Create app.py

app.py

@@ -0,0 +1,403 @@
+import streamlit as st
+import openai
+import requests
+import json
+import asyncio
+import aiohttp
+from typing import Dict, Any, List
+from datetime import datetime
+import os
+
+# Page configuration
+st.set_page_config(
+    page_title="AI Assistant with SAP & News Integration",
+    page_icon="🤖",
+    layout="wide"
+)
+
+# Custom CSS for better UI
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: bold;
+        text-align: center;
+        color: #1f77b4;
+        margin-bottom: 2rem;
+    }
+    .chat-message {
+        padding: 1rem;
+        border-radius: 0.5rem;
+        margin: 0.5rem 0;
+    }
+    .user-message {
+        background-color: #e3f2fd;
+        border-left: 4px solid #2196f3;
+    }
+    .assistant-message {
+        background-color: #f5f5f5;
+        border-left: 4px solid #4caf50;
+    }
+    .tool-result {
+        background-color: #fff3e0;
+        border: 1px solid #ff9800;
+        border-radius: 0.5rem;
+        padding: 1rem;
+        margin: 1rem 0;
+    }
+    .error-message {
+        background-color: #ffebee;
+        border: 1px solid #f44336;
+        border-radius: 0.5rem;
+        padding: 1rem;
+        margin: 1rem 0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+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"]:
+                        # Extract the actual data from MCP response
+                        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:
+            st.error(f"Error listing tools: {str(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.
+
+Always explain what you're doing and present results clearly. If a tool call fails, explain the error and suggest alternatives.
+
+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:
+                    # Parse tool call: CALL_TOOL: tool_name(param1=value1, param2=value2)
+                    tool_part = line.strip()[10:].strip()  # Remove 'CALL_TOOL:'
+                    
+                    if '(' in tool_part and ')' in tool_part:
+                        tool_name = tool_part.split('(')[0].strip()
+                        params_str = tool_part.split('(')[1].split(')')[0]
+                        
+                        # Parse parameters
+                        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 to convert to appropriate type
+                                    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:
+                    st.error(f"Error parsing tool call: {e}")
+        
+        return tool_calls
+    
+    async def process_message(self, user_message: str) -> str:
+        """Process user message and handle tool calls"""
+        try:
+            # First, get AI response to understand what tools to call
+            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
+            
+            # Check if AI wants to call any tools
+            tool_calls = self.extract_tool_calls(ai_response)
+            
+            if tool_calls:
+                tool_results = []
+                
+                for tool_call in tool_calls:
+                    st.info(f"🔧 Calling tool: {tool_call['name']} with parameters: {tool_call['arguments']}")
+                    
+                    result = await self.mcp_client.call_tool(
+                        tool_call['name'], 
+                        tool_call['arguments']
+                    )
+                    
+                    tool_results.append({
+                        'tool': tool_call['name'],
+                        'result': result
+                    })
+                    
+                    # Display tool result
+                    if result.get('success'):
+                        st.success(f"✅ Tool {tool_call['name']} executed successfully")
+                        with st.expander(f"📊 {tool_call['name']} Results", expanded=False):
+                            st.json(result)
+                    else:
+                        st.error(f"❌ Tool {tool_call['name']} failed: {result.get('error', 'Unknown error')}")
+                
+                # Get final response with tool results
+                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
+            
+            else:
+                return ai_response
+                
+        except Exception as e:
+            return f"❌ Error processing your request: {str(e)}"
+
+# Streamlit App
+def main():
+    st.markdown('<h1 class="main-header">🤖 AI Assistant with SAP & News Integration</h1>', unsafe_allow_html=True)
+    
+    # Sidebar for configuration
+    with st.sidebar:
+        st.header("⚙️ Configuration")
+        
+        # OpenAI API Key
+        openai_api_key = st.text_input(
+            "OpenAI API Key",
+            type="password",
+            help="Enter your OpenAI API key"
+        )
+        
+        # MCP Server URL
+        mcp_server_url = st.text_input(
+            "MCP Server URL",
+            value="https://your-ngrok-url.ngrok.io",
+            help="Enter your ngrok URL where the MCP server is running"
+        )
+        
+        # Test connection button
+        if st.button("🔍 Test MCP Connection"):
+            if mcp_server_url:
+                try:
+                    response = requests.get(f"{mcp_server_url.rstrip('/')}/health", timeout=10)
+                    if response.status_code == 200:
+                        st.success("✅ MCP Server connected successfully!")
+                        st.json(response.json())
+                    else:
+                        st.error(f"❌ Connection failed: HTTP {response.status_code}")
+                except Exception as e:
+                    st.error(f"❌ Connection error: {str(e)}")
+            else:
+                st.error("Please enter MCP Server URL")
+        
+        st.markdown("---")
+        st.markdown("### 📋 Available Commands")
+        st.markdown("""
+        - **SAP Purchase Orders**: "Show me recent purchase orders"
+        - **SAP Requisitions**: "Get purchase requisitions"
+        - **News Headlines**: "What's the latest tech news?"
+        - **News by Source**: "Get news from BBC"
+        """)
+    
+    # Main chat interface
+    if not openai_api_key:
+        st.warning("⚠️ Please enter your OpenAI API key in the sidebar to continue.")
+        return
+    
+    if not mcp_server_url or mcp_server_url == "https://your-ngrok-url.ngrok.io":
+        st.warning("⚠️ Please enter your MCP server URL in the sidebar.")
+        return
+    
+    # Initialize session state
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+    
+    if 'assistant' not in st.session_state:
+        mcp_client = MCPClient(mcp_server_url)
+        st.session_state.assistant = AIAssistant(openai_api_key, mcp_client)
+        
+        # Initialize assistant
+        async def init_assistant():
+            await st.session_state.assistant.initialize()
+        
+        try:
+            asyncio.run(init_assistant())
+            st.success("🚀 AI Assistant initialized successfully!")
+        except Exception as e:
+            st.error(f"❌ Failed to initialize assistant: {str(e)}")
+            return
+    
+    # Display chat messages
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    # Chat input
+    if prompt := st.chat_input("Ask me about SAP data, news, or anything else..."):
+        # Add user message
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        # Get assistant response
+        with st.chat_message("assistant"):
+            with st.spinner("🤔 Thinking and processing..."):
+                try:
+                    response = asyncio.run(
+                        st.session_state.assistant.process_message(prompt)
+                    )
+                    st.markdown(response)
+                    
+                    # Add assistant response to history
+                    st.session_state.messages.append({"role": "assistant", "content": response})
+                    
+                except Exception as e:
+                    error_msg = f"❌ Sorry, I encountered an error: {str(e)}"
+                    st.error(error_msg)
+                    st.session_state.messages.append({"role": "assistant", "content": error_msg})
+    
+    # Footer
+    st.markdown("---")
+    st.markdown(
+        "💡 **Tip**: Try asking about purchase orders, requisitions, or latest news. "
+        "The AI will automatically use the appropriate tools to fetch the data."
+    )
+
+if __name__ == "__main__":
+    main()