Create app.py

app.py

@@ -1,204 +1,492 @@
+from smolagents import (
+    CodeAgent, 
+    DuckDuckGoSearchTool, 
+    HfApiModel, 
+    LiteLLMModel,
+    OpenAIServerModel,
+    PythonInterpreterTool,
+    tool,
+    InferenceClientModel
+)
+from typing import List, Dict, Any, Optional
 import os
-import gradio as gr
+import tempfile
+import re
+import json
 import requests
-import pandas as pd
-from dotenv import load_dotenv
-from gemini_agent import GeminiAgent
+from urllib.parse import urlparse
 
-# Constants
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-class BasicAgent:
-    def __init__(self):
-        print("Initializing the BasicAgent")
-        
-        # Get Gemini API key
-        api_key = os.getenv('GOOGLE_API_KEY')
-        if not api_key:
-            raise ValueError("GOOGLE_API_KEY environment variable not set.")
-            
-        # Initialize GeminiAgent
-        self.agent = GeminiAgent(api_key=api_key)
-        print("GeminiAgent initialized successfully")
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        final_answer = self.agent.run(question)
-        print(f"Agent returning fixed answer: {final_answer}")
-        return final_answer
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+@tool
+def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    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
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+    temp_dir = tempfile.gettempdir()
+    if filename is None:
+        temp_file = tempfile.NamedTemporaryFile(delete=False)
+        filepath = temp_file.name
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+        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."
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+@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:
-        agent = BasicAgent()
+        # 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:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+        return f"Error downloading file: {str(e)}"
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+@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:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
+        # 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:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+        return f"Error extracting text from image: {str(e)}"
 
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+@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
+    """
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        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'."
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        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
+    """
+    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'."
+    except Exception as e:
+        return f"Error analyzing Excel file: {str(e)}"
 
