Update agent.py

agent.py

@@ -1,10 +1,7 @@
 """
-agent.py – Simplified Claude implementation for GAIA challenge
+agent.py - Minimal Claude implementation for GAIA challenge
 -----------------------------------------------------------
-Environment
------------
-ANTHROPIC_API_KEY   – API key from Anthropic (set in Hugging Face space secrets)
-GAIA_API_URL     – (optional) override for the GAIA scoring endpoint
+A simplified implementation with direct litellm access to Anthropic's Claude
 """
 
 import base64
@@ -13,10 +10,11 @@ import os
 import re
 import tempfile
 import time
-from typing import List, Dict, Any, Optional
 import random
+from typing import List, Dict, Any, Optional
 import requests
 from urllib.parse import urlparse
+
 from smolagents import CodeAgent, DuckDuckGoSearchTool, PythonInterpreterTool, tool
 
 # --------------------------------------------------------------------------- #
@@ -35,156 +33,93 @@ def _download_file(file_id: str) -> bytes:
     return resp.content
 
 # --------------------------------------------------------------------------- #
-# Rate limiting helper
-# --------------------------------------------------------------------------- #
-class RateLimiter:
-    """Simple rate limiter to prevent Anthropic API rate limit errors"""
-    def __init__(self, requests_per_minute=20, burst=3):
-        self.requests_per_minute = requests_per_minute
-        self.burst = burst
-        self.request_times = []
-        
-    def wait(self):
-        """Wait if needed to avoid exceeding rate limits"""
-        now = time.time()
-        # Remove timestamps older than 1 minute
-        self.request_times = [t for t in self.request_times if now - t < 60]
-        
-        # If we've made too many requests in the last minute, wait
-        if len(self.request_times) >= self.requests_per_minute:
-            oldest = min(self.request_times)
-            sleep_time = 60 - (now - oldest) + 1  # +1 for safety
-            print(f"Rate limit approaching. Waiting {sleep_time:.2f} seconds before next request...")
-            time.sleep(sleep_time)
-        
-        # Add current timestamp to the list
-        self.request_times.append(time.time())
-        
-        # Add a small random delay to avoid bursts of requests
-        if len(self.request_times) > self.burst:
-            time.sleep(random.uniform(0.2, 1.0))
-
-# Global rate limiter instance
-RATE_LIMITER = RateLimiter(requests_per_minute=15)  # Reduced to be extra cautious
-
-# --------------------------------------------------------------------------- #
-# Direct function to call Claude via LiteLLM
-# --------------------------------------------------------------------------- #
-def call_claude(
-    prompt: str, 
-    system_prompt: Optional[str] = None, 
-    temperature: float = 0.1,
-    max_tokens: int = 1024,
-    model_name: str = "anthropic/claude-3-5-sonnet-20240620"
-) -> str:
-    """
-    Call Claude through LiteLLM directly, following official LiteLLM documentation
-    
-    Args:
-        prompt: The user's question
-        system_prompt: Optional system prompt
-        temperature: Temperature for generation
-        max_tokens: Max tokens to generate
-        model_name: Claude model to use
-    
-    Returns:
-        The response text from Claude
-    """
-    from litellm import completion
-    
-    # Respect rate limits
-    RATE_LIMITER.wait()
-    
-    try:
-        # Build messages following exactly LiteLLM's documented format
-        messages = []
-        
-        # Add system message if provided
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        
-        # Add user message - this is simple text only format
-        messages.append({"role": "user", "content": prompt})
-        
-        # Make the API call exactly as documented
-        response = completion(
-            model=model_name,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens
-        )
-        
-        # Extract just the text content from the response
-        return response.choices[0].message.content
-        
-    except Exception as e:
-        if "rate_limit" in str(e).lower():
-            print(f"Rate limit hit: {e}")
-            # Wait 60 seconds and try again
-            time.sleep(60)
-            return call_claude(prompt, system_prompt, temperature, max_tokens, model_name)
-        else:
-            print(f"Error calling Claude API: {e}")
-            raise
-
-# --------------------------------------------------------------------------- #
-# Simple Claude Model wrapper for smolagents
+# Direct Claude model implementation with litellm
 # --------------------------------------------------------------------------- #
-class SimpleClaudeModel:
+class DirectClaudeModel:
     """
-    A minimal wrapper around LiteLLM's direct call to Anthropic that works with smolagents
+    Direct interface to Claude via litellm that works with smolagents
+    This avoids the message format issues by keeping things very simple
     """
     
