-                    # 年度数据，只在没有FY的情况下添加
-                    display_period = period if period.startswith('FY') else f"FY{period}"
-                
-                source_link = create_source_link(source_form, source_url)
-                
-                result += f"| {display_period} | {format_value(rev)} | {format_value(inc)} | {format_value(eps_val, 'eps')} | {format_value(opex)} | {format_value(ocf)} | {source_link} |\n"
-        
-        elif internal_query_type == "公司报表列表":
-            # 查询公司所有报表
-            filings_resp = session.post(
-                f"{MCP_URL}/mcp",
-                json={
-                    "jsonrpc": "2.0",
-                    "method": "tools/call",
-                    "params": {
-                        "name": "get_company_filings",
-                        "arguments": {"cik": cik, "limit": 50}
-                    },
-                    "id": 1
-                },
-                headers=HEADERS,
-                timeout=90  # 增加到90秒
-            )
-            
-            if filings_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {filings_resp.status_code}\n\n{filings_resp.text[:500]}"
-            
-            try:
-                filings_result = filings_resp.json()
-                # 使用统一的 MCP 响应解析函数
-                filings_data = parse_mcp_response(filings_result)
-            except (ValueError, KeyError, json.JSONDecodeError) as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\n{filings_resp.text[:500]}"
-            
-            if isinstance(filings_data, dict) and filings_data.get("error"):
-                return result + f"❌ {filings_data['error']}"
-            
-            filings = filings_data.get('filings', []) if isinstance(filings_data, dict) else filings_data
-            
-            result += f"## Company Filings ({len(filings)} records)\n\n"
-            result += "| Form Type | Filing Date | Accession Number | Primary Document |\n"
-            result += "|-----------|-------------|------------------|------------------|\n"
-            
-            for filing in filings:
-                form_type = filing.get('form_type', 'N/A')
-                filing_date = filing.get('filing_date', 'N/A')
-                accession_num = filing.get('accession_number', 'N/A')
-                primary_doc = filing.get('primary_document', 'N/A')
-                filing_url = filing.get('filing_url', None)  # 从后端获取URL
-                
-                # 使用后端返回的URL创建链接
-                if filing_url and filing_url != 'N/A':
-                    form_link = f"[{form_type}]({filing_url})"
-                    primary_doc_link = f"[{primary_doc}]({filing_url})"
-                else:
-                    form_link = form_type
-                    primary_doc_link = primary_doc
-                
-                result += f"| {form_link} | {filing_date} | {accession_num} | {primary_doc_link} |\n"
-        
-        return result
-        
-    except requests.exceptions.RequestException as e:
-        return f"❌ Network Error: {str(e)}\n\nMCP Server: {MCP_URL}"
-    except Exception as e:
-        import traceback
-        return f"❌ Unexpected Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+# ========== 初始化 LLM 客户端 ==========
+hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
+client = InferenceClient(api_key=hf_token) if hf_token else InferenceClient()
+print(f"✅ LLM initialized: Qwen/Qwen2.5-72B-Instruct:novita")
+print(f"📊 MCP Services: {len(MCP_SERVICES)} services, {len(MCP_TOOLS)} tools")
+print(f"   - Financial: advanced_search_company, get_latest_financial_data, extract_financial_metrics")
+print(f"   - Market: get_quote, get_market_news, get_company_news")
+
+# ========== 系统提示词 ==========
+SYSTEM_PROMPT = """You are an intelligent financial and market analysis assistant.
+
+You have access to TWO data sources:
+
+1. **SEC Financial Reports** (Official filings)
+   - advanced_search_company: Find US companies
+   - get_latest_financial_data: Get latest 10-K/10-Q data
+   - extract_financial_metrics: Get multi-year trends
+
+2. **Market & Stock Data** (Finnhub real-time)
+   - get_quote: Real-time stock price, volume, change
+   - get_market_news: Latest market news (general/forex/crypto/merger)
+   - get_company_news: Company-specific news
+
+Automatically use the right tools and provide clear, data-driven insights."""
 
