app.py

import os
import gradio as gr
import requests
import pandas as pd
# Import smolagents components
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, VisitWebpageTool

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

# --- Robust Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("Initializing High-Performance CodeAgent...")
        # We use Qwen 2.5 72B because it excels at the logic needed for GAIA tasks
        self.model = HfApiModel(model_id="Qwen/Qwen2.5-72B-Instruct")
        
        # Tools are the agent's 'hands'
        self.tools = [
            DuckDuckGoSearchTool(), 
            VisitWebpageTool()
        ]
        
        # The CodeAgent allows the LLM to write and run Python to solve math/sorting
        self.agent = CodeAgent(
            tools=self.tools,
            model=self.model,
            add_base_tools=True  # This gives it built-in tools for things like math
        )

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:100]}...")
        try:
            # We add a small prompt hint to ensure the answer is concise for the grader
            prompt = f"{question}\n\nFinal Answer Requirement: Provide ONLY the specific answer requested (number, name, or list) with no extra text."
            result = self.agent.run(prompt)
            return str(result)
        except Exception as e:
            print(f"Agent Error: {e}")
            return f"Error: {e}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID") 

    if profile:
        username = f"{profile.username}"
    else:
        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 our new Agent
    try:
        agent = BasicAgent()
    except Exception as e:
        return f"Error initializing agent: {e}", None
        
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # 2. Fetch Questions
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Run Agent
    results_log = []
    answers_payload = []
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        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}"})

    # 4. Prepare & Submit
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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')}% \n"
            f"Message: {result_data.get('message')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# 🚀 Professional Agent Evaluation Runner")
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5)
    results_table = gr.DataFrame(label="Live Results Trace", wrap=True)

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

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