     def __init__(
-        self,
-        model_id: str = "anthropic/claude-3-5-sonnet-20240620",
+        self, 
         api_key: Optional[str] = None,
-        temperature: float = 0.1,
-        max_tokens: int = 1024,
-        system_prompt: Optional[str] = None,
+        temperature: float = 0.1
     ):
-        """Initialize a minimal Claude model wrapper"""
-        # Get API key from env if not provided
-        if api_key is None:
-            api_key = os.getenv("ANTHROPIC_API_KEY")
-            if not api_key:
-                raise ValueError("No Anthropic API key provided. Set ANTHROPIC_API_KEY env var.")
-                
-        self.model_id = model_id
-        self.api_key = api_key
+        """Initialize the Claude model"""
+        self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
+        if not self.api_key:
+            raise ValueError("No Anthropic API key provided")
+            
         self.temperature = temperature
-        self.max_tokens = max_tokens
+        self.model_name = "anthropic/claude-3-5-sonnet-20240620"
         
-        # Store the system prompt
-        self.system_prompt = system_prompt or """You are a concise, highly accurate assistant specialized in solving challenges. 
+        print(f"Initialized DirectClaudeModel with {self.model_name}")
+        
+        # Sleep random amount to avoid race conditions with many queries
+        time.sleep(random.uniform(1, 3))
+        
+    def __call__(self, prompt: str, **kwargs) -> str:
+        """
+        Simple call method that works with smolagents
+        
+        Args:
+            prompt: The user prompt
+            **kwargs: Additional parameters (ignored)
+            
+        Returns:
+            Claude's response as a string
+        """
+        # Import here to avoid any circular imports
+        from litellm import completion
+        
+        # Use a simple format: system message + user message
+        messages = [
+            {
+                "role": "system",
+                "content": """You are a concise, highly accurate assistant specialized in solving challenges.
 Your answers should be precise, direct, and exactly match the expected format.
 All answers are graded by exact string match, so format carefully!"""
+            },
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ]
         
-        print(f"Initialized SimpleClaudeModel with {model_id}")
-    
-    def __call__(self, prompt: str, **kwargs) -> str:
-        """Call method to make this class callable by smolagents CodeAgent"""
-        # Directly use the call_claude function
-        return call_claude(
-            prompt=prompt,
-            system_prompt=self.system_prompt,
-            temperature=self.temperature,
-            max_tokens=self.max_tokens,
-            model_name=self.model_id
-        )
+        # Add delay to avoid rate limits
+        time.sleep(random.uniform(0.5, 2.0))
+        
+        try:
+            # Make API call with simple format
+            response = completion(
+                model=self.model_name,
+                messages=messages,
+                temperature=self.temperature,
+                max_tokens=1024,
+                api_key=self.api_key
+            )
+            
+            # Extract and return the text content only
+            return response.choices[0].message.content
+        
+        except Exception as e:
+            # If it's a rate limit error, wait and retry
+            if "rate_limit" in str(e).lower():
+                print(f"Rate limit hit, waiting 30 seconds: {e}")
+                time.sleep(30)
+                return self.__call__(prompt, **kwargs)
+            else:
+                print(f"Error: {str(e)}")
+                raise
 