-# 调用 MCP 工具的实际执行函数
+# ========== 核心函数：调用 MCP 工具 ==========
 def call_mcp_tool(tool_name, arguments):
-    """调用 MCP 工具并返回结果"""
+    """调用 MCP 工具"""
+    mcp_url = TOOL_ROUTING.get(tool_name)
+    if not mcp_url:
+        return {"error": f"Unknown tool: {tool_name}"}
+    
     try:
-        # FastMCP HTTP Server 使用 /mcp 端点
-        response = session.post(
-            f"{MCP_URL}/mcp",
+        response = requests.post(
+            mcp_url,
             json={
                 "jsonrpc": "2.0",
                 "method": "tools/call",
-                "params": {
-                    "name": tool_name,
-                    "arguments": arguments
-                },
+                "params": {"name": tool_name, "arguments": arguments},
                 "id": 1
             },
-            headers=HEADERS,
+            headers={"Content-Type": "application/json"},
             timeout=60
         )
         
-        if response.status_code != 200:
-            return {"error": f"HTTP {response.status_code}: {response.text[:200]}"}
-        
-        result = response.json()
-        return parse_mcp_response(result)
+        if response.status_code == 200:
+            return response.json()
+        else:
+            return {"error": f"HTTP {response.status_code}", "detail": response.text[:200]}
     except Exception as e:
         return {"error": str(e)}
 
-# Chatbot 功能：使用 LLM + MCP 工具
+# ========== 核心函数：AI 助手 ==========
 def chatbot_response(message, history):
-    """智能聊天机器人，集成 LLM 和 MCP 工具"""
+    """AI 助手主函数"""
     try:
-        # 构建对话历史
-        messages = []
+        # 构建消息历史
+        messages = [{"role": "system", "content": SYSTEM_PROMPT}]
         
-        # 系统提示词 - 自由智能的财报分析助手
-        system_prompt = """You are an intelligent financial analysis assistant with access to real-time SEC EDGAR data.
-
-You have 3 powerful tools to fetch financial data:
-1. advanced_search_company(company_input) - Search for any US company
-2. get_latest_financial_data(cik) - Get latest financial report
-3. extract_financial_metrics(cik, years) - Get multi-year trends (3 or 5 years)
-
-When users ask about a company:
-- Automatically use tools to fetch the data they need
-- Analyze the numbers and provide insights
-- Explain trends, growth rates, and what they mean
-- Be conversational and helpful
-
-You can handle any financial question - from simple data queries to complex multi-company comparisons. Be creative and thorough in your analysis."""
-        
-        messages.append({"role": "system", "content": system_prompt})
-        
-        # 添加历史对话（最近 5 轮）
-        # Gradio 6.x 的 history 格式可能是 [{"role": "user", "content": ...}, {"role": "assistant", "content": ...}]
-        # 或者是 [(user_msg, assistant_msg), ...] 的元组列表
+        # 添加对话历史（最近5轮）
         if history:
             for item in history[-5:]:
                 if isinstance(item, dict):
-                    # 新格式：字典列表
                     messages.append(item)
                 elif isinstance(item, (list, tuple)) and len(item) == 2:
-                    # 旧格式：元组列表
                     user_msg, assistant_msg = item
                     messages.append({"role": "user", "content": user_msg})
                     messages.append({"role": "assistant", "content": assistant_msg})
         
-        # 添加当前消息
         messages.append({"role": "user", "content": message})
         
-        # 调用 LLM，启用工具调用
-        response_text = ""
+        # LLM 调用循环（支持多轮工具调用）
         tool_calls_log = []
-        max_iterations = 5  # 防止无限循环
-        iteration = 0
-        
-        while iteration < max_iterations:
-            iteration += 1
+        max_iterations = 5
+        
+        for iteration in range(max_iterations):
+            # 调用 LLM
+            response = client.chat_completion(
+                messages=messages,
+                model="Qwen/Qwen2.5-72B-Instruct:novita",
+                tools=MCP_TOOLS,
+                max_tokens=3000,
+                temperature=0.7,
+                tool_choice="auto"
+            )
             
-            # 使用支持工具调用的模型（如 Qwen, Llama 等）
-            try:
-                # 检查 client 是否可用
-                if client is None:
-                    print("⚠️ LLM client not available, using fallback")
-                    return fallback_chatbot_response(message)
-                
-                response = client.chat_completion(
-                    messages=messages,
-                    model="Qwen/Qwen2.5-72B-Instruct:novita",  # 使用 novita provider，更稳定
-                    tools=MCP_TOOLS,
-                    max_tokens=3000,
-                    temperature=0.7,
-                    tool_choice="auto"
-                )
-                
-                print(f"✅ LLM response received (iteration {iteration})")
-                
-                choice = response.choices[0]
+            choice = response.choices[0]
+            
+            # 检查是否有工具调用
+            if choice.message.tool_calls:
+                messages.append(choice.message)
                 
