timeout

agent.py

@@ -7,6 +7,7 @@ from state import AgentState
 from typing import Any, Dict, List, Optional
 import json
 from langgraph.prebuilt import create_react_agent
+import signal
 
 # ─────────────────────────── External tools ──────────────────────────────
 from tools import (
@@ -20,7 +21,13 @@ from tools import (
 
 # ─────────────────────────── Configuration ───────────────────────────────
 MAX_TOOL_CALLS = 5
+AGENT_TIMEOUT = 300  # 5 minutes timeout for agent execution
 
+class TimeoutError(Exception):
+    pass
+
+def timeout_handler(signum, frame):
+    raise TimeoutError("Agent execution timed out")
 
 # ─────────────────────────── Helper utilities ────────────────────────────
 
@@ -34,8 +41,13 @@ MAX_TOOL_CALLS = 5
 # ─────────────────────────── Graph wiring ───────────────────────────────
 
 def build_graph():
-    """Build and return a create_react_agent."""
-    llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0.3)
+    """Build and return a create_react_agent with improved configuration."""
+    llm = ChatOpenAI(
+        model_name="gpt-4o-mini", 
+        temperature=0.1,  # Lower temperature for more consistent responses
+        max_tokens=2000,  # Ensure reasonable response length
+        timeout=60  # 1 minute timeout for LLM calls
+    )
 
     llm_tools = [
         wikipedia_search_tool,

app.py

@@ -5,6 +5,7 @@ import requests
 import pandas as pd
 from langchain.schema import HumanMessage, SystemMessage
 from typing import Optional
+import re
 
 from agent import build_graph
 from state import AgentState
@@ -15,6 +16,14 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 SYSTEM_PROMPT = """
 You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
 
+CRITICAL REQUIREMENT - ALWAYS PROVIDE AN ANSWER:
+- You MUST always provide a FINAL ANSWER, no matter what happens
+- If tools fail, provide the best answer you can based on your knowledge
+- If information is incomplete, make a reasonable inference or educated guess
+- If you cannot find specific information, provide a general or approximate answer
+- Never say "I cannot answer" or "I don't know" - always attempt to provide some form of answer
+- Even if uncertain, provide your best estimate and acknowledge the uncertainty if needed
+
 IMPORTANT: When using tools that require file access (such as audio_transcriber_tool, excel_tool, analyze_code_tool, or image_tool), ALWAYS use the task_id parameter only. Do NOT use any file names mentioned by the user - ignore them completely and only pass the task_id.
 
 SEARCH STRATEGY: 
@@ -33,6 +42,12 @@ RECURSION LIMIT HANDLING:
 - Do NOT say you cannot answer due to recursion limits - always provide the best answer possible with available information
 - If you have partial information, use it to make a reasonable inference or educated guess
 - Better to provide an approximate answer than no answer at all
+
+ERROR HANDLING:
+- If any tool fails, acknowledge the failure but continue with your analysis
+- Use your existing knowledge to compensate for failed tool calls
+- Always end with a FINAL ANSWER regardless of tool failures or errors
+- Frame uncertain answers appropriately but still provide them
 """
 
 
@@ -41,44 +56,115 @@ class BasicAgent:
         print("BasicAgent initialized.")
         self.graph = build_graph()
 
-    def __call__(self, question: str, task_id: Optional[str] = None) -> str:
-        """Run the agent and return whatever FINAL_ANSWER the graph produces."""
-        print(f"Agent received question: {question}")
+    def extract_final_answer(self, content: str) -> str:
+        """Extract final answer from content with multiple fallback strategies."""
+        if not content:
+            return "No content generated"
+            
+        # Strategy 1: Look for FINAL ANSWER: pattern (case insensitive)
+        final_answer_patterns = [
+            r'FINAL ANSWER:\s*(.+?)(?:\n|$)',
+            r'Final Answer:\s*(.+?)(?:\n|$)', 
+            r'final answer:\s*(.+?)(?:\n|$)',
+            r'Answer:\s*(.+?)(?:\n|$)',
+            r'ANSWER:\s*(.+?)(?:\n|$)'
+        ]
         
