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

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

# --- Новый агент: KeywordAgent ---
class KeywordAgent:
    def __init__(self):
        self.keywords = {
            "python": "Python — это мощный язык программирования, широко используемый в науке и разработке.",
            "ai": "AI (искусственный интеллект) позволяет компьютерам решать задачи, которые обычно требуют разума.",
            "data": "Данные — это основа всех аналитических систем и машинного обучения.",
            "huggingface": "Hugging Face — это платформа для работы с ИИ, особенно NLP-моделями.",
            "gradio": "Gradio — это библиотека для создания веб-интерфейсов ИИ-моделей."
        }
        self.default_answer = "Извините, я пока не знаю, как ответить на этот вопрос. Попробуйте иначе."

    def __call__(self, question: str) -> str:
        print(f"🔎 Вопрос получен: {question[:50]}...")
        q_lower = question.lower()
        for word, response in self.keywords.items():
            if word in q_lower:
                print(f"✅ Найдено ключевое слово: {word}")
                return response
        print("⚠️ Ключевые слова не найдены.")
        return self.default_answer

# --- Run Agent only ---
def run_agent_only(profile: gr.OAuthProfile | None):
    if not profile:
        return "Please login first.", None, None

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

    questions_url = f"{DEFAULT_API_URL}/questions"
    try:
        response = requests.get(questions_url, timeout=10)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Failed to fetch questions: {e}", None, 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 not question_text:
            continue
        try:
            answer = agent(question_text)
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": answer
            })
            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": answer
            })
        except Exception as e:
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"ERROR: {e}"
            })

    return "✅ Answers generated. You can now submit.", pd.DataFrame(results_log), answers_payload

# --- Submit only ---
def submit_only(profile: gr.OAuthProfile | None, answers_payload: list | None):
    if not profile:
        return "Please login first.", None
    if not answers_payload:
        return "No answers to submit. Run the agent first.", None

    username = profile.username
    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    submit_url = f"{DEFAULT_API_URL}/submit"

    try:
        submission_data = {
            "username": username.strip(),
            "agent_code": agent_code,
            "answers": answers_payload
        }
        response = requests.post(submit_url, json=submission_data, timeout=30)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"🎉 Submission Successful!\n"
            f"👤 User: {result_data.get('username')}\n"
            f"✅ Score: {result_data.get('score')}%\n"
            f"🎯 Correct: {result_data.get('correct_count')}/{result_data.get('total_attempted')}"
        )
        return final_status, None
    except Exception as e:
        return f"❌ Submission failed: {e}", None

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 Keyword Agent Evaluation Runner")
    gr.Markdown("""
    **Steps to Complete Your Hugging Face Certification:**
    1. Clone this Space and build your own logic in the `KeywordAgent`.
    2. Log in using the Hugging Face button.
    3. Click `Run Agent` to generate answers.
    4. Once ready, click `Submit` to send your answers and view your score.
    """)

    gr.LoginButton()
    profile_input = gr.OAuthProfileComponent()

    run_button = gr.Button("🧠 Run Agent (Generate Answers)")
    submit_button = gr.Button("📤 Submit All Answers")

    status_output = gr.Textbox(label="Status", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Agent Answers", wrap=True)

    answers_state = gr.State(value=None)

    run_button.click(
        fn=run_agent_only,
        inputs=[profile_input],
        outputs=[status_output, results_table, answers_state]
    )

    submit_button.click(
        fn=submit_only,
        inputs=[profile_input, answers_state],
        outputs=[status_output, results_table]
    )

# --- Startup ---
if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST not found.")

    if space_id_startup:
        print(f"✅ SPACE_ID: {space_id_startup}")
        print(f"   Repo: https://huggingface.co/spaces/{space_id_startup}")
    else:
        print("ℹ️  SPACE_ID not found.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    demo.launch(debug=True, share=False)