app.py

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

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

# ---- Smolagents imports ----
from smolagents import WikipediaSearchTool, DuckDuckGoSearchTool

# ---- Agent Definition ----
class SmolAgent:
    def __init__(self):
        self.wiki = WikipediaSearchTool()
        self.duck = DuckDuckGoSearchTool()
    def __call__(self, question: str) -> str:
        # Try Wikipedia first, fall back to DuckDuckGo if nothing useful.
        wiki_result = ""
        duck_result = ""
        try:
            wiki_result = self.wiki.run(question)
        except Exception as e:
            wiki_result = f"(Wiki error: {e})"
        # If Wikipedia yields nothing or too short, use DuckDuckGo as fallback
        if wiki_result and len(str(wiki_result)) > 40:
            return str(wiki_result)
        try:
            duck_result = self.duck.run(question)
        except Exception as e:
            duck_result = f"(DuckDuckGo error: {e})"
        if duck_result:
            return str(duck_result)
        # If both fail, return error info
        return f"Wikipedia: {wiki_result}\nDuckDuckGo: {duck_result}\n(No answer found.)"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    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 = SmolAgent()
    except Exception as 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:
            return "Fetched questions list is empty or invalid format.", None
    except Exception as e:
        return f"Error 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}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    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.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        results_df = pd.DataFrame(results_log)
        return f"Submission Failed: {e}", results_df

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# SmolAgent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Clone and modify this space to improve your agent logic as you see fit.
        2. Log in to your Hugging Face account with the button below.
        3. Click 'Run Evaluation & Submit All Answers' to begin.
        ---
        **Disclaimer:** Submission may take a while depending on the number of questions and agent speed.
        """
    )

    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__":
    print("\n--- App Starting ---\n")
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")
    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST not found (running locally?)")
    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID not found")
    print("--- App Starting ---\n")
    demo.launch(debug=True, share=False)