-        # Create system prompt with task_id included
-        system_prompt_with_task = SYSTEM_PROMPT
-        if task_id:
-            system_prompt_with_task += f"\n\nIMPORTANT: Your current task_id is: {task_id}. When using any tools that require a task_id parameter (audio_transcriber_tool, excel_tool, analyze_code_tool, image_tool), use this exact task_id: {task_id}"
+        for pattern in final_answer_patterns:
+            match = re.search(pattern, content, re.IGNORECASE | re.DOTALL)
+            if match:
+                answer = match.group(1).strip()
+                if answer:
+                    return answer
         
-        # Initialize the state properly with all required fields
-        init_state = {
-            "messages": [
-                SystemMessage(content=system_prompt_with_task),
-                HumanMessage(content=question)
-            ]
-        }
+        # Strategy 2: Look for the last meaningful sentence/paragraph
+        # Split by sentences and take the last non-empty one
+        sentences = [s.strip() for s in content.split('.') if s.strip()]
+        if sentences:
+            last_sentence = sentences[-1]
+            # If it's too long, truncate it
+            if len(last_sentence) > 200:
+                last_sentence = last_sentence[:200] + "..."
+            return last_sentence
         
-        # IMPORTANT: invoke() returns a **new** state instance (or an AddableValuesDict),
-        # not the object we pass in.  Use the returned value to fetch final_answer.
-        out_state = self.graph.invoke(init_state, {"recursion_limit": 8})
-        # print("out_state: ", out_state,'\n\n\n\n')
+        # Strategy 3: Take the last line that's not empty
+        lines = [line.strip() for line in content.split('\n') if line.strip()]
+        if lines:
+            return lines[-1]
         
+        # Strategy 4: Return truncated content as fallback
+        return content[:200] + "..." if len(content) > 200 else content
+
+    def __call__(self, question: str, task_id: Optional[str] = None) -> str:
+        """Run the agent and return whatever FINAL_ANSWER the graph produces."""
+        print(f"Agent received question: {question}")
         
-        # Extract the final answer from the last message
-        if out_state and "messages" in out_state:
-            last_message = out_state["messages"][-1]
-            if hasattr(last_message, 'content'):
-                content = last_message.content
-                # Look for FINAL ANSWER: pattern
-                print("content: ", content)
-                print("\n\n\n\n")
-                if "FINAL ANSWER:" in content:
-                    final_answer = content.split("FINAL ANSWER:")[-1].strip()
+        try:
+            # Create system prompt with task_id included
+            system_prompt_with_task = SYSTEM_PROMPT
+            if task_id:
+                system_prompt_with_task += f"\n\nIMPORTANT: Your current task_id is: {task_id}. When using any tools that require a task_id parameter (audio_transcriber_tool, excel_tool, analyze_code_tool, image_tool), use this exact task_id: {task_id}"
+            
+            # Initialize the state properly with all required fields
+            init_state = {
+                "messages": [
+                    SystemMessage(content=system_prompt_with_task),
+                    HumanMessage(content=question)
+                ]
+            }
+            
+            # Add timeout and better error handling
+            try:
+                # Set up timeout for agent execution
+                import signal
+                
+                def timeout_handler(signum, frame):
+                    raise TimeoutError("Agent execution exceeded time limit")
+                
+                signal.signal(signal.SIGALRM, timeout_handler)
+                signal.alarm(180)  # 3 minute timeout
+                
+                try:
+                    out_state = self.graph.invoke(init_state, {"recursion_limit": 8})
+                finally:
+                    signal.alarm(0)  # Clear the alarm
+                    
+            except TimeoutError:
+                print("Agent execution timed out")
+                return f"I need to provide a quick answer for '{question}' due to time constraints. Based on general knowledge, this question would likely require research into the specific topic. Please consider consulting reliable sources for the most accurate information."
+            except Exception as e:
+                print(f"Graph execution error: {e}")
+                # Fallback: try with simpler approach
+                return f"Based on the question '{question}', I cannot provide a complete analysis due to technical limitations. However, I would recommend researching this topic further for a comprehensive answer."
+            
+            # Extract the final answer from the last message
+            if out_state and "messages" in out_state and out_state["messages"]:
+                last_message = out_state["messages"][-1]
+                
+                if hasattr(last_message, 'content') and last_message.content:
+                    content = last_message.content
+                    print("content: ", content)
+                    print("\n\n\n\n")
+                    
+                    final_answer = self.extract_final_answer(content)
                     return final_answer
                 else:
-                    return content
-        
-        return "No answer generated."
+                    # If last message has no content, look at previous messages
+                    for message in reversed(out_state["messages"]):
+                        if hasattr(message, 'content') and message.content and hasattr(message, 'type'):
+                            if message.type == 'ai':  # Only look at AI messages
+                                final_answer = self.extract_final_answer(message.content)
+                                if final_answer and final_answer != "No content generated":
+                                    return final_answer
+            
+            # If we can't extract from messages, provide a fallback response
+            print("No valid content found in messages, providing fallback response")
+            return f"I was unable to provide a complete answer to the question: '{question}'. This may require additional research or clarification."
+            
+        except Exception as e:
+            print(f"Unexpected error in agent execution: {e}")
+            # Always provide some form of answer, even if there's an error
+            return f"I encountered an error while processing the question: '{question}'. The issue appears to be technical in nature. Please try rephrasing the question or contact support if the problem persists."
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):

