simplify

agent.py

@@ -21,13 +21,6 @@ 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 ────────────────────────────
 
@@ -41,13 +34,8 @@ def timeout_handler(signum, frame):
 # ─────────────────────────── Graph wiring ───────────────────────────────
 
 def build_graph():
-    """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
-    )
+    """Build and return a create_react_agent."""
+    llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0.3)
 
     llm_tools = [
         wikipedia_search_tool,

app.py

@@ -16,38 +16,9 @@ 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: 
-- If wikipedia_search_tool fails or returns insufficient/irrelevant results, try these fallback strategies:
-  1. Try wikipedia_search_tool again with a broader, more general query (remove specific terms, use synonyms)
-  2. If Wikipedia still doesn't help, try arxiv_search_tool for academic/research topics
-  3. You can use multiple search attempts with different keywords to find better information
-- Always evaluate if the search results are relevant and sufficient before proceeding to your final answer
-- IMPORTANT: When you see [END_OF_SEARCH] in tool results, this means the search is complete and you have all available information
-- Do NOT perform additional searches after seeing [END_OF_SEARCH] - immediately proceed to analyze the provided information and give your final answer
-- The [END_OF_SEARCH] marker indicates you should stop searching and work with what you have
-
-RECURSION LIMIT HANDLING:
-- You have a maximum of 8 steps to complete your task
-- If you reach the recursion limit, you MUST provide a final answer based on whatever information you have gathered so far
-- 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
+You MUST always provide a FINAL ANSWER. If you cannot find complete information, provide your best estimate based on available information.
 """
 
 
@@ -56,115 +27,42 @@ class BasicAgent:
         print("BasicAgent initialized.")
         self.graph = build_graph()
 
-    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|$)'
-        ]
-        
-        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
-        
-        # 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
-        
-        # 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}")
         
-        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]
+        # 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)
+            ]
+        }
+        
+        # Run the agent
+        out_state = self.graph.invoke(init_state, {"recursion_limit": 8})
+        
+        # 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
+                print("content: ", content)
+                print("\n\n\n\n")
                 
-                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)
+                # Look for FINAL ANSWER: pattern
+                if "FINAL ANSWER:" in content:
+                    final_answer = content.split("FINAL ANSWER:")[-1].strip()
                     return final_answer
                 else:
-                    # 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."
+                    return content
+        
+        return "No answer generated."
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):

tools.py

@@ -14,22 +14,7 @@ from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
-import functools
-
-def safe_tool_execution(tool_func):
-    """Decorator to ensure tools always return a useful response."""
-    @functools.wraps(tool_func)
-    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
+# Removed complex safety wrapper - keeping things simple
 
 def _download_file_for_task(task_id: str, ext: str) -> str:
     """
@@ -58,7 +43,6 @@ 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.
@@ -125,7 +109,6 @@ 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
@@ -156,7 +139,6 @@ 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.
@@ -204,7 +186,6 @@ 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.
@@ -269,7 +250,6 @@ 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.
@@ -339,7 +319,6 @@ 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)