import json
import gradio as gr
from context.db_loader import SQLiteContextLoader
import constants
from helpers import load_json_file


class NeuroRavenApp:
    def __init__(self):
        self.system_prompt = None
        self.current_theme = None
        self.client = self.load_model_client()
        self.context_loader = SQLiteContextLoader()
        self.conversation_themes = self.get_conversation_themes()
        if constants.NO_CONTEXT not in self.conversation_themes:
            self.conversation_themes.append(constants.NO_CONTEXT)

    @staticmethod
    def get_conversation_themes():
        themes = load_json_file(constants.CONVERSATION_THEMES_JSON)
        return themes

    def handle_query(self, message, history, context_theme, should_save):
        if should_save and isinstance(self.context_loader, SQLiteContextLoader):
            if context_theme == constants.NO_CONTEXT:
                return constants.ERROR_CANNOT_SAVE_MESSAGE
            self.context_loader.save_message(context_theme, message)
            self.current_theme = None
            return constants.MESSAGE_SAVED_TO_CONTEXT

        if context_theme != self.current_theme:
            context = self.context_loader.load_context(context_theme)
            if context:
                self.system_prompt = constants.ANSWER_USING_CONTEXT_PROMPT.format(context=context)
            else:
                self.system_prompt = constants.DEFAULT_SYSTEM_PROMPT
            self.current_theme = context_theme

        response_generator = self.client.get_response(
            message=message,
            history=history,
            sys_prompt=self.system_prompt
        )

        # Генерация ответов по мере их поступления
        for response in response_generator:
            yield response  # Передаем каждый ответ в Gradio

    def launch_app(self):
        gr.ChatInterface(
            fn=self.handle_query,
            chatbot=gr.Chatbot(height=500),
            textbox=gr.Textbox(
                placeholder=constants.INPUT_PLACEHOLDER,
                container=False,
                scale=7
            ),
            title="NeuroRaven",
            theme="soft",
            retry_btn=None,
            clear_btn=constants.CLEAR_BUTTON_LABEL,
            additional_inputs=[
                gr.Dropdown(
                    self.conversation_themes,
                    label=constants.SELECT_CONTEXT_THEME_LABEL,
                    value=constants.NO_CONTEXT
                ),
                gr.Checkbox(
                    label=constants.SAVE_MESSAGE_LABEL,
                    value=False
                )
            ],
            css="footer{display:none !important}"
        ).launch(server_name=constants.SERVER_NAME, server_port=constants.SERVER_PORT)

    @staticmethod
    def load_model_client():
        with open("config.json") as config_file:
            config = json.load(config_file)

        model_name = config.get("model", "mini_4o")  # Значение по умолчанию
        if model_name == "gemma":
            from models.gemma_model import ModelClient
        elif model_name == "lms":
            from models.lms_model import ModelClient
        elif model_name == "mini_4o":
            from models.mini_4o_model import ModelClient
        elif model_name == "turbo_4o":
            from models.turbo_4o_model import ModelClient
        else:
            raise ValueError(f"Unknown model name: {model_name}")

        return ModelClient()


if __name__ == "__main__":
    app = NeuroRavenApp()
    app.launch_app()

import requests
import json
import secrets


class ModelClient:
    def __init__(self):
        self.session_hash = None
        self.stream_url = None
        self.join_url = "https://yuntian-deng-chatgpt.hf.space/queue/join?__theme=light"
        self.headers = {
            "accept": "*/*",
            "accept-language": "ru,en;q=0.9",
            "content-type": "application/json",
            "priority": "u=1, i",
            "sec-ch-ua": "\"Not/A)Brand\";v=\"8\", \"Chromium\";v=\"126\", \"YaBrowser\";v=\"24.7\", "
                         "\"Yowser\";v=\"2.5\"",
            "sec-ch-ua-mobile": "?0",
            "sec-ch-ua-platform": "\"Windows\"",
            "sec-fetch-dest": "empty",
            "sec-fetch-mode": "cors",
            "sec-fetch-site": "same-origin"
        }

    @staticmethod
    def _generate_random_hash(length=10):
        """Генерирует случайный хэш указанной длины"""
        characters = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
        return ''.join(secrets.choice(characters) for _ in range(length))

    def _make_join_request(self, body):
        try:
            response = requests.post(self.join_url, headers=self.headers, json=body)
            response.raise_for_status()
        except requests.exceptions.HTTPError as e:
            print(f"HTTP error occurred: {e}")
        except Exception as e:
            print(f"An error occurred: {e}")
        return None

    def _make_stream_request(self):
        try:
            response = requests.get(self.stream_url, headers={**self.headers, 'accept': "text/event-stream"},
                                    stream=True)
            response.raise_for_status()
            return response
        except requests.exceptions.HTTPError as e:
            print(f"HTTP error occurred: {e}")
        except Exception as e:
            print(f"An error occurred: {e}")
        return None

    @staticmethod
    def _process_stream_response(response):
        response_message = ""
        if response:
            for chunk in response.iter_lines():
                if chunk:
                    try:
                        event_data = json.loads(chunk.decode('utf-8').replace("data: ", ""))
                        if event_data.get('msg') == "process_generating":
                            if event_data['output']['data'][0]:
                                output = event_data['output']['data'][0][-1]
                                response_message += output[-1]  # аккумулируем текст
                                yield response_message  # отдаём текущее состояние текста
                    except json.JSONDecodeError:
                        continue
        yield response_message  # Возвращаем окончательный ответ

    def get_response(self, message, history, sys_prompt):
        if not history:
            self.session_hash = self._generate_random_hash()
            self.stream_url = f"https://yuntian-deng-chatgpt.hf.space/queue/data?session_hash={self.session_hash}"
            input_message = sys_prompt + "\n" + message
        else:
            input_message = message

        body = {
            "data": [input_message, 1, 1, len(history), history, None],
            "event_data": None,
            "fn_index": 3,
            "trigger_id": 5,
            "session_hash": self.session_hash
        }
        self._make_join_request(body)
        stream_response = self._make_stream_request()

        # Используем цикл for для последовательной обработки
        for chunk in self._process_stream_response(stream_response):
            yield chunk  # Теперь передаём каждый кусок данных Gradio