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
+class GAIAAgent:
+    def __init__(
+        self, 
+        model_type: str = "HfApiModel", 
+        model_id: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        temperature: float = 0.2,
+        executor_type: str = "local",  # Changed from use_e2b to executor_type
+        additional_imports: List[str] = None,
+        additional_tools: List[Any] = None,
+        system_prompt: Optional[str] = None,  # We'll still accept this parameter but not use it directly
+        verbose: bool = False,
+        provider: Optional[str] = None,  # Add provider for InferenceClientModel
+        timeout: Optional[int] = None   # Add timeout for InferenceClientModel
+    ):
         """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        Initialize a GAIAAgent with specified configuration
+        
+        Args:
+            model_type: Type of model to use (HfApiModel, LiteLLMModel, OpenAIServerModel, InferenceClientModel)
+            model_id: ID of the model to use
+            api_key: API key for the model provider
+            api_base: Base URL for API calls
+            temperature: Temperature for text generation
+            executor_type: Type of executor for code execution ('local' or 'e2b')
+            additional_imports: Additional Python modules to allow importing
+            additional_tools: Additional tools to provide to the agent
+            system_prompt: Custom system prompt to use (not directly used, kept for backward compatibility)
+            verbose: Enable verbose logging
+            provider: Provider for InferenceClientModel (e.g., "hf-inference")
+            timeout: Timeout in seconds for API calls
         """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+        # Set verbosity
+        self.verbose = verbose
+        self.system_prompt = system_prompt  # Store for potential future use
+        
+        # Initialize model based on configuration
+        if model_type == "HfApiModel":
+            if api_key is None:
+                api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+                if not api_key:
+                    raise ValueError("No Hugging Face token provided. Please set HUGGINGFACEHUB_API_TOKEN environment variable or pass api_key parameter.")
+            
+            if self.verbose:
+                print(f"Using Hugging Face token: {api_key[:5]}...")
+                
+            self.model = HfApiModel(
+                model_id=model_id or "meta-llama/Llama-3-70B-Instruct",
+                token=api_key,
+                temperature=temperature
+            )
+        elif model_type == "InferenceClientModel":
+            if api_key is None:
+                api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+                if not api_key:
+                    raise ValueError("No Hugging Face token provided. Please set HUGGINGFACEHUB_API_TOKEN environment variable or pass api_key parameter.")
+            
+            if self.verbose:
+                print(f"Using Hugging Face token: {api_key[:5]}...")
+                
+            self.model = InferenceClientModel(
+                model_id=model_id or "meta-llama/Llama-3-70B-Instruct",
+                provider=provider or "hf-inference",
+                token=api_key,
+                timeout=timeout or 120,
+                temperature=temperature
+            )
+        elif model_type == "LiteLLMModel":
+            from smolagents import LiteLLMModel
+            self.model = LiteLLMModel(
+                model_id=model_id or "gpt-4o",
+                api_key=api_key or os.getenv("OPENAI_API_KEY"),
+                temperature=temperature
+            )
+        elif model_type == "OpenAIServerModel":
+            # Check for xAI API key and base URL first
+            xai_api_key = os.getenv("XAI_API_KEY")
+            xai_api_base = os.getenv("XAI_API_BASE")
+            
+            # If xAI credentials are available, use them
+            if xai_api_key and api_key is None:
+                api_key = xai_api_key
+                if self.verbose:
+                    print(f"Using xAI API key: {api_key[:5]}...")
+            
+            # If no API key specified, fall back to OPENAI_API_KEY
+            if api_key is None:
+                api_key = os.getenv("OPENAI_API_KEY")
+                if not api_key:
+                    raise ValueError("No OpenAI API key provided. Please set OPENAI_API_KEY or XAI_API_KEY environment variable or pass api_key parameter.")
+            
+            # If xAI API base is available and no api_base is provided, use it
+            if xai_api_base and api_base is None:
+                api_base = xai_api_base
+                if self.verbose:
+                    print(f"Using xAI API base URL: {api_base}")
+            
+            # If no API base specified but environment variable available, use it
+            if api_base is None:
+                api_base = os.getenv("AGENT_API_BASE")
+                if api_base and self.verbose:
+                    print(f"Using API base from AGENT_API_BASE: {api_base}")
+            
+            self.model = OpenAIServerModel(
+                model_id=model_id or "gpt-4o",
+                api_key=api_key,
+                api_base=api_base,
+                temperature=temperature
+            )
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+        
+        if self.verbose:
+            print(f"Initialized model: {model_type} - {model_id}")
+        
+        # Initialize default tools
+        self.tools = [
+            DuckDuckGoSearchTool(),
+            PythonInterpreterTool(),
+            save_and_read_file,
+            download_file_from_url,
+            analyze_csv_file,
+            analyze_excel_file
+        ]
+        
+        # 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")
+        
+        # Add any additional tools
+        if additional_tools:
+            self.tools.extend(additional_tools)
+            
+        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"]
+        if additional_imports:
+            self.imports.extend(additional_imports)
+            
+        # Initialize the CodeAgent
+        executor_kwargs = {}
+        if executor_type == "e2b":
+            try:
+                # Try to import e2b dependencies to check if they're available
+                from e2b_code_interpreter import Sandbox
+                if self.verbose:
+                    print("Using e2b executor")
+            except ImportError:
+                if self.verbose:
+                    print("e2b dependencies not found, falling back to local executor")
+                executor_type = "local"  # Fallback to local if e2b is not available
+        
+        self.agent = CodeAgent(
+            tools=self.tools,
+            model=self.model,
+            additional_authorized_imports=self.imports,
+            executor_type=executor_type,
+            executor_kwargs=executor_kwargs,
+            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:
+                    with open(task_file_path, 'r') as f:
+                        file_content = f.read()
+                    
+                    # Determine file type from extension
+                    import os
+                    file_ext = os.path.splitext(task_file_path)[1].lower()
+                    
+                    context = f"""
+Question: {question}
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+This question has an associated file. Here is the file content:
 
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+```{file_ext}
+{file_content}
+```
 
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+Analyze the file content above to answer the question.
+"""
+                except Exception as file_e:
+                    context = f"""
+Question: {question}
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+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]}
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+Now answer the question above. Remember to format your answer exactly as requested.
+"""
+            
+            # Add a prompt to ensure precise answers
+            full_prompt = f"""{context}
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+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.
+For example, if asked "What is the capital of France?", respond simply 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