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README.md

@@ -1,39 +1,39 @@
----
-title: SEC Financial Data Query Assistant
-emoji: 📊
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 6.0.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-# SEC Financial Data Query Assistant
-
-A Gradio-based web application for querying SEC financial data through MCP Server.
-
-## Features
-
-- 🔍 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
-
-## Usage
-
-Simply enter a company name or ticker symbol (e.g., NVIDIA, AAPL, Microsoft) and select the query type:
-- **Latest Financial Data**: Shows the most recent fiscal year data
-- **3-Year Trend**: Displays financial trends over 3 years
-- **5-Year Trend**: Displays financial trends over 5 years
-
-## Data Source
-
-SEC EDGAR data via MCP Server: https://jc321-easyreportdatemcp.hf.space
-
-## Technology Stack
-
-- **Frontend**: Gradio 4.0+
-- **Backend**: Python with requests
-- **Data Source**: SEC EDGAR via MCP Server
+---
+title: SEC Financial Data Query Assistant
+emoji: 📊
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 6.0.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# SEC Financial Data Query Assistant
+
+A Gradio-based web application for querying SEC financial data through MCP Server.
+
+## Features
+
+- 🔍 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
+
+## Usage
+
+Simply enter a company name or ticker symbol (e.g., NVIDIA, AAPL, Microsoft) and select the query type:
+- **Latest Financial Data**: Shows the most recent fiscal year data
+- **3-Year Trend**: Displays financial trends over 3 years
+- **5-Year Trend**: Displays financial trends over 5 years
+
+## Data Source
+
+SEC EDGAR data via MCP Server: https://huggingface.co/spaces/JC321/EasyReportDateMCP
+
+## Technology Stack
+
+- **Frontend**: Gradio 4.0+
+- **Backend**: Python with requests
+- **Data Source**: SEC EDGAR via MCP Server

app.py

@@ -5,9 +5,10 @@ import os
 import time
 from requests.adapters import HTTPAdapter
 from urllib3.util.retry import Retry
+from huggingface_hub import InferenceClient
 
-MCP_SPACE = "JC321/EasyReportsMCPServer"
-MCP_URL = "https://jc321-easyreportsmcpserver.hf.space"
+MCP_SPACE = "JC321/EasyReportDateMCP"
+MCP_URL = "https://jc321-easyreportdatemcp.hf.space"
 
 # 设置请求头
 HEADERS = {
@@ -32,6 +33,71 @@ def create_session_with_retry():
 # 创建全局 session
 session = create_session_with_retry()
 
+# 初始化 Hugging Face Inference Client
+# 使用环境变量或者免费的公开模型
+HF_TOKEN = os.getenv("HF_TOKEN", None)  # 可选：如果需要访问私有模型
+client = InferenceClient(token=HF_TOKEN)
+
+# 定义可用的 MCP 工具
+MCP_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",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "company_input": {
+                        "type": "string",
+                        "description": "Company name or stock ticker symbol (e.g., 'Apple', 'AAPL', 'Microsoft', 'TSLA')"
+                    }
+                },
+                "required": ["company_input"]
+            }
+        }
+    },
+    {
+        "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",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "cik": {
+                        "type": "string",
+                        "description": "10-digit CIK number of the company (must be obtained from advanced_search_company first)"
+                    }
+                },
+                "required": ["cik"]
+            }
+        }
+    },
+    {
+        "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",
+            "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]
+                    }
+                },
+                "required": ["cik", "years"]
+            }
+        }
+    }
+]
+
 # 格式化数值显示
 def format_value(value, value_type="money"):
     """
@@ -475,50 +541,180 @@ def query_financial_data(company_name, query_type):
         import traceback
         return f"❌ Unexpected Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
 
