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| import gradio as gr | |
| import requests | |
| from smolagents import CodeAgent | |
| import logging | |
| from tenacity import retry, stop_after_attempt, wait_exponential | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Correct URL based on your Space | |
| HF_SPACE_URL = "https://manavraj-troubleshoot-mcp.hf.space" | |
| def call_mcp_server(message, tool_type="knowledge_base"): | |
| """Call MCP server with proper endpoint routing""" | |
| try: | |
| endpoint_map = { | |
| "knowledge_base": "api/knowledge_base", | |
| "web_search": "api/web_search", | |
| "formatter": "api/formatter" | |
| } | |
| endpoint = endpoint_map.get(tool_type, "api/knowledge_base") | |
| url = f"{HF_SPACE_URL}/{endpoint}" | |
| response = requests.post( | |
| url, | |
| json={"data": [message]}, | |
| timeout=30 | |
| ) | |
| if response.status_code != 200: | |
| raise Exception(f"MCP server returned {response.status_code}") | |
| data = response.json() | |
| if not isinstance(data, dict) or 'data' not in data: | |
| raise Exception("Invalid MCP response format") | |
| return data['data'][0] | |
| except Exception as e: | |
| logger.error(f"MCP call failed: {str(e)}") | |
| return f"Error: {str(e)}" | |
| # Initialize CodeAgent with optimized system prompt | |
| agent = CodeAgent( | |
| tools=[], # No tools defined here as they're in MCP | |
| model="microsoft/DialoGPT-medium", | |
| system_prompt="""You are an advanced Technical Support Assistant with these capabilities: | |
| 1. Troubleshooting technical issues (wifi, hardware, software) | |
| 2. Finding information through web search | |
| 3. Formatting instructions into clear steps | |
| Tools available via MCP server: | |
| - knowledge_base: For technical issues (wifi, screen, sound problems) | |
| - web_search: For finding latest information or non-technical queries | |
| - formatter: To organize raw steps into numbered lists | |
| Response Guidelines: | |
| 1. INTERNALLY follow THOUGHT/ACTION/OBSERVATION cycle | |
| 2. Use knowledge_base for technical troubleshooting | |
| 3. Use web_search for information lookup | |
| 4. Use formatter when user provides unorganized steps | |
| 5. Provide FINAL RESPONSE in clear, helpful language | |
| Example workflow: | |
| THOUGHT: User has wifi issue, should check knowledge base | |
| ACTION: knowledge_base | |
| OBSERVATION: Found 4 troubleshooting steps | |
| FINAL RESPONSE: Here's how to fix your wifi: [steps] | |
| Never show THOUGHT/ACTION/OBSERVATION to user - only final response.""" | |
| ) | |
| def chat_interface(message, history): | |
| """Simplified chat interface""" | |
| try: | |
| # Let agent handle the complete process | |
| response = agent.run(message) | |
| return str(response).split("FINAL RESPONSE:")[-1].strip() | |
| except Exception as e: | |
| logger.error(f"Chat error: {str(e)}") | |
| return f"Error processing request: {str(e)}" | |
| # Gradio interface | |
| demo = gr.ChatInterface( | |
| fn=chat_interface, | |
| title="🔧 Technical Support Agent", | |
| examples=[ | |
| "My wifi keeps disconnecting", | |
| "Find the latest Windows 11 update", | |
| "Format: Restart. Check connections. Update drivers" | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |