app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("Initializing Smolagents CodeAgent...")
        
        # 1. Define the Model
        # Qwen 2.5 Coder is excellent for the logic/math required in GAIA
        # It will automatically use the HF_TOKEN from your Space Secrets
        model = HfApiModel(
            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
        )
        
        # 2. Define Tools
        search_tool = DuckDuckGoSearchTool()
        
        # 3. Initialize the Agent
        # We allow imports like requests and bs4 so the agent can scrape if needed
        self.agent = CodeAgent(
            tools=[search_tool],
            model=model,
            additional_authorized_imports=["requests", "bs4", "datetime", "pandas", "math"],
            max_steps=20, # Give it enough steps to think
            verbosity_level=1
        )
        print("Agent initialized successfully.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question}")
        try:
            # Run the smolagent
            # We cast to string in case the agent returns a non-string object
            answer = self.agent.run(question)
            print(f"Agent calculated answer: {answer}")
            return str(answer)
        except Exception as e:
            print(f"Agent failed with error: {e}")
            return "Error processing request"

# --- Logic to Run and Submit (Provided by Course Template) ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- 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}")
    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 = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    # Link to your codebase
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "https://huggingface.co/spaces/generic/tree/main"
    print(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 your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    
    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        print(f"Processing {i+1}/{len(questions_data)}: Task {task_id}")
        
        if not task_id or question_text is None:
            continue
        
        try:
            # THE AGENT CALL
            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 Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Final Agent Evaluation Runner (SmolAgents)")
    gr.Markdown(
        """
        **Instructions:**
        1. Ensure `HF_TOKEN` is set in your Space Secrets.
        2. Log in via the button below.
        3. Click 'Run Evaluation'. 
        
        *Note: This process takes a few minutes as the agent thinks through 10-20 questions.*
        """
    )

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Status", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Results", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    demo.launch()