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"

# --- Import your custom agent graph from agent.py ---
from agent import build_graph
from langchain_core.messages import HumanMessage

# --- Basic Agent Definition (wrapper around your graph) ---
class BasicAgent:
    def __init__(self):
        print("Initializing BasicAgent with agent.py graph...")
        self.graph = build_graph()

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:50]}...")
        try:
            messages = [HumanMessage(content=question)]
            result = self.graph.invoke({"messages": messages})
            answer = result["messages"][-1].content
            if answer.lower().startswith("final answer"):
                answer = answer.split(":", 1)[-1].strip()
            print(f"Agent returning: {answer}")
            return answer
        except Exception as e:
            print(f"Error inside agent: {e}")
            return f"AGENT ERROR: {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 = 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"

    # 1. Instantiate Agent
    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" if space_id else "N/A"
    print(f"Agent code repo: {agent_code}")

    # 2. Fetch Questions
    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
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Run your Agent
    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
        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
        })

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

    # 4. Prepare Submission
    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)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    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:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1. Clone this space and implement your logic in `agent.py`.
        2. Log in with your Hugging Face account.
        3. Click **Run Evaluation & Submit All Answers**.

        ---
        ⚠️ The process may take a while (agent needs to answer all questions).
        """
    )

    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 ---")
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST: {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: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
    else:
        print("ℹ️ SPACE_ID not found (running locally?).")

    print("--------------------\n")
    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)