Update app.py

app.py

@@ -3,11 +3,19 @@ import requests
 from smolagents import CodeAgent
 import json
 import re
+import logging
+from tenacity import retry, stop_after_attempt, wait_exponential
 
-HF_SPACE_URL = "https://manavraj-troubleshoot_mcp.hf.space"
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
+# Use lowercase and verify your exact Hugging Face Space URL
+HF_SPACE_URL = "https://manavraj-troubleshoot-mcp.hf.space"  # Note: Changed underscores to hyphens
+
+@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 your MCP server and let it handle tool selection"""
+    """Call your MCP server with retries and proper error handling"""
     try:
         # Determine which tool to use based on tool_type
         fn_index_map = {
@@ -18,10 +26,26 @@ def call_mcp_server(message, tool_type="knowledge_base"):
         
         fn_index = fn_index_map.get(tool_type, 0)
         
-        response = requests.post(f"{HF_SPACE_URL}/api/predict", 
-                               json={"data": [message], "fn_index": fn_index})
-        return response.json().get("data", ["No response"])[0]
+        logger.info(f"Calling MCP server with message: {message[:50]}...")
+        
+        response = requests.post(
+            f"{HF_SPACE_URL}/api/predict",
+            json={"data": [message], "fn_index": fn_index},
+            verify=False,  # Temporarily disable SSL verification
+            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 server call failed: {str(e)}")
         return f"MCP server error: {str(e)}"
 
 def extract_thought_action_observation(response):
@@ -51,45 +75,33 @@ agent = CodeAgent(
     tools=[],
     model="microsoft/DialoGPT-medium",
     system_prompt="""You are a Technical Support AI Agent that uses a structured Thought-Action-Observation cycle to solve user queries effectively.
-
 WORKFLOW:
 For every user query, you MUST follow this exact structure:
-
 THOUGHT: Analyze the user's problem and decide what action to take
 - Identify the type of issue (technical problem, information request, formatting need)
 - Determine which MCP server tool would be most appropriate
 - Consider what information you need to provide a complete answer
-
 ACTION: Specify exactly what action you will take
 - "search_knowledge_base" for known technical issues (wifi, screen, hardware problems)
 - "web_search" for current information, news, or unknown topics
 - "format_response" for organizing or structuring information
 - "direct_response" if you can answer without tools
-
 OBSERVATION: Analyze the results and determine next steps
 - Evaluate if the action provided sufficient information
 - Identify if additional actions are needed
 - Note any gaps in the information
-
 FINAL RESPONSE: Provide a clear, helpful answer to the user
-
 EXAMPLE:
 User: "My wifi keeps disconnecting"
-
 THOUGHT: The user has a wifi connectivity issue. This is a common technical problem that likely has solutions in our knowledge base. I should search the knowledge base first for wifi-related solutions.
-
 ACTION: search_knowledge_base with query "wifi disconnecting problem"
-
 OBSERVATION: The knowledge base provided basic wifi troubleshooting steps. These steps are helpful but I should also check for any recent wifi issues or advanced solutions via web search to provide comprehensive help.
-
 FINAL RESPONSE: [Provide complete solution combining knowledge base and any additional insights]
-
 Remember:
 - Always use the THOUGHT-ACTION-OBSERVATION-FINAL RESPONSE structure
 - Be specific about which tool you're using and why
 - Provide actionable, clear solutions
 - If one action isn't sufficient, explain what additional actions you'll take
-
 {{managed_agents_descriptions}}
 {{authorized_imports}}"""
 )
@@ -119,18 +131,20 @@ def determine_tool_type(message):
 def chat_interface(message, history):
     """Enhanced chat interface with thought-action-observation cycle"""
     
-    # Step 1: Let agent think about the problem
-    thinking_prompt = f"""
-    User Query: "{message}"
-    
-    Follow the THOUGHT-ACTION-OBSERVATION cycle:
-    
-    THOUGHT: Analyze this query and determine the best approach.
-    """
-    
     try:
+        # Step 1: Let agent think about the problem
+        thinking_prompt = f"""
+        User Query: "{message}"
+        
+        Follow the THOUGHT-ACTION-OBSERVATION cycle:
+        
+        THOUGHT: Analyze this query and determine the best approach.
+        """
+        
         # Get agent's initial thought process
         agent_response = agent.run(thinking_prompt)
+        if not isinstance(agent_response, str):
+            raise ValueError("Agent returned non-string response")
         
         # Extract thought and determine action
         cycle_parts = extract_thought_action_observation(agent_response)
@@ -155,30 +169,27 @@ def chat_interface(message, history):
         """
         
         final_response = agent.run(final_prompt)
+        if not isinstance(final_response, str):
+            final_response = str(final_response)
         
         # Format the response to show the thinking process
         formatted_response = f"""🤔 **THOUGHT:** {cycle_parts.get('thought', 'Analyzing your query...')}
-
 ⚡ **ACTION:** Used {tool_type.replace('_', ' ')} tool
-
 👁️ **OBSERVATION:** {mcp_response[:200]}{'...' if len(mcp_response) > 200 else ''}
-
 ✅ **SOLUTION:**
 {final_response}"""
         
         return formatted_response
         
     except Exception as e:
+        logger.error(f"Error in chat interface: {str(e)}")
         # Fallback to direct MCP response with simple structure
         tool_type = determine_tool_type(message)
         mcp_response = call_mcp_server(message, tool_type)
         
         return f"""🤔 **THOUGHT:** You have a {tool_type.replace('_', ' ')} related query.
-
 ⚡ **ACTION:** Consulted the MCP server using {tool_type} tool.
-
 👁️ **OBSERVATION:** Found relevant information.
-
 ✅ **SOLUTION:**
 {mcp_response}"""