streamlit_app.py

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()