 # --------------------------------------------------------------------------- #
-# custom tool: fetch GAIA attachments
+# Custom tool: fetch GAIA attachments
 # --------------------------------------------------------------------------- #
 @tool
 def gaia_file_reader(file_id: str) -> str:
     """
-    Download a GAIA attachment and return its contents.
-    Args:
-        file_id: identifier that appears inside a <file:...> placeholder.
-    Returns:
-        base64-encoded string for binary files (images, PDFs, …) or decoded
-        UTF-8 text for textual files.
+    Download a GAIA attachment and return its contents
     """
     try:
         raw = _download_file(file_id)
@@ -196,21 +131,11 @@ def gaia_file_reader(file_id: str) -> str:
         return f"ERROR downloading {file_id}: {exc}"
 
 # --------------------------------------------------------------------------- #
-# additional tool functions
+# Additional tools
 # --------------------------------------------------------------------------- #
 @tool
 def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
-    """
-    Save content to a temporary file and return the path.
-    Useful for processing files from the GAIA API.
-    
-    Args:
-        content: The content to save to the file
-        filename: Optional filename, will generate a random name if not provided
-        
-    Returns:
-        Path to the saved file
-    """
+    """Save content to a file and return the path"""
     temp_dir = tempfile.gettempdir()
     if filename is None:
         temp_file = tempfile.NamedTemporaryFile(delete=False)
@@ -218,495 +143,173 @@ def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
     else:
         filepath = os.path.join(temp_dir, filename)
     
-    # Write content to the file
     with open(filepath, 'w') as f:
         f.write(content)
     
-    return f"File saved to {filepath}. You can read this file to process its contents."
-
-@tool
-def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
-    """
-    Download a file from a URL and save it to a temporary location.
-    
-    Args:
-        url: The URL to download from
-        filename: Optional filename, will generate one based on URL if not provided
-        
-    Returns:
-        Path to the downloaded file
-    """
-    try:
-        # Parse URL to get filename if not provided
-        if not filename:
-            path = urlparse(url).path
-            filename = os.path.basename(path)
-            if not filename:
-                # Generate a random name if we couldn't extract one
-                import uuid
-                filename = f"downloaded_{uuid.uuid4().hex[:8]}"
-        
-        # Create temporary file
-        temp_dir = tempfile.gettempdir()
-        filepath = os.path.join(temp_dir, filename)
-        
-        # Download the file
-        response = requests.get(url, stream=True)
-        response.raise_for_status()
-        
-        # Save the file
-        with open(filepath, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-        
-        return f"File downloaded to {filepath}. You can now process this file."
-    except Exception as e:
-        return f"Error downloading file: {str(e)}"
-
-@tool
-def extract_text_from_image(image_path: str) -> str:
-    """
-    Extract text from an image using pytesseract (if available).
-    
-    Args:
-        image_path: Path to the image file
-        
-    Returns:
-        Extracted text or error message
-    """
-    try:
-        # Try to import pytesseract
-        import pytesseract
-        from PIL import Image
-        
-        # Open the image
-        image = Image.open(image_path)
-        
-        # Extract text
-        text = pytesseract.image_to_string(image)
-        
-        return f"Extracted text from image:\n\n{text}"
-    except ImportError:
-        return "Error: pytesseract is not installed. Please install it with 'pip install pytesseract' and ensure Tesseract OCR is installed on your system."
-    except Exception as e:
-        return f"Error extracting text from image: {str(e)}"
+    return f"File saved to {filepath}."
 
 @tool
 def analyze_csv_file(file_path: str, query: str) -> str:
-    """
-    Analyze a CSV file using pandas and answer a question about it.
-    
-    Args:
-        file_path: Path to the CSV file
-        query: Question about the data
-        
-    Returns:
-        Analysis result or error message
-    """
+    """Analyze a CSV file with pandas"""
     try:
         import pandas as pd
-        
-        # Read the CSV file
         df = pd.read_csv(file_path)
         
