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

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
API_URL = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B-Instruct"
headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN', '')}"}

# --- Simple Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("Simple Agent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:50]}...")
        try:
            # Direct Hugging Face API call without extra libraries
            payload = {
                "inputs": f"<|im_start|>user\nAnswer this question shortly and accurately: {question}<|im_end|>\n<|im_start|>assistant\n",
                "parameters": {"max_new_tokens": 100, "return_full_text": False}
            }
            response = requests.post(API_URL, headers=headers, json=payload, timeout=20)
            
            if response.status_code == 200:
                result = response.json()
                if isinstance(result, list) and len(result) > 0:
                    answer = result[0].get("generated_text", "").strip()
                else:
                    answer = str(result).strip()
            else:
                answer = "Error from API"
                
            print(f"Agent generated answer: {answer}")
            return answer
        except Exception as e:
            print(f"Error: {e}")
            return "Fallback Answer"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID") 
    if profile:
        username = f"{profile.username}"
    else:
        return "Please Login to Hugging Face with the button.", None

    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_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:
        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.", None
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []
    
    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: 
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": "ERROR"})

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

    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    
    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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

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