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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"
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
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() |