app.py

# app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd

# SmolAgents imports
from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel

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

# --- Enhanced Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized with real agentic capabilities.")

        # Initialize tools and model
        self.search_tool = DuckDuckGoSearchTool()
        self.model = InferenceClientModel()
        self.agent = CodeAgent(
            model=self.model,
            tools=[self.search_tool]
        )

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            response = self.agent.run(question)
            print(f"Agent response (first 50 chars): {response[:50]}...")
            return response
        except Exception as e:
            print(f"Agent error during run: {e}")
            return f"Error in agent: {e}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    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 to submit answers.", None

    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_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)

    # Fetch questions
    try:
        resp = requests.get(questions_url, timeout=15)
        resp.raise_for_status()
        questions_data = resp.json()
        if not questions_data:
            return "Empty or invalid question list.", None
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    # Run agent on questions
    results_log = []
    answers_payload = []
    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 = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted})
        except Exception as e:
            print(f"Error on task {task_id}: {e}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})

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

    # Prepare & submit
    payload = {"username": username, "agent_code": agent_code, "answers": answers_payload}
    try:
        submit_resp = requests.post(submit_url, json=payload, timeout=60)
        submit_resp.raise_for_status()
        result_json = submit_resp.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_json.get('username')}\n"
            f"Score: {result_json.get('score', 'N/A')}% "
            f"({result_json.get('correct_count', '?')}/{result_json.get('total_attempted', '?')} correct)\n"
            f"Message: {result_json.get('message', '')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission failed: {e}", pd.DataFrame(results_log)

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
    Modify `BasicAgent` to add more tools or logic.
    Log in, click **Run Evaluation & Submit All Answers**, and watch it process automatically.
    """)
    gr.LoginButton()
    run_btn = gr.Button("Run Evaluation & Submit All Answers")
    status = gr.Textbox(label="Status / Submission Result", lines=5, interactive=False)
    results = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_btn.click(fn=run_and_submit_all, outputs=[status, results])

if __name__ == "__main__":
    print("Launching app...")
    demo.launch(debug=True, share=False)