-        # Run various analyses based on the query
         result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
         result += f"Columns: {', '.join(df.columns)}\n\n"
-        
-        # Add summary statistics
         result += "Summary statistics:\n"
         result += str(df.describe())
         
         return result
     except ImportError:
-        return "Error: pandas is not installed. Please install it with 'pip install pandas'."
+        return "Error: pandas is not installed."
     except Exception as e:
         return f"Error analyzing CSV file: {str(e)}"
 
 @tool
 def analyze_excel_file(file_path: str, query: str) -> str:
-    """
-    Analyze an Excel file using pandas and answer a question about it.
-    
-    Args:
-        file_path: Path to the Excel file
-        query: Question about the data
-        
-    Returns:
-        Analysis result or error message
-    """
+    """Analyze an Excel file with pandas"""
     try:
         import pandas as pd
-        
-        # Read the Excel file
         df = pd.read_excel(file_path)
         
-        # Run various analyses based on the query
         result = f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
         result += f"Columns: {', '.join(df.columns)}\n\n"
-        
-        # Add summary statistics
         result += "Summary statistics:\n"
         result += str(df.describe())
         
         return result
     except ImportError:
-        return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'."
+        return "Error: pandas and openpyxl are not installed."
     except Exception as e:
         return f"Error analyzing Excel file: {str(e)}"
 
