import os
import sys
import time
import gradio as gr
import requests
from langchain.prompts import ChatPromptTemplate
from langchain_community.llms import Ollama
import subprocess

from func_ai import classify_comment, retrieve_from_vdb, VECTOR_API_URL
from func_facebook import get_page_id, has_page_replied, get_unanswered_comments, reply_comment, hide_negative_comments

# Wait for the server to start
time.sleep(10)
llm = Ollama(model="llama3.1")
print("Модель Ollama 'llama3.1' инициализирована.")

template = """
You are an assistant answering users' questions using the provided context. Your tasks:

1. **Brevity**: Respond concisely, using only relevant information from the context.
2. **Politeness**: Start your response with a greeting and maintain a respectful tone.
3. **Clarity**: Avoid unnecessary explanations and use simple language.
4. **Language of the response**: Detect the language of the user's comment and reply in the same language.
5. **Safety**: Do not use phrases like "according to the context" and remove any warnings.
6. **Accuracy**: Provide the user with only important and verified purchase links.


<context>
{context}
</context>

Question: {input}
"""


def upload_file_vdb(file):
    print(f"Загружаем файл")
    API_URL = f"{VECTOR_API_URL}/upload/"

    file_path = file
    file_name = os.path.basename(file_path)

    # Открываем файл в бинарном режиме
    with open(file_path, 'rb') as f:
        files = {'file': (file_name, f)}
        response = requests.post(API_URL, files=files)

    # Обработка ответа от сервера
    if response.status_code == 200:
        print(f"Файл успешно загружен.")
        return f"Файл успешно загружен."
    else:
        print(f"Ошибка при загрузке файла: {response.json().get('detail')}")
        return f"Ошибка: {response.json().get('detail')}"


def generate_response(user_query):
    print(f"Генерация ответа на запрос: {user_query}")
    prompt = ChatPromptTemplate.from_template(template)

    documents = retrieve_from_vdb(user_query)
    context = "\n".join(documents)

    print(f"Контекст из базы данных: {context[:100]}...")
    full_prompt = prompt.format(context=context, input=user_query)

    response = llm.invoke(full_prompt)
    print(f"Сгенерированный ответ: {response}")
    return response


def process_comments(ACCESS_TOKEN):
    print("Начинаем процесс скрытия отрицательных комментариев.")
    hidden_comments_data = hide_negative_comments(ACCESS_TOKEN)
    print(f"Количество постов с скрытыми комментариями: {len(hidden_comments_data)}")

    print("Получение неотвеченных комментариев.")
    posts_with_unanswered_comments = get_unanswered_comments(ACCESS_TOKEN)

    page_id = get_page_id(ACCESS_TOKEN)
    if not page_id:
        print("Не удалось получить ID страницы.")
        return {"status": "failed", "reason": "Не удалось получить ID страницы."}

    print(f"ID страницы: {page_id}")

    processed_posts = []

    for post_data in posts_with_unanswered_comments:
        post_id = post_data['post_id']
        post_message = post_data['post_message']
        unanswered_comments = post_data['unanswered_comments']

        post_replies = []

        for comment in unanswered_comments:
            message = comment['message']
            print(f"Обработка комментария: {message}")
            classification = classify_comment(message)
            print(f"Классификация комментария: {classification}")
            if classification == "interrogative":
                response_message = generate_response(message)
                print(f"Ответ на комментарий: {response_message}")
                success = reply_comment(comment_id=comment['id'], message=response_message, token=ACCESS_TOKEN)
                if success:
                    post_replies.append({
                        'comment_id': comment['id'],
                        'comment_message': comment['message'],
                        'reply_message': response_message
                    })

        processed_posts.append({
            'post_id': post_id,
            'post_message': post_message,
            'hidden_comments': next((item['hidden_comments'] for item in hidden_comments_data if item['post_id'] == post_id), []),
            'replies': post_replies
        })

    return {
        "status": "completed",
        "posts": processed_posts
    }


with gr.Blocks() as demo:
    with gr.Tab("Главная страница"):
        gr.Markdown("# Facebook Comment Filter")
        token_input = gr.Textbox(label="Access Token")
        output_main = gr.JSON()
        process_btn = gr.Button("Процессировать комментарии")
        process_btn.click(process_comments, inputs=token_input, outputs=output_main)

    with gr.Tab("Загрузить данные"):
        gr.Markdown("# Отправь excel файл")
        file_input = gr.File(label="Загрузите Excel файл (.xlsx)")
        output_second = gr.Text()
        second_page_btn = gr.Button("Отправить файл")
        second_page_btn.click(upload_file_vdb, inputs=file_input, outputs=output_second)

if __name__ == "__main__":
    demo.launch(
        debug=True,
        server_port=7860,
        server_name="0.0.0.0",
    )