-                # 检查是否有工具调用
-                if choice.message.tool_calls:
-                    # 有工具调用
-                    print(f"🔧 Tool calls detected: {len(choice.message.tool_calls)}")
-                    messages.append(choice.message)
+                for tool_call in choice.message.tool_calls:
+                    tool_name = tool_call.function.name
+                    tool_args = json.loads(tool_call.function.arguments)
                     
-                    for tool_call in choice.message.tool_calls:
-                        tool_name = tool_call.function.name
-                        tool_args = json.loads(tool_call.function.arguments)
-                        
-                        print(f"  → Calling tool: {tool_name} with args: {tool_args}")
-                        
-                        # 记录工具调用
-                        tool_calls_log.append({
-                            "name": tool_name,
-                            "arguments": tool_args
-                        })
-                        
-                        # 调用 MCP 工具
-                        tool_result = call_mcp_tool(tool_name, tool_args)
-                        
-                        print(f"  ← Tool result received")
-                        
-                        # 将工具结果添加到消息列表
-                        messages.append({
-                            "role": "tool",
-                            "name": tool_name,
-                            "content": json.dumps(tool_result),
-                            "tool_call_id": tool_call.id
-                        })
+                    # 记录工具调用
+                    tool_calls_log.append({"name": tool_name, "arguments": tool_args})
                     
-                    # 继续下一轮对话，让 LLM 处理工具结果
-                    continue
-                else:
-                    # 没有工具调用，直接返回回答
-                    print(f"💬 Final response generated")
-                    response_text = choice.message.content
-                    break
+                    # 调用 MCP 工具
+                    tool_result = call_mcp_tool(tool_name, tool_args)
                     
-            except ValueError as ve:
-                error_msg = str(ve)
-                print(f"⚠️ LLM API error: {ve}")
-                if "api_key" in error_msg.lower() or "token" in error_msg.lower():
-                    print("ℹ️ Falling back to simple response logic")
-                    return fallback_chatbot_response(message)
-                else:
-                    raise
-            except Exception as e:
-                print(f"⚠️ LLM API error: {type(e).__name__} - {e}")
-                # 如果是工具调用相关错误，尝试不使用工具重新请求
-                if "tool" in str(e).lower() or "function" in str(e).lower():
-                    print("🔄 Tool calling failed, retrying without tools...")
-                    try:
-                        response = client.chat_completion(
-                            messages=messages,
-                            model="Qwen/Qwen2.5-72B-Instruct:novita",
-                            max_tokens=3000,
-                            temperature=0.7
-                        )
-                        response_text = response.choices[0].message.content
-                        return response_text
-                    except:
-                        pass
-                print("ℹ️ Falling back to simple response logic")
-                return fallback_chatbot_response(message)
+                    # 添加工具结果到消息
+                    messages.append({
+                        "role": "tool",
+                        "name": tool_name,
+                        "content": json.dumps(tool_result),
+                        "tool_call_id": tool_call.id
+                    })
+                
+                continue  # 继续下一轮
+            else:
+                # 无工具调用，返回最终答案
+                response_text = choice.message.content
+                break
         
         # 构建最终响应
         final_response = ""
         
-        # 显示调试信息：使用的模型
-        final_response += f"<div style='padding: 8px; background: #e3f2fd; border-left: 3px solid #2196f3; margin-bottom: 10px; font-size: 0.9em;'>🤖 <strong>Model:</strong> Qwen/Qwen2.5-72B-Instruct:novita | <strong>Iterations:</strong> {iteration}</div>\n\n"
+        # 显示模型信息
+        final_response += f"<div style='padding: 8px; background: #e3f2fd; border-left: 3px solid #2196f3; margin-bottom: 10px; font-size: 0.9em;'>🤖 <strong>Model:</strong> Qwen/Qwen2.5-72B-Instruct:novita</div>\n\n"
         
