Create app.py

app.py

@@ -0,0 +1,370 @@
+import gradio as gr
+import openai
+import requests
+import json
+import asyncio
+import aiohttp
+from typing import Dict, Any, List, Tuple
+from datetime import datetime
+import os
+
+class MCPClient:
+    """MCP Client for communicating with the MCP server"""
+    
+    def __init__(self, server_url: str):
+        self.server_url = server_url.rstrip('/')
+        self.session = None
+    
+    async def initialize_session(self):
+        """Initialize aiohttp session"""
+        if not self.session:
+            self.session = aiohttp.ClientSession()
+    
+    async def close_session(self):
+        """Close aiohttp session"""
+        if self.session:
+            await self.session.close()
+            self.session = None
+    
+    async def call_tool(self, tool_name: str, arguments: Dict[str, Any] = None) -> Dict[str, Any]:
+        """Call a tool on the MCP server"""
+        if arguments is None:
+            arguments = {}
+        
+        await self.initialize_session()
+        
+        mcp_request = {
+            "jsonrpc": "2.0",
+            "id": 1,
+            "method": "tools/call",
+            "params": {
+                "name": tool_name,
+                "arguments": arguments
+            }
+        }
+        
+        try:
+            async with self.session.post(
+                f"{self.server_url}/mcp",
+                json=mcp_request,
+                headers={"Content-Type": "application/json"}
+            ) as response:
+                if response.status == 200:
+                    result = await response.json()
+                    if "result" in result and "content" in result["result"]:
+                        content = result["result"]["content"][0]["text"]
+                        return json.loads(content)
+                    return result
+                else:
+                    return {
+                        "success": False,
+                        "error": f"HTTP {response.status}: {await response.text()}"
+                    }
+        except Exception as e:
+            return {
+                "success": False,
+                "error": f"Connection error: {str(e)}"
+            }
+    
+    async def list_tools(self) -> List[Dict[str, Any]]:
+        """List available tools on the MCP server"""
+        await self.initialize_session()
+        
+        mcp_request = {
+            "jsonrpc": "2.0",
+            "id": 1,
+            "method": "tools/list"
+        }
+        
+        try:
+            async with self.session.post(
+                f"{self.server_url}/mcp",
+                json=mcp_request,
+                headers={"Content-Type": "application/json"}
+            ) as response:
+                if response.status == 200:
+                    result = await response.json()
+                    return result.get("result", {}).get("tools", [])
+                return []
+        except Exception as e:
+            return []
+
+class AIAssistant:
+    """AI Assistant with MCP integration"""
+    
+    def __init__(self, openai_api_key: str, mcp_client: MCPClient):
+        self.openai_client = openai.OpenAI(api_key=openai_api_key)
+        self.mcp_client = mcp_client
+        self.available_tools = []
+    
+    async def initialize(self):
+        """Initialize the assistant by fetching available tools"""
+        self.available_tools = await self.mcp_client.list_tools()
+    
+    def get_system_prompt(self) -> str:
+        """Generate system prompt with available tools"""
+        tools_description = "\n".join([
+            f"- {tool['name']}: {tool['description']}"
+            for tool in self.available_tools
+        ])
+        
+        return f"""You are an AI assistant with access to SAP business systems and news data through specialized tools. 
+
+Available tools:
+{tools_description}
+
+When a user asks for information that can be retrieved using these tools, you should:
+1. Identify which tool(s) would be helpful
+2. Call the appropriate tool(s) with the right parameters
+3. Interpret and present the results in a user-friendly way
+
+For SAP-related queries (purchase orders, requisitions), use the SAP tools.
+For news-related queries, use the news tools.
+
+You can call tools by responding with: CALL_TOOL: tool_name(parameter1=value1, parameter2=value2)
+"""
+    
+    def extract_tool_calls(self, response: str) -> List[Dict[str, Any]]:
+        """Extract tool calls from AI response"""
+        tool_calls = []
+        lines = response.split('\n')
+        
+        for line in lines:
+            if line.strip().startswith('CALL_TOOL:'):
+                try:
+                    tool_part = line.strip()[10:].strip()
+                    
+                    if '(' in tool_part and ')' in tool_part:
+                        tool_name = tool_part.split('(')[0].strip()
+                        params_str = tool_part.split('(')[1].split(')')[0]
+                        
+                        params = {}
+                        if params_str.strip():
+                            for param in params_str.split(','):
+                                if '=' in param:
+                                    key, value = param.split('=', 1)
+                                    key = key.strip()
+                                    value = value.strip().strip('"\'')
+                                    try:
+                                        if value.isdigit():
+                                            value = int(value)
+                                        elif value.lower() in ['true', 'false']:
+                                            value = value.lower() == 'true'
+                                    except:
+                                        pass
+                                    params[key] = value
+                        
+                        tool_calls.append({
+                            'name': tool_name,
+                            'arguments': params
+                        })
+                except Exception as e:
+                    continue
+        
+        return tool_calls
+    
+    async def process_message(self, user_message: str) -> Tuple[str, str]:
+        """Process user message and handle tool calls"""
+        tool_info = ""
+        
+        try:
+            messages = [
+                {"role": "system", "content": self.get_system_prompt()},
+                {"role": "user", "content": user_message}
+            ]
+            
+            response = self.openai_client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=messages,
+                temperature=0.7,
+                max_tokens=1000
+            )
+            
+            ai_response = response.choices[0].message.content