 # --------------------------------------------------------------------------- #
-# GAIAAgent class
-# --------------------------------------------------------------------------- #
-class GAIAAgent:
-    def __init__(
-        self, 
-        api_key: Optional[str] = None,
-        temperature: float = 0.1,
-        verbose: bool = False,
-        max_tokens: int = 1024,
-    ):
-        """
-        Initialize a GAIAAgent with Claude model
-        
-        Args:
-            api_key: Anthropic API key (fetched from environment if not provided)
-            temperature: Temperature for text generation
-            verbose: Enable verbose logging
-            max_tokens: Maximum number of tokens to generate per response
-        """
-        # Set verbosity
-        self.verbose = verbose
-        
-        # System prompt for all Claude interactions
-        self.system_prompt = """You are a concise, highly accurate assistant specialized in solving challenges for the GAIA benchmark. 
-Unless explicitly required, reply with ONE short sentence.
-Your answers should be precise, direct, and exactly match the expected format.
-All answers are graded by exact string match, so format carefully!"""
-        
-        # Get API key
-        if api_key is None:
-            api_key = os.getenv("ANTHROPIC_API_KEY")
-            if not api_key:
-                raise ValueError("No Anthropic token provided. Please set ANTHROPIC_API_KEY environment variable.")
-            
-        if self.verbose:
-            print(f"Using Anthropic token: {api_key[:5]}...")
-                
-        # Initialize Claude model with our simplified wrapper
-        self.model = SimpleClaudeModel(
-            model_id="anthropic/claude-3-5-sonnet-20240620",  # Use Claude 3.5 Sonnet
-            api_key=api_key,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            system_prompt=self.system_prompt,
-        )
-            
-        if self.verbose:
-            print(f"Initialized model: SimpleClaudeModel - claude-3-5-sonnet-20240620")
-        
-        # Initialize default tools
-        self.tools = [
-            DuckDuckGoSearchTool(),
-            PythonInterpreterTool(),
-            save_and_read_file,
-            download_file_from_url,
-            analyze_csv_file,
-            analyze_excel_file,
-            gaia_file_reader
-        ]
-        
-        # Add extract_text_from_image if PIL and pytesseract are available
-        try:
-            import pytesseract
-            from PIL import Image
-            self.tools.append(extract_text_from_image)
-            if self.verbose:
-                print("Added image processing tool")
-        except ImportError:
-            if self.verbose:
-                print("Image processing libraries not available")
-            
-        if self.verbose:
-            print(f"Initialized with {len(self.tools)} tools")
-        
-        # Setup imports allowed
-        self.imports = ["pandas", "numpy", "datetime", "json", "re", "math", "os", "requests", "csv", "urllib"]
-            
-        # Initialize the CodeAgent
-        self.agent = CodeAgent(
-            tools=self.tools,
-            model=self.model,
-            additional_authorized_imports=self.imports,
-            executor_type="local",
-            verbosity_level=2 if self.verbose else 0
-        )
-        
-        if self.verbose:
-            print("Agent initialized and ready")
-    
-    def answer_question(self, question: str, task_file_path: Optional[str] = None) -> str:
-        """
-        Process a GAIA benchmark question and return the answer
-        
-        Args:
-            question: The question to answer
-            task_file_path: Optional path to a file associated with the question
-            
-        Returns:
-            The answer to the question
-        """
-        try:
-            if self.verbose:
-                print(f"Processing question: {question}")
-                if task_file_path:
-                    print(f"With associated file: {task_file_path}")
-            
-            # Create a context with file information if available
-            context = question
-            file_content = None
-            
-            # If there's a file, read it and include its content in the context
-            if task_file_path:
-                try:
-                    # Limit file content size to avoid token limits
-                    max_file_size = 8000  # Characters - reduced further to help with token limits
-                    with open(task_file_path, 'r', errors='ignore') as f:
-                        file_content = f.read(max_file_size)
-                        if len(file_content) >= max_file_size:
-                            file_content = file_content[:max_file_size] + "... [content truncated to prevent exceeding token limits]"
-                    
-                    # Determine file type from extension
-                    import os
-                    file_ext = os.path.splitext(task_file_path)[1].lower()
-                    
-                    context = f"""
-Question: {question}
-This question has an associated file. Here is the file content (it may be truncated):
-```{file_ext}
-{file_content}
-```
-Analyze the available file content to answer the question.
-"""
-                except Exception as file_e:
-                    try:
-                        # Try to read in binary mode
-                        with open(task_file_path, 'rb') as f:
-                            binary_content = f.read()
-                        
-                        # For image files
-                        if file_ext.lower() in ['.jpg', '.jpeg', '.png', '.gif', '.bmp']:
-                            context = f"""
-Question: {question}
-This question has an associated image file. Please use the extract_text_from_image tool to process it.
-File path: {task_file_path}
-"""
-                        else:
-                            context = f"""
-Question: {question}
-This question has an associated file at path: {task_file_path}
-This is a binary file. Use appropriate tools to analyze it.
-"""
-                    except Exception as binary_e:
-                        context = f"""
-Question: {question}
-This question has an associated file at path: {task_file_path}
-However, there was an error reading the file: {file_e}
-You can still try to answer the question based on the information provided.
-"""
-            
-            # Check for special cases that need specific formatting
-            # Reversed text questions
-            if question.startswith(".") or ".rewsna eht sa" in question:
-                context = f"""
-This question appears to be in reversed text. Here's the reversed version:
-{question[::-1]}
-Now answer the question above. Remember to format your answer exactly as requested.
-"""
-            
-            # Add a prompt to ensure precise answers but keep it concise
-            full_prompt = f"""{context}
-When answering, provide ONLY the precise answer requested. 
-Do not include explanations, steps, reasoning, or additional text.
-Be direct and specific. GAIA benchmark requires exact matching answers.
-Example: If asked "What is the capital of France?", respond just with "Paris".
-"""
-            
-            # Run the agent with the question
-            answer = self.agent.run(full_prompt)
-            
-            # Clean up the answer to ensure it's in the expected format
-            # Remove common prefixes that models often add
-            answer = self._clean_answer(answer)
-            
-            if self.verbose:
-                print(f"Generated answer: {answer}")
-                
-            return answer
-        except Exception as e:
-            error_msg = f"Error answering question: {e}"
-            if self.verbose:
-                print(error_msg)
-            return error_msg
-    
-    def _clean_answer(self, answer: any) -> str:
-        """
-        Clean up the answer to remove common prefixes and formatting
-        that models often add but that can cause exact match failures.
-        
-        Args:
-            answer: The raw answer from the model
-            
-        Returns:
-            The cleaned answer as a string
-        """
-        # Convert non-string types to strings
-        if not isinstance(answer, str):
-            # Handle numeric types (float, int)
-            if isinstance(answer, float):
-                # Format floating point numbers properly
-                # Check if it's an integer value in float form (e.g., 12.0)
-                if answer.is_integer():
-                    formatted_answer = str(int(answer))
-                else:
-                    # For currency values that might need formatting
-                    if abs(answer) >= 1000:
-                        formatted_answer = f"${answer:,.2f}"
-                    else:
-                        formatted_answer = str(answer)
-                return formatted_answer
-            elif isinstance(answer, int):
-                return str(answer)
-            else:
-                # For any other type
-                return str(answer)
-        
-        # Now we know answer is a string, so we can safely use string methods
-        # Normalize whitespace
-        answer = answer.strip()
-        
-        # Remove common prefixes and formatting that models add
-        prefixes_to_remove = [
-            "The answer is ", 
-            "Answer: ",
-            "Final answer: ",
-            "The result is ",
-            "To answer this question: ",
-            "Based on the information provided, ",
-            "According to the information: ",
-        ]
-        
-        for prefix in prefixes_to_remove:
-            if answer.startswith(prefix):
-                answer = answer[len(prefix):].strip()
-        
-        # Remove quotes if they wrap the entire answer
-        if (answer.startswith('"') and answer.endswith('"')) or (answer.startswith("'") and answer.endswith("'")):
-            answer = answer[1:-1].strip()
-        
-        return answer
-
-# --------------------------------------------------------------------------- #
-# ClaudeAgent class - Wrapper around GAIAAgent
+# ClaudeAgent - Main class for GAIA challenge
 # --------------------------------------------------------------------------- #
 class ClaudeAgent:
-    """Claude-enhanced agent for GAIA challenge"""
+    """A simplified Claude agent for the GAIA challenge"""
     
