import inspect
import os
import gradio as gr
import pandas as pd
import requests
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel

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

# --- Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("Initializing smolagents CodeAgent...")
        # Uses Qwen/Qwen2.5-Coder-32B-Instruct via Hugging Face Inference API
        model = HfApiModel(
            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
            token=os.getenv("HF_TOKEN")
        )
        # Web search tool to retrieve up-to-date facts
        search_tool = DuckDuckGoSearchTool()
        
        self.agent = CodeAgent(
            tools=[search_tool],
            model=model,
            max_steps=5,
            verbosity_level=1
        )
        print("BasicAgent initialized successfully.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:80]}...")
        prompt = (
            f"Answer the following question concisely and factually. "
            f"Provide only the final answer without extra conversational text:\n{question}"
        )
        try:
            answer = self.agent.run(prompt)
            return str(answer).strip()
        except Exception as e:
            print(f"Error during agent execution: {e}")
            return f"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"
    print(f"Agent Code Repository: {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 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 question_text is None:
            continue

        try:
            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})
        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)

    # 4. Prepare Submission
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    print(f"Submitting {len(answers_payload)} answers for user '{username}'...")

    # 5. Submit Answers
    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')}%\n"
            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:
        status_message = f"Submission Failed: {e}"
        return status_message, pd.DataFrame(results_log)


# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to run your agent and record the score.
        """
    )
    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__":
    demo.launch(debug=True, share=False)