-        # 如果有工具调用，显示调用日志
+        # 显示工具调用日志
         if tool_calls_log:
             final_response += "**🛠️ MCP Tools Used:**\n\n"
             for i, tool_call in enumerate(tool_calls_log, 1):
-                final_response += f"{i}. `{tool_call['name']}` with arguments: `{json.dumps(tool_call['arguments'])}`\n"
+                final_response += f"{i}. `{tool_call['name']}` - {json.dumps(tool_call['arguments'])}\n"
             final_response += "\n---\n\n"
         
         final_response += response_text
@@ -777,156 +254,38 @@ You can handle any financial question - from simple data queries to complex mult
         return final_response
         
     except Exception as e:
-        import traceback
-        return f"❌ Error: {str(e)}\n\nTraceback:\n```\n{traceback.format_exc()}\n```"
-
-def fallback_chatbot_response(message):
-    """退回策略：当 LLM API 不可用时使用的简单逻辑"""
-    # 显示警告信息
-    fallback_warning = "<div style='padding: 10px; background: #fff3cd; border-left: 4px solid #ffc107; margin-bottom: 15px;'>⚠️ <strong>Notice:</strong> LLM API unavailable, using fallback logic.</div>\n\n"
-    
-    # 检查是否是财务查询相关问题
-    financial_keywords = ['financial', 'revenue', 'income', 'earnings', 'cash flow', 'expenses', '财务', '收入', '利润', 'data', 'trend', 'performance']
-    
-    if any(keyword in message.lower() for keyword in financial_keywords):
-        # 提取公司名称和查询类型
-        company_keywords = ['apple', 'microsoft', 'nvidia', 'tesla', 'alibaba', 'google', 'amazon', 'meta', 'tsla', 'aapl', 'msft', 'nvda', 'googl', 'amzn']
-        detected_company = None
-        
-        for company in company_keywords:
-            if company in message.lower():
-                if company in ['aapl']: detected_company = 'Apple'
-                elif company in ['msft']: detected_company = 'Microsoft' 
-                elif company in ['nvda']: detected_company = 'NVIDIA'
-                elif company in ['tsla']: detected_company = 'Tesla'
-                elif company in ['googl']: detected_company = 'Google'
-                elif company in ['amzn']: detected_company = 'Amazon'
-                else: detected_company = company.capitalize()
-                break
-        
-        if detected_company:
-            # 根据问题内容选择查询类型
-            if any(word in message.lower() for word in ['trend', '趋势', 'history', 'historical', 'over time']):
-                if any(word in message for word in ['5', 'five', '五年']):
-                    query_type = '5-Year Trends'
-                else:
-                    query_type = '3-Year Trends'
-            else:
-                query_type = 'Latest Financial Data'
-            
-            # 调用财务查询函数
-            result = query_financial_data(detected_company, query_type)
-            return fallback_warning + f"Here's the financial information for {detected_company}:\n\n{result}"
-        else:
-            return fallback_warning + "I can help you query financial data! Please specify a company name. For example: 'Show me Apple's latest financial data' or 'What's NVIDIA's 3-year trend?' \n\nSupported companies include: Apple, Microsoft, NVIDIA, Tesla, Alibaba, Google, Amazon, and more."
-    
-    # 如果不是财务查询，返回通用回复
-    return fallback_warning + "Hello! I'm a financial data assistant powered by SEC EDGAR data. I can help you query financial information for US listed companies.\n\n**What I can do:**\n- Get latest financial data (revenue, income, EPS, etc.)\n- Show 3-year or 5-year financial trends\n- Provide detailed financial metrics\n\n**Try asking:**\n- 'Show me Apple's latest financial data'\n- 'What's NVIDIA's 3-year financial trend?'\n- 'How is Microsoft performing financially?'"
-
-# 包装函数，显示加载状态
-def query_with_status(company, query_type):
-    """Query with loading status indicator"""
-    try:
-        # 返回加载状态和结果
-        yield "<div style='padding: 10px; background: #e3f2fd; border-left: 4px solid #2196f3; margin: 10px 0;'>🔄 <strong>Loading...</strong> Querying SEC EDGAR data for <strong>{}</strong>...</div>".format(company), ""
-        
-        # 执行实际查询
-        result = query_financial_data(company, query_type)
-        
-        # 返回成功状态和结果
-        yield "<div style='padding: 10px; background: #e8f5e9; border-left: 4px solid #4caf50; margin: 10px 0;'>✅ <strong>Query completed successfully!</strong></div>", result
-    
-    except Exception as e:
-        # 返回错误状态
-        yield "<div style='padding: 10px; background: #ffebee; border-left: 4px solid #f44336; margin: 10px 0;'>❌ <strong>Error:</strong> {}</div>".format(str(e)), ""
+        return f"❌ Error: {str(e)}"
 
-# 创建 Gradio 界面
-with gr.Blocks(title="SEC Financial Data Query Assistant") as demo:
-    gr.Markdown("# 🤖 SEC Financial Data Query Assistant")
+# ========== Gradio 界面 ==========
+with gr.Blocks(title="Financial & Market AI Assistant") as demo:
+    gr.Markdown("# 🤖 Financial & Market AI Assistant")
     