tools.py

@@ -14,6 +14,20 @@ from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
+def safe_tool_execution(tool_func):
+    """Decorator to ensure tools always return a useful response."""
+    def wrapper(*args, **kwargs):
+        try:
+            result = tool_func(*args, **kwargs)
+            if result and result.strip():
+                return result
+            else:
+                return f"Tool {tool_func.__name__} completed but returned no content. Please try a different approach or use general knowledge."
+        except Exception as e:
+            print(f"Error in {tool_func.__name__}: {e}")
+            return f"Tool {tool_func.__name__} encountered an error: {str(e)}. Please continue with available information or try an alternative approach."
+    return wrapper
+
 def _download_file_for_task(task_id: str, ext: str) -> str:
     """
     Helper: attempt to GET the remote file for a given task_id.
@@ -41,6 +55,7 @@ def _download_file_for_task(task_id: str, ext: str) -> str:
     return ""
 
 @tool
+@safe_tool_execution
 def image_tool(task_id: str) -> str:
     """
     Expects: task_id (str) — a valid image task ID.
@@ -107,6 +122,7 @@ def image_tool(task_id: str) -> str:
 
 
 @tool
+@safe_tool_execution
 def excel_tool(task_id: str) -> str:
     """
     Downloads <task_id>.xlsx (if any) and returns a stringified list of
@@ -137,6 +153,7 @@ def excel_tool(task_id: str) -> str:
 
 import openai
 @tool
+@safe_tool_execution
 def audio_transcriber_tool(task_id: str) -> str:
     """
     LangGraph tool for transcribing audio via OpenAI's Whisper API.
@@ -184,6 +201,7 @@ import re
 import requests
 
 @tool
+@safe_tool_execution
 def wikipedia_search_tool(wiki_query: str) -> str:
     """
     Searches Wikipedia for the given query and returns the first 5 pages.
@@ -248,6 +266,7 @@ def wikipedia_search_tool(wiki_query: str) -> str:
         return error_msg
     
 @tool
+@safe_tool_execution
 def arxiv_search_tool(arxiv_query: str) -> str:
     """
     Searches Arxiv for the given query and returns the first 5 pages.
@@ -317,6 +336,7 @@ from langchain.schema import SystemMessage, HumanMessage
 LLM = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0.2)
 
 @tool
+@safe_tool_execution
 def analyze_code_tool(task_id: str) -> str:
     """
     Either task_id  OR  (file + task_id)