Create main.py

main.py

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+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)