-    # 显示 AI 功能说明
     gr.Markdown("""
     <div style='padding: 15px; background: #d4edda; border-left: 4px solid #28a745; margin: 10px 0; border-radius: 4px;'>
-        <strong>✅ AI Assistant Enabled:</strong> Powered by Qwen/Qwen2.5-72B-Instruct:novita with automatic MCP tool calling.
+        <strong>✅ AI Powered by:</strong> Qwen/Qwen2.5-72B-Instruct:novita
         <br>
-        <strong>💬 Ask me anything:</strong> I can understand natural language and automatically fetch financial data when needed!
+        <strong>📊 Data Sources:</strong> SEC Financial Reports + Market & Stock Data
     </div>
     """)
     
-    with gr.Tab("AI Assistant"):
-        # 使用 Gradio ChatInterface（兼容 4.44.1）
-        chat = gr.ChatInterface(
-            fn=chatbot_response,
-            examples=[
-                "Analyze Apple's latest financial performance",
-                "Show me NVIDIA's 3-year revenue and profit trends",
-                "What's Tesla's profitability situation? Is it improving?",
-                "Compare Microsoft's recent quarterly results with last year",
-                "Analyze Amazon's operating cash flow trends over 5 years",
-                "Is Alibaba's earnings per share growing or declining?",
-                "What are the key highlights in Meta's latest financial report?",
-                "Evaluate Google's revenue growth and profit margins",
-            ],
-            cache_examples=False,
-            title="📊 Financial Reporting Analysis Expert",
-            description="I'm a financial analysis expert specializing in SEC EDGAR data. Ask me to analyze any US-listed company's financial performance, and I'll provide professional insights based on real financial reports."
-        )
-    
-    with gr.Tab("Direct Query"):
-        gr.Markdown("## 🔍 Direct Financial Data Query")
-        gr.Markdown("Select a company and query type to retrieve financial information.")
-        
-        with gr.Row():
-            company_input = gr.Textbox(
-                label="Company Name or Stock Symbol",
-                placeholder="e.g., NVIDIA, Apple, Alibaba, AAPL",
-                scale=2
-            )
-            query_type = gr.Radio(
-                ["Latest Financial Data", "3-Year Trends", "5-Year Trends", "Company Filings"],
-                label="Query Type",
-                value="Latest Financial Data",
-                scale=1
-            )
-        
-        submit_btn = gr.Button("🔍 Query Financial Data", variant="primary", size="lg")
-        
-        # 添加加载状态指示器
-        with gr.Row():
-            status_text = gr.Markdown("")
-        
-        output = gr.Markdown(label="Query Results")
-        
-        # 示例
-        gr.Examples(
-            examples=[
-                ["NVIDIA", "Latest Financial Data"],
-                ["Apple", "3-Year Trends"],
-                ["Microsoft", "5-Year Trends"],
-                ["Alibaba", "Company Filings"],
-                ["Tesla", "3-Year Trends"]
-            ],
-            inputs=[company_input, query_type],
-            outputs=output,
-            fn=query_financial_data,
-            cache_examples=False
-        )
-        
-        submit_btn.click(
-            fn=query_with_status,
-            inputs=[company_input, query_type],
-            outputs=[status_text, output],
-            show_progress="full"  # 显示完整的进度条
-        )
-    
-    gr.Markdown("---")
-    gr.Markdown("**Data Source**: SEC EDGAR | **MCP Server**: https://huggingface.co/spaces/JC321/EasyReportDateMCP")
+    chat = gr.ChatInterface(
+        fn=chatbot_response,
+        examples=[
+            "What's Apple's latest revenue and profit?",
+            "Show me NVIDIA's 3-year financial trends",
+            "How is Tesla's stock performing today?",
+            "Get the latest market news about crypto",
+            "Compare Microsoft's latest earnings with its current stock price",
+        ],
+        title="💬 AI Assistant",
+        description="Ask me about company financials, stock prices, or market trends. I'll automatically fetch the data you need!"
+    )
 
-# Launch the app for Hugging Face Space
+# 启动应用
 if __name__ == "__main__":
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
         show_error=True,
-        ssr_mode=False  # 禁用 SSR 模式，避免 asyncio 文件描述符错误
-    )
+        ssr_mode=False
+    )