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

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_TOKEN = os.getenv("HF_TOKEN")  # Make sure your HF read token is set in environment variables

# --- Gaia Agent using Qwen API ---
class GaiaAgentQwen:
    def __init__(self, model="Qwen/Qwen2.5-Coder-32B-Instruct"):
        self.model = model
        self.api_url = f"https://api-inference.huggingface.co/models/{model}"
        self.headers = {"Authorization": f"Bearer {HF_TOKEN}"}
        print(f"GaiaAgentQwen initialized with model {model}")

    def __call__(self, question: str) -> str:
        prompt = f"Answer the following question concisely and correctly:\n{question}"
        payload = {"inputs": prompt, "options": {"wait_for_model": True}}
        try:
            response = requests.post(self.api_url, headers=self.headers, json=payload, timeout=60)
            response.raise_for_status()
            data = response.json()
            if isinstance(data, list) and "generated_text" in data[0]:
                return data[0]["generated_text"]
            else:
                return str(data)  # fallback
        except Exception as e:
            print(f"Error calling HF Inference API: {e}")
            return f"API ERROR: {e}"

# --- Main function ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    api_url = DEFAULT_API_URL
    space_id = os.getenv("SPACE_ID") or "unknown-space"

    username = profile.username if profile else "anonymous"
    if profile:
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")

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

    # Instantiate the Gaia agent
    try:
        agent = GaiaAgentQwen()
    except Exception as e:
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code repo: {agent_code}")

    # Fetch questions
    try:
        print(f"Fetching questions from: {questions_url}")
        response = requests.get(questions_url, timeout=10)
        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

    # Run agent on questions
    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 not question_text:
            print(f"Skipping invalid question: {item}")
            continue
        try:
            answer = agent(question_text)
            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:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # Submit answers
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    try:
        print(f"Submitting {len(answers_payload)} answers for user '{username}'...")
        response = requests.post(submit_url, json=submission_data, timeout=20)
        response.raise_for_status()
        submission_result = response.json()
        print(f"Submission result: {submission_result}")
        return "Submission completed successfully!", pd.DataFrame(results_log)
    except Exception as e:
        return f"Error submitting answers: {e}", pd.DataFrame(results_log)

# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Gaia Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Clone this space, then modify the code to define your agent's logic.
    2. Log in to your Hugging Face account using the button below.
    3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see results.

    **Note:** Using the HF API can take a few seconds per question.
    """)
    login_btn = 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,
        inputs=[login_btn],
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    print("Launching Gradio Interface for Gaia Agent Evaluation...")
    demo.launch(debug=True, share=False)