app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
# Modern smolagents imports matching the latest API
from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel

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

# --- Smart Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("Initializing smart CodeAgent...")
        
        # Pull the token automatically from either standard HF Space variables or your manual secrets
        token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_TOKEN")
        
        # 1. Setup a top-tier, non-gated coding model
        self.model = InferenceClientModel(
            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
            token=token
        )
        
        # 2. Build the agent with the search tool
        self.agent = CodeAgent(
            tools=[DuckDuckGoSearchTool()], 
            model=self.model,
            max_steps=5  # Allows it to reason, search, and recover from errors
        )
        print("Smart Agent initialized successfully.")

    def __call__(self, question: str) -> str:
        print(f"\n[Agent Processing] Received question: {question[:100]}...")
        
        # Injected prompt template to cleanly force an EXACT MATCH format
        strict_prompt = (
            f"You are a precise, truth-seeking QA bot. Answer the following question using your tools:\n"
            f"\"{question}\"\n\n"
            "CRITICAL INSTRUCTION: Output ONLY the final target answer value (e.g., just the raw number, the precise date, or the specific name). "
            "Do not write conversational filler, do not explain your steps in the final output, and DO NOT include "
            "phrases like 'The answer is:' or 'FINAL ANSWER'."
        )
        
        try:
            # Run the agent framework through its loop
            raw_result = self.agent.run(strict_prompt)
            final_answer = str(raw_result).strip()
            print(f"[Agent Success] Output: {final_answer}")
            return final_answer
        except Exception as e:
            print(f"[Agent Error] Failed to process task: {e}")
            return "Error calculating answer"

# --- Submission Logic ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the upgraded BasicAgent on them, submits all answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID") 

    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"

    try:
        agent = BasicAgent()
    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(agent_code)

    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 Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    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:
            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:
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        return status_message, pd.DataFrame(results_log)

# --- Gradio UI Layout ---
with gr.Blocks() as demo:
    gr.Markdown("# Smart Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below. This links your leaderboard submission profile.
        2. Click 'Run Evaluation & Submit All Answers' to process the GAIA benchmark and log your live score.
        """
    )

    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__":
    demo.launch(debug=True, share=False)