app.py

import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel
from huggingface_hub import login

# Constants
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_TOKEN = os.environ.get("HF_TOKEN", "")  # Hugging Face token

# Login if token is provided
if HF_TOKEN:
    login(token=HF_TOKEN)

class GAIACodeAgent:
    def __init__(self):
        """Initialize the advanced agent with tools and capabilities"""
        model = InferenceClientModel()
        self.agent = CodeAgent(
            tools=[DuckDuckGoSearchTool()],
            model=model
        )
    
    def __call__(self, question: str) -> str:
        """Process a question and return an answer"""
        try:
            print(f"Agent received question: {question[:50]}...")
            # Improve the prompt to get better accuracy on exact match questions
            enriched_prompt = (
                f"Answer the following question accurately and concisely. "
                f"Provide a straightforward answer without unnecessary elaboration. "
                f"The answer will be evaluated for exact match accuracy.\n\n"
                f"Question: {question}\n\n"
                f"Answer: "
            )
            
            # Run the agent with the enriched prompt
            response = self.agent.run(enriched_prompt)
            
            # Clean up response to improve exact match chances
            cleaned_response = response.strip()
            print(f"Agent returning answer: {cleaned_response[:50]}...")
            return cleaned_response
        except Exception as e:
            error_msg = f"Error: {str(e)}"
            print(error_msg)
            return error_msg

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GAIACodeAgent on them, submits all answers,
    and displays the results.
    """
    # Determine HF Space Runtime URL and Repo URL
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    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"

    # 1. Instantiate Agent
    try:
        agent = GAIACodeAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code URL: {agent_code}")

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    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 Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run 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}"})

    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}")
    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 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

def query_single_agent(question):
    """Run agent on a single question for testing"""
    try:
        agent = GAIACodeAgent()
        response = agent(question)
        return response
    except Exception as e:
        return f"Error: {str(e)}"

# Build Gradio Interface
with gr.Blocks(title="GAIA Code Agent Evaluation") as demo:
    gr.Markdown("# GAIA Code Agent Evaluation")
    gr.Markdown(
        """
        This application helps you evaluate a code agent on the GAIA benchmark.
        
        ## Instructions:
        1. Log in to your Hugging Face account using the button below
        2. You can test the agent with a single question in the "Test Agent" tab
        3. Use the "Run Evaluation" tab to run the agent on all GAIA questions and submit answers
        """
    )
    
    with gr.Tab("Test Agent"):
        question_input = gr.Textbox(
            label="Enter a question", 
            placeholder="How many seconds would it take for a leopard at full speed to run through Pont des Arts?"
        )
        query_button = gr.Button("Get Answer")
        response_output = gr.Textbox(label="Agent Response", lines=10)
        query_button.click(query_single_agent, inputs=question_input, outputs=response_output)
    
    with gr.Tab("Run Evaluation"):
        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])

# Start the app
if __name__ == "__main__":
    demo.launch()