app.py

import os
import gradio as gr
import requests
import pandas as pd
from agent import GaiaAgent

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

agent = GaiaAgent()

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_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
        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

    results_log = []
    answers_payload = []

    print("\n--- STARTING AGENT RUN ---")
    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:
            final_answer, trace = agent(question_text)
            print(f"\n--- QUESTION ---\nTask ID: {task_id}\nQuestion: {question_text}")
            print(f"\n--- REASONING TRACE ---\n{trace}")
            print(f"\n--- FINAL ANSWER (SUBMITTED) ---\n{final_answer}")

            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": final_answer,
                "reasoning_trace": trace
            })
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": final_answer})
        except Exception as 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 to submit.", pd.DataFrame(results_log)

    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')}% "
            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:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

def evaluate(question, files):
    uploaded_files = {}
    if files:
        for file in files:
            file_path = file.name
            file.save(file_path)
            uploaded_files[file.name] = file_path

    prediction, reasoning = agent(question, uploaded_files)
    return prediction, reasoning

with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Interface")
    gr.Markdown("Logga in och kör agenten på alla frågor eller testa enskilda.")

    with gr.Tab("✅ Run Full Evaluation"):
        gr.LoginButton()
        run_button = gr.Button("Run Evaluation & Submit All Answers")
        status_output = gr.Textbox(label="Submission Result")
        results_table = gr.DataFrame(label="Answers")
        run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])

    with gr.Tab("🔍 Test Manual Question"):
        question = gr.Textbox(label="Question")
        files = gr.File(label="Optional files (mp3, wav, xlsx)", file_types=['.mp3', '.wav', '.xlsx'], type="filepath", file_count="multiple")
        submit = gr.Button("Run Agent")
        answer = gr.Textbox(label="Answer")
        reasoning = gr.Textbox(label="Reasoning Trace")
        submit.click(fn=evaluate, inputs=[question, files], outputs=[answer, reasoning])

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    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 should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable 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}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?).")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)