Delete main.py

main.py

@@ -1,277 +0,0 @@
-import os
-import tempfile
-import gradio as gr
-import pandas as pd
-import traceback
-from core_agent import GAIAAgent
-from api_integration import GAIAApiClient
-
-# Constants
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-def save_task_file(file_content, task_id):
-    """
-    Save a task file to a temporary location
-    """
-    if not file_content:
-        return None
-    
-    # Create a temporary file
-    temp_dir = tempfile.gettempdir()
-    file_path = os.path.join(temp_dir, f"gaia_task_{task_id}.txt")
-    
-    # Write content to the file
-    with open(file_path, 'wb') as f:
-        f.write(file_content)
-    
-    print(f"File saved to {file_path}")
-    return file_path
-
-def get_agent_configuration():
-    """
-    Get the agent configuration based on environment variables
-    """
-    # Default configuration
-    config = {
-        "model_type": "OpenAIServerModel",  # Default to OpenAIServerModel
-        "model_id": "gpt-4o",  # Default model for OpenAI
-        "temperature": 0.2,
-        "executor_type": "local",
-        "verbose": False,
-        "provider": "hf-inference",  # For InferenceClientModel
-        "timeout": 120        # For InferenceClientModel
-    }
-    
-    # Check for xAI API key and base URL
-    xai_api_key = os.getenv("XAI_API_KEY")
-    xai_api_base = os.getenv("XAI_API_BASE")
-    
-    # If we have xAI credentials, use them
-    if xai_api_key:
-        config["api_key"] = xai_api_key
-        if xai_api_base:
-            config["api_base"] = xai_api_base
-            # Use a model that works well with xAI
-            config["model_id"] = "mixtral-8x7b-32768"
-    
-    # Override with environment variables if present
-    if os.getenv("AGENT_MODEL_TYPE"):
-        config["model_type"] = os.getenv("AGENT_MODEL_TYPE")
-    
-    if os.getenv("AGENT_MODEL_ID"):
-        config["model_id"] = os.getenv("AGENT_MODEL_ID")
-    
-    if os.getenv("AGENT_TEMPERATURE"):
-        config["temperature"] = float(os.getenv("AGENT_TEMPERATURE"))
-    
-    if os.getenv("AGENT_EXECUTOR_TYPE"):
-        config["executor_type"] = os.getenv("AGENT_EXECUTOR_TYPE")
-    
-    if os.getenv("AGENT_VERBOSE") is not None:
-        config["verbose"] = os.getenv("AGENT_VERBOSE").lower() == "true"
-    
-    if os.getenv("AGENT_API_BASE"):
-        config["api_base"] = os.getenv("AGENT_API_BASE")
-    
-    # InferenceClientModel specific settings
-    if os.getenv("AGENT_PROVIDER"):
-        config["provider"] = os.getenv("AGENT_PROVIDER")
-    
-    if os.getenv("AGENT_TIMEOUT"):
-        config["timeout"] = int(os.getenv("AGENT_TIMEOUT"))
-    
-    return config
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the GAIAAgent on them, submits all answers,
-    and displays the results.