+            tool_calls = self.extract_tool_calls(ai_response)
+            
+            if tool_calls:
+                tool_results = []
+                
+                for tool_call in tool_calls:
+                    tool_info += f"🔧 Calling: {tool_call['name']}\n"
+                    
+                    result = await self.mcp_client.call_tool(
+                        tool_call['name'], 
+                        tool_call['arguments']
+                    )
+                    
+                    tool_results.append({
+                        'tool': tool_call['name'],
+                        'result': result
+                    })
+                    
+                    if result.get('success'):
+                        tool_info += f"✅ {tool_call['name']} completed\n"
+                    else:
+                        tool_info += f"❌ {tool_call['name']} failed: {result.get('error', 'Unknown error')}\n"
+                
+                tool_results_text = "\n\n".join([
+                    f"Tool: {tr['tool']}\nResult: {json.dumps(tr['result'], indent=2)}"
+                    for tr in tool_results
+                ])
+                
+                final_messages = messages + [
+                    {"role": "assistant", "content": ai_response},
+                    {"role": "user", "content": f"Here are the tool results:\n\n{tool_results_text}\n\nPlease interpret these results and provide a helpful response to the user."}
+                ]
+                
+                final_response = self.openai_client.chat.completions.create(
+                    model="gpt-3.5-turbo",
+                    messages=final_messages,
+                    temperature=0.7,
+                    max_tokens=1000
+                )
+                
+                return final_response.choices[0].message.content, tool_info
+            else:
+                return ai_response, ""
+                
+        except Exception as e:
+            return f"❌ Error processing your request: {str(e)}", ""
+
+# Global variables
+assistant = None
+mcp_client = None
+
+def test_connection(mcp_url):
+    """Test MCP server connection"""
+    if not mcp_url or mcp_url == "https://your-ngrok-url.ngrok.io":
+        return "❌ Please enter a valid MCP server URL"
+    
+    try:
+        response = requests.get(f"{mcp_url.rstrip('/')}/health", timeout=10)
+        if response.status_code == 200:
+            data = response.json()
+            return f"✅ Connected successfully!\nStatus: {data.get('status', 'Unknown')}\nTools: {len(data.get('available_tools', []))}"
+        else:
+            return f"❌ Connection failed: HTTP {response.status_code}"
+    except Exception as e:
+        return f"❌ Connection error: {str(e)}"
+
+async def initialize_assistant(openai_key, mcp_url):
+    """Initialize the AI assistant"""
+    global assistant, mcp_client
+    
+    if not openai_key:
+        return "❌ Please enter your OpenAI API key"
+    
+    if not mcp_url or mcp_url == "https://your-ngrok-url.ngrok.io":
+        return "❌ Please enter a valid MCP server URL"
+    
+    try:
+        mcp_client = MCPClient(mcp_url)
+        assistant = AIAssistant(openai_key, mcp_client)
+        await assistant.initialize()
+        return f"✅ AI Assistant initialized with {len(assistant.available_tools)} tools available"
+    except Exception as e:
+        return f"❌ Failed to initialize: {str(e)}"
+
+def chat_interface(message, history, openai_key, mcp_url):
+    """Main chat interface"""
+    global assistant
+    
+    if not assistant:
+        init_result = asyncio.run(initialize_assistant(openai_key, mcp_url))
+        if "❌" in init_result:
+            history.append([message, init_result])
+            return history, ""
+    
+    try:
+        response, tool_info = asyncio.run(assistant.process_message(message))
+        
+        # Format response with tool info if available
+        if tool_info:
+            full_response = f"**Tool Execution:**\n{tool_info}\n\n**Response:**\n{response}"
+        else:
+            full_response = response
+            
+        history.append([message, full_response])
+        return history, ""
+    except Exception as e:
+        error_response = f"❌ Error: {str(e)}"
+        history.append([message, error_response])
+        return history, ""
+
+# Create Gradio interface
+with gr.Blocks(title="AI Assistant with SAP & News Integration", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 AI Assistant with SAP & News Integration")
+    gr.Markdown("Chat with an AI that can access SAP business data and news through natural language queries.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(
+                height=500,
+                show_label=False,
+                container=True,
+                bubble_full_width=False
+            )
+            
+            msg = gr.Textbox(
+                placeholder="Ask me about SAP data, news, or anything else...",
+                show_label=False,
+                container=False
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button("Send", variant="primary")
+                clear_btn = gr.Button("Clear", variant="secondary")
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### ⚙️ Configuration")
+            
+            openai_key = gr.Textbox(
+                label="OpenAI API Key",
+                type="password",
+                placeholder="sk-..."
+            )
+            
+            mcp_url = gr.Textbox(
+                label="MCP Server URL",
+                value="https://your-ngrok-url.ngrok.io",
+                placeholder="https://abc123.ngrok.io"
+            )
+            
+            test_btn = gr.Button("Test Connection", variant="secondary")
+            connection_status = gr.Textbox(label="Connection Status", interactive=False)
+            
+            gr.Markdown("### 📋 Example Queries")
+            gr.Markdown("""
+            - "Show me recent purchase orders"
+            - "Get purchase requisitions"
+            - "What's the latest tech news?"
+            - "Get news from BBC"
+            - "Show me business news from the US"
+            """)
+    
+    # Event handlers
+    def respond(message, history, openai_key, mcp_url):
+        return chat_interface(message, history, openai_key, mcp_url)
+    
+    submit_btn.click(
+        respond,
+        [msg, chatbot, openai_key, mcp_url],
+        [chatbot, msg]
+    )
+    
+    msg.submit(
+        respond,
+        [msg, chatbot, openai_key, mcp_url],
+        [chatbot, msg]
+    )
+    
+    clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
+    
+    test_btn.click(
+        test_connection,
+        [mcp_url],
+        [connection_status]
+    )
+
+if __name__ == "__main__":
+    demo.launch()