-# Chatbot 功能：使用MCP工具
+# 调用 MCP 工具的实际执行函数
+def call_mcp_tool(tool_name, arguments):
+    """调用 MCP 工具并返回结果"""
+    try:
+        response = session.post(
+            f"{MCP_URL}/message",
+            json={
+                "method": "tools/call",
+                "params": {
+                    "name": tool_name,
+                    "arguments": arguments
+                }
+            },
+            headers=HEADERS,
+            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)
+    except Exception as e:
+        return {"error": str(e)}
+
+# Chatbot 功能：使用 LLM + MCP 工具
 def chatbot_response(message, history):
-    """聊天机器人响应函数，集成MCP工具"""
+    """智能聊天机器人，集成 LLM 和 MCP 工具"""
     try:
-        # 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
+        # 构建对话历史
+        messages = []
+        
+        # 系统提示词
+        system_prompt = """You are a helpful financial data assistant with access to SEC EDGAR data through specialized tools. 
+
+You can help users with:
+- General questions and conversations about any topic
+- Financial data queries for US listed companies
+- Company information and stock data analysis
+
+When users ask about financial data, company information, or stock performance, you should use the available tools to retrieve accurate, real-time data from SEC EDGAR filings.
+
+Available tools:
+1. advanced_search_company: Search for company information by name or ticker
+2. get_latest_financial_data: Get the latest financial metrics for a company
+3. extract_financial_metrics: Get historical financial trends (3 or 5 years)
+
+Always be helpful, accurate, and cite the data sources when providing financial information."""
+        
+        messages.append({"role": "system", "content": system_prompt})
+        
+        # 添加历史对话（最近 5 轮）
+        for user_msg, assistant_msg in history[-5:]:
+            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 = ""
+        tool_calls_log = []
+        max_iterations = 5  # 防止无限循环
+        iteration = 0
+        
+        while iteration < max_iterations:
+            iteration += 1
             
-            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'
+            # 使用支持工具调用的模型（如 Qwen, Llama 等）
+            try:
+                response = client.chat_completion(
+                    messages=messages,
+                    model="Qwen/Qwen2.5-72B-Instruct",  # 支持工具调用的模型
+                    tools=MCP_TOOLS,
+                    max_tokens=2000,
+                    temperature=0.7
+                )
                 
-                # 调用财务查询函数
-                result = query_financial_data(detected_company, query_type)
-                return f"Here's the financial information for {detected_company}:\n\n{result}"
-            else:
-                return "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."
+                choice = response.choices[0]
+                
+                # 检查是否有工具调用
+                if choice.message.tool_calls:
+                    # 有工具调用
+                    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)
+                        
+                        # 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)
+                        
+                        # 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
+                        })
+                    
+                    # 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
+                    response_text = choice.message.content
+                    break
+                    
+            except Exception as e:
+                # 如果 LLM API 失败，退回到简单逻辑
+                return fallback_chatbot_response(message)
+        
+        # 构建最终响应
+        final_response = ""
+        
+        # 如果有工具调用，显示调用日志
+        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 += "\n---\n\n"
+        
+        final_response += response_text
+        
+        return final_response
         
-        # 如果不是财务查询，返回通用回复
-        return "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?'"
-    
     except Exception as e:
-        return f"Sorry, I encountered an error: {str(e)}. Please try asking about financial data for specific companies like Apple, Microsoft, NVIDIA, Tesla, etc."
+        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"""
+    # 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 f"Here's the financial information for {detected_company}:\n\n{result}"
+        else:
+            return "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 "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):
@@ -540,13 +736,6 @@ def query_with_status(company, query_type):
 # 创建 Gradio 界面
 with gr.Blocks(title="SEC Financial Data Query Assistant") as demo:
     gr.Markdown("# 🤖 SEC Financial Data Query Assistant")
-    gr.Markdown("Query SEC financial data for US listed companies through MCP Server")
-    
-    # 添加 MCP Server 状态提示
-    with gr.Row():
-        gr.Markdown(f"🔗 **MCP Server**: [{MCP_URL}]({MCP_URL})")
-    with gr.Row():
-        gr.Markdown("⚠️ **Note**: First query after idle may take 1-2 minutes (server cold start). Please be patient.")
     
     with gr.Tab("AI Assistant"):
         # 使用 Gradio ChatInterface（兼容 4.44.1）
@@ -609,7 +798,7 @@ with gr.Blocks(title="SEC Financial Data Query Assistant") as demo:
         )
     
     gr.Markdown("---")
-    gr.Markdown(f"**Data Source**: SEC EDGAR | **MCP Server**: `{MCP_URL}`")
+    gr.Markdown("**Data Source**: SEC EDGAR | **MCP Server**: https://huggingface.co/spaces/JC321/EasyReportDateMCP")
 
 # Launch the app for Hugging Face Space
 if __name__ == "__main__":

requirements.txt

@@ -1,3 +1,4 @@
 # Updated to Gradio 6.0.1
 gradio==6.0.1
-requests
+requests
+huggingface_hub