-    """
-    # Check for user login
-    if not profile:
-        return "Please Login to Hugging Face with the button.", None
-    
-    username = profile.username
-    print(f"User logged in: {username}")
-    
-    # Get SPACE_ID for code link
-    space_id = os.getenv("SPACE_ID")
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    
-    # Initialize API client
-    api_client = GAIAApiClient(DEFAULT_API_URL)
-    
-    # Initialize Agent with configuration
-    try:
-        agent_config = get_agent_configuration()
-        print(f"Using agent configuration: {agent_config}")
-        
-        agent = GAIAAgent(**agent_config)
-        print("Agent initialized successfully")
-    except Exception as e:
-        error_details = traceback.format_exc()
-        print(f"Error initializing agent: {e}\n{error_details}")
-        return f"Error initializing agent: {e}", None
-    
-    # Fetch questions
-    try:
-        questions_data = api_client.get_questions()
-        if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except Exception as e:
-        error_details = traceback.format_exc()
-        print(f"Error fetching questions: {e}\n{error_details}")
-        return f"Error fetching questions: {e}", None
-    
-    # Run agent on questions
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    
-    # Progress tracking
-    total_questions = len(questions_data)
-    completed = 0
-    failed = 0
-    
-    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:
-            # Update progress
-            completed += 1
-            print(f"Processing question {completed}/{total_questions}: Task ID {task_id}")
-            
-            # Check if the question has an associated file
-            file_path = None
-            try:
-                file_content = api_client.get_file(task_id)
-                print(f"Downloaded file for task {task_id}")
-                file_path = save_task_file(file_content, task_id)
-            except Exception as file_e:
-                print(f"No file found for task {task_id} or error: {file_e}")
-            
-            # Run the agent to get the answer
-            submitted_answer = agent.answer_question(question_text, file_path)
-            
-            # Add to results
-            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:
-            # Update error count
-            failed += 1
-            error_details = traceback.format_exc()
-            print(f"Error running agent on task {task_id}: {e}\n{error_details}")
-            
-            # Add error to results
-            error_msg = f"AGENT ERROR: {e}"
-            answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": error_msg
-            })
-    
-    # Print summary
-    print(f"\nProcessing complete: {completed} questions processed, {failed} failures")
-    
-    if not answers_payload:
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-    
-    # Submit answers
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
-    
-    print(f"Submitting {len(answers_payload)} answers for username '{username}'...")
-    
-    try:
-        result_data = api_client.submit_answers(
-            username.strip(),
-            agent_code,
-            answers_payload
-        )
-        
-        # Calculate success rate
-        correct_count = result_data.get('correct_count', 0)
-        total_attempted = result_data.get('total_attempted', len(answers_payload))
-        success_rate = (correct_count / total_attempted) * 100 if total_attempted > 0 else 0
-        
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({correct_count}/{total_attempted} correct, {success_rate:.1f}% success rate)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        
-        print("Submission successful.")
-        return final_status, pd.DataFrame(results_log)
-    except Exception as e:
-        error_details = traceback.format_exc()
-        status_message = f"Submission Failed: {e}\n{error_details}"
-        print(status_message)
-        return status_message, pd.DataFrame(results_log)
-
-# Build Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        
-        1. Log in to your Hugging Face account using the button below.
-        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        
-        **Configuration:**
-        
-        You can configure the agent by setting these environment variables:
-        - `AGENT_MODEL_TYPE`: Model type (HfApiModel, InferenceClientModel, LiteLLMModel, OpenAIServerModel)
-        - `AGENT_MODEL_ID`: Model ID
-        - `AGENT_TEMPERATURE`: Temperature for generation (0.0-1.0)
-        - `AGENT_EXECUTOR_TYPE`: Type of executor ('local' or 'e2b')
-        - `AGENT_VERBOSE`: Enable verbose logging (true/false)
-        - `AGENT_API_BASE`: Base URL for API calls (for OpenAIServerModel)
-        
-        **xAI Support:**
-        - `XAI_API_KEY`: Your xAI API key
-        - `XAI_API_BASE`: Base URL for xAI API (default: https://api.groq.com/openai/v1)
-        - When using xAI, set AGENT_MODEL_TYPE=OpenAIServerModel and AGENT_MODEL_ID=mixtral-8x7b-32768
-        
-        **InferenceClientModel specific settings:**
-        - `AGENT_PROVIDER`: Provider for InferenceClientModel (e.g., "hf-inference") 
-        - `AGENT_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)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-    
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    
-    # Check for environment variables
-    config = get_agent_configuration()
-    print(f"Agent configuration: {config}")
-    
-    # Run the Gradio app
-    demo.launch(debug=True, share=False)