     def __init__(self):
-        # Try to initialize GAIAAgent with Claude
+        """Initialize the agent with Claude"""
         try:
             # Get API key
             api_key = os.getenv("ANTHROPIC_API_KEY")
             if not api_key:
                 raise ValueError("ANTHROPIC_API_KEY environment variable not found")
                 
-            print("✅ Initializing GAIAAgent with Claude")
+            print("✅ Initializing ClaudeAgent")
+            
+            # Create the model with direct implementation
+            model = DirectClaudeModel(api_key=api_key, temperature=0.1)
+            
+            # Set up tools
+            tools = [
+                DuckDuckGoSearchTool(),
+                PythonInterpreterTool(),
+                save_and_read_file,
+                analyze_csv_file,
+                analyze_excel_file,
+                gaia_file_reader
+            ]
             
-            # Create GAIAAgent instance
-            self.agent = GAIAAgent(
-                api_key=api_key,
-                temperature=0.1,      # Use low temperature for precise answers
-                verbose=True,         # Enable verbose logging
-                max_tokens=1024,      # Reduce max tokens to avoid hitting rate limits
+            # Create the CodeAgent
+            self.agent = CodeAgent(
+                tools=tools,
+                model=model,
+                additional_authorized_imports=["pandas", "numpy", "json", "re", "math"],
+                executor_type="local",
+                verbosity_level=2
             )
+            
+            print("Agent initialized successfully")
+            
         except Exception as e:
-            print(f"Error initializing GAIAAgent: {e}")
+            print(f"Error initializing ClaudeAgent: {e}")
             raise
     
     def __call__(self, question: str) -> str:
-        """
-        Process a GAIA question and return the answer
-        
-        Args:
-            question: The question to answer
-            
-        Returns:
-            The answer to the question
-        """
+        """Process a question and return the answer"""
         try:
-            print(f"Received question: {question[:100]}..." if len(question) > 100 else f"Received question: {question}")
-            
-            # Add delay between questions to respect rate limits
-            time.sleep(random.uniform(0.5, 2.0))
+            print(f"Processing question: {question[:100]}..." if len(question) > 100 else question)
             
