Upload 2 files

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import requests
+from gradio_client import Client
 
 MCP_URL = "https://jc321-easyreportdatemcp.hf.space"
 
@@ -9,96 +10,69 @@ HEADERS = {
     "User-Agent": "SEC-Query-Assistant/1.0 (jtyxabc@gmail.com)"
 }
 
+# 初始化 Gradio Client
+try:
+    client = Client(MCP_URL)
+except Exception as e:
+    client = None
+    print(f"Warning: Failed to initialize Gradio Client: {e}")
+
 def query_financial_data(company_name, query_type):
     """查询财务数据的主函数"""
     
     if not company_name:
         return "请输入公司名称或股票代码"
     
+    if client is None:
+        return "❌ Gradio Client 未初始化，无法连接到 MCP 服务"
+    
     try:
-        # Gradio Space API 调用方式
+        # 使用 Gradio Client 调用 MCP 服务
         # 先搜索公司
-        search_resp = requests.post(
-            f"{MCP_URL}/call/advanced_search",
-            json={"data": [company_name]},
-            headers=HEADERS,
-            timeout=30
+        company = client.predict(
+            company_input=company_name,
+            api_name="/advanced_search"
         )
         
-        # 检查响应状态
-        if search_resp.status_code != 200:
-            return f"❌ Server Error: HTTP {search_resp.status_code}\n\nResponse: {search_resp.text[:500]}"
-        
-        try:
-            resp_data = search_resp.json()
-            # Gradio API 返回格式: {"data": [result]}
-            if isinstance(resp_data, dict) and "data" in resp_data:
-                company = resp_data["data"][0] if resp_data["data"] else {}
-            else:
-                company = resp_data
-        except ValueError as e:
-            return f"❌ JSON Parse Error: {str(e)}\n\nResponse Text: {search_resp.text[:500]}"
-        
-        if company.get("error"):
+        if isinstance(company, dict) and company.get("error"):
             return f"❌ 错误: {company['error']}"
         
-        result = f"# {company['name']}\n\n"
+        result = f"# {company.get('name', 'Unknown')}\n\n"
         result += f"**股票代码**: {company.get('tickers', ['N/A'])[0] if company.get('tickers') else 'N/A'}\n"
         result += f"**行业**: {company.get('sic_description', 'N/A')}\n\n---\n\n"
         
-        cik = company['cik']
+        cik = company.get('cik')
         
         # 根据查询类型获取数据
         if query_type == "最新财务数据":
-            data_resp = requests.post(
-                f"{MCP_URL}/call/get_latest_financial_data",
-                json={"data": [cik]},
-                headers=HEADERS,
-                timeout=30
+            data = client.predict(
+                cik=cik,
+                api_name="/get_latest_financial_data"
             )
             
-            if data_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {data_resp.status_code}\n\n{data_resp.text[:500]}"
-            
-            try:
-                resp_data = data_resp.json()
-                data = resp_data["data"][0] if isinstance(resp_data, dict) and "data" in resp_data else resp_data
-            except ValueError as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\n{data_resp.text[:500]}"
-            
-            if data.get("error"):
+            if isinstance(data, dict) and data.get("error"):
                 return result + f"❌ {data['error']}"
             
-            result += f"## 财年 {data['period']}\n\n"
-            result += f"- **总收入**: ${data['total_revenue']:,.0f} (${data['total_revenue']/1e9:.2f}B)\n"
-            result += f"- **净利润**: ${data['net_income']:,.0f} (${data['net_income']/1e9:.2f}B)\n"
-            result += f"- **每股收益**: ${data['earnings_per_share']:.2f}\n"
-            result += f"- **来源**: {data['source_form']}\n"
+            result += f"## 财年 {data.get('period', 'N/A')}\n\n"
+            result += f"- **总收入**: ${data.get('total_revenue', 0):,.0f} (${data.get('total_revenue', 0)/1e9:.2f}B)\n"
+            result += f"- **净利润**: ${data.get('net_income', 0):,.0f} (${data.get('net_income', 0)/1e9:.2f}B)\n"
+            result += f"- **每股收益**: ${data.get('earnings_per_share', 0):.2f}\n"
+            result += f"- **来源**: {data.get('source_form', 'N/A')}\n"
         
         elif query_type == "3年趋势":
-            metrics_resp = requests.post(
-                f"{MCP_URL}/call/extract_financial_metrics",
-                json={"data": [cik, 3]},
-                headers=HEADERS,
-                timeout=60
+            metrics = client.predict(
+                cik=cik,
+                years=3,
+                api_name="/extract_financial_metrics"
             )
             
-            if metrics_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {metrics_resp.status_code}\n\n{metrics_resp.text[:500]}"
-            
-            try:
-                resp_data = metrics_resp.json()
-                metrics = resp_data["data"][0] if isinstance(resp_data, dict) and "data" in resp_data else resp_data
-            except ValueError as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\n{metrics_resp.text[:500]}"
-            
-            if metrics.get("error"):
+            if isinstance(metrics, dict) and metrics.get("error"):
                 return result + f"❌ {metrics['error']}"
             
-            result += f"## 财务趋势 (共 {metrics['count']} 个期间)\n\n"
+            result += f"## 财务趋势 (共 {metrics.get('count', 0)} 个期间)\n\n"
             
             # 只显示年度数据
-            annual_data = [m for m in metrics['metrics'] if 'Q' not in m['period']][:3]
+            annual_data = [m for m in metrics.get('metrics', []) if 'Q' not in m.get('period', '')][:3]
             
             result += "| 期间 | 收入(十亿) | 净利润(十亿) | EPS |\n"
             result += "|------|-----------|------------|-----|\n"
@@ -111,26 +85,16 @@ def query_financial_data(company_name, query_type):
                 result += f"| FY{period} | ${rev:.2f}B | ${inc:.2f}B | ${eps:.2f} |\n"
         
         elif query_type == "5年趋势":
-            metrics_resp = requests.post(
-                f"{MCP_URL}/call/extract_financial_metrics",
-                json={"data": [cik, 5]},
-                headers=HEADERS,
-                timeout=60
+            metrics = client.predict(
+                cik=cik,
+                years=5,
+                api_name="/extract_financial_metrics"
             )
             
-            if metrics_resp.status_code != 200:
-                return result + f"❌ Server Error: HTTP {metrics_resp.status_code}\n\n{metrics_resp.text[:500]}"
-            
-            try:
-                resp_data = metrics_resp.json()
-                metrics = resp_data["data"][0] if isinstance(resp_data, dict) and "data" in resp_data else resp_data
-            except ValueError as e:
-                return result + f"❌ JSON Parse Error: {str(e)}\n\n{metrics_resp.text[:500]}"
-            
-            if metrics.get("error"):
+            if isinstance(metrics, dict) and metrics.get("error"):
                 return result + f"❌ {metrics['error']}"
             
-            annual_data = [m for m in metrics['metrics'] if 'Q' not in m['period']][:5]
+            annual_data = [m for m in metrics.get('metrics', []) if 'Q' not in m.get('period', '')][:5]
             
             result += f"## 5年财务趋势\n\n"
             result += "| 期间 | 收入 | 净利润 | EPS |\n"

requirements.txt

@@ -1,2 +1,3 @@
 gradio>=4.0.0
-requests
+requests
+gradio_client