import os
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage
from agent import build_graph

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

class BasicAgent:
    """A langgraph agent."""
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        messages = [HumanMessage(content=question)]
        result = self.graph.invoke({"messages": messages})
        answer = result['messages'][-1].content
        return answer

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if not profile:
        return "Please Login to Hugging Face with the button.", None
    username = profile.username
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    try:
        agent = BasicAgent()
    except Exception as e:
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    try:
        resp_q = requests.get(questions_url, timeout=15)
        resp_q.raise_for_status()
        questions = resp_q.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers = []
    for item in questions:
        task_id = item.get("task_id")
        q = item.get("question")
        if not task_id or q is None:
            continue
        try:
            ans = agent(q)
            answers.append({"task_id": task_id, "submitted_answer": ans})
            results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": ans})
        except Exception as e:
            results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": f"ERROR: {e}"})

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

    payload = {"username": username.strip(), "agent_code": agent_code, "answers": answers}
    try:
        resp_s = requests.post(submit_url, json=payload, timeout=60)
        resp_s.raise_for_status()
        data = resp_s.json()
        status = (
            f"Submission Successful!\n"
            f"User: {data.get('username')}\n"
            f"Score: {data.get('score', 'N/A')}% "
            f"({data.get('correct_count', '?')}/{data.get('total_attempted', '?')})\n"
            f"{data.get('message', '')}"
        )
        return status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
1.  Clone this space and customize your agent logic.
2.  Log in with the button below.
3.  Click **Run Evaluation & Submit All Answers**.
""")
    gr.LoginButton()
    run_btn = gr.Button("Run Evaluation & Submit All Answers")
    status_out = gr.Textbox(label="Run Status / Submission Result", lines=5)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
    run_btn.click(fn=run_and_submit_all, outputs=[status_out, results_table])

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