-            # Detect reversed text
-            if question.startswith(".") or ".rewsna eht sa" in question:
-                print("Detected reversed text question")
-                # GAIAAgent handles reversed text internally
+            # Add a small delay between questions
+            time.sleep(random.uniform(1.0, 3.0))
             
-            # Detect if there's a file
+            # Handle file references
             file_match = re.search(r"<file:([^>]+)>", question)
             if file_match:
                 file_id = file_match.group(1)
-                print(f"Detected file reference: {file_id}")
+                print(f"Detected file: {file_id}")
                 
-                # Download the file
+                # Download file
                 try:
                     file_content = _download_file(file_id)
-                    
-                    # Create temporary file for the file
                     temp_dir = tempfile.gettempdir()
                     file_path = os.path.join(temp_dir, file_id)
                     
-                    # Save file content
                     with open(file_path, 'wb') as f:
                         f.write(file_content)
                     
-                    print(f"File downloaded to: {file_path}")
-                    
                     # Remove file tag from question
                     clean_question = re.sub(r"<file:[^>]+>", "", question).strip()
                     
-                    # Process question with file path
-                    answer = self.agent.answer_question(clean_question, file_path)
-                    return self._clean_answer(answer)
+                    # Build prompt with file context
+                    prompt = f"""
+Question: {clean_question}
+There is a file available at path: {file_path}
+Use appropriate tools to analyze this file if needed.
+Answer the question directly and precisely.
+"""
                 except Exception as e:
-                    print(f"Error processing file: {e}")
-                    # Fall back to processing without file
+                    print(f"Error downloading file: {e}")
+                    prompt = question
+            else:
+                # Handle reversed text separately
+                if question.startswith(".") or ".rewsna eht sa" in question:
+                    prompt = f"""
+This question is in reversed text. Here's the normal version:
+{question[::-1]}
+Answer the question directly and precisely.
+"""
+                else:
+                    prompt = question
+            
+            # Execute agent with prompt
+            answer = self.agent.run(prompt)
+            
+            # Clean up response
+            answer = self._clean_answer(answer)
+            
+            print(f"Generated answer: {answer}")
+            return answer
             
-            # Process standard question
-            answer = self.agent.answer_question(question)
-            return self._clean_answer(answer)
         except Exception as e:
-            print(f"Error processing question: {e}")
-            error_msg = f"Unable to process question: {str(e)}"
-            return error_msg
+            print(f"Error: {str(e)}")
+            return f"Error processing question: {str(e)}"
     
-    def _clean_answer(self, answer: str) -> str:
-        """
-        Final cleanup of answer to ensure correct format
-        Reuses GAIAAgent's cleaning method
-        """
-        # Already cleaned in GAIAAgent, but do additional checks
-        if isinstance(answer, str):
-            # Remove any trailing periods and whitespace
-            answer = answer.rstrip(". \t\n\r")
-            
-            # Ensure it's not too long an answer - GAIA usually needs concise responses
-            if len(answer) > 1000:
-                # Try to find the first sentence or statement of the answer
-                sentences = answer.split('. ')
-                if len(sentences) > 1:
-                    return sentences[0].strip()
+    def _clean_answer(self, answer: any) -> str:
+        """Clean up the answer for exact matching"""
+        if not isinstance(answer, str):
+            return str(answer)
         
+        # Normalize spacing
+        answer = answer.strip()
+        
+        # Remove common prefixes
+        prefixes = [
+            "The answer is ", "Answer: ", "Final answer: ", 
+            "The result is ", "Based on the information provided, "
+        ]
+        
+        for prefix in prefixes:
+            if answer.startswith(prefix):
+                answer = answer[len(prefix):].strip()
+        
+        # Remove quotes
+        if (answer.startswith('"') and answer.endswith('"')) or (
+            answer.startswith("'") and answer.endswith("'")
+        ):
+            answer = answer[1:-1].strip()
+            
         return answer