app.py

import os
import gradio as gr
import requests
import pandas as pd
from dotenv import load_dotenv
from functions import *
from langchain_core.messages import HumanMessage

load_dotenv()

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

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

    if not profile:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None
    username = profile.username
    print(f"User logged in: {username}")

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    try:
        graph = build_graph()
        agent = graph.invoke
    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" if space_id else "Repo URL not available"
    print(f"Agent code repo: {agent_code}")

    # Fetch questions
    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"Error fetching questions: {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:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            input_messages = [HumanMessage(content=question_text)]

            result = agent({"messages": input_messages})

            if "messages" in result and result["messages"]:
                last_valid = next(
                    (m for m in reversed(result["messages"]) if hasattr(m, "content") and isinstance(m.content, str)), 
                    None
                )
                if last_valid:
                    answer = last_valid.content.strip()
                else:
                    answer = "UNKNOWN"
            else:
                answer = "UNKNOWN"
                
            print("Answered with:", answer)
            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": answer
            })
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        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 for user '{username}'...")

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

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        Modify the code here to define your agent's logic, the tools, the necessary packages, etc...
        """
    )

    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" + "-"*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("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("SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

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

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