import logging
from huggingface_hub import InferenceClient
from typing import List, Dict
from config import HF_TOKEN, MODEL_NAME, SYSTEM_PROMPT

# Konfiguracja logowania
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

class Chatbot:
    def __init__(self):
        logging.info("Inicjalizacja chatbota...")
        self.client = InferenceClient(api_key=HF_TOKEN)
        self.conversation_history = [
            {"role": "system", "content": SYSTEM_PROMPT}
        ]

    def generate_context(self, relevant_chunks: List[Dict]) -> str:
        context = "Kontekst z przepisów prawnych:\n\n"
        for chunk in relevant_chunks:
            context += f"{chunk['text']}\n\n"
        logging.info("Wygenerowano kontekst dla chatbota.")
        return context

    def get_response(self, user_input: str, context: str) -> str:
        messages = self.conversation_history + [
            {"role": "user", "content": f"Kontekst: {context}\n\nPytanie: {user_input}"}
        ]

        response = ""
        logging.info("Generowanie odpowiedzi dla użytkownika: %s", user_input)
        stream = self.client.chat.completions.create(
            model=MODEL_NAME,
            messages=messages,
            temperature=0.5,
            max_tokens=8192,
            top_p=0.7,
            stream=True
        )

        for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                response += content
                logging.debug("Otrzymano fragment odpowiedzi: %s", content)

        self.conversation_history.append({"role": "user", "content": user_input})
        self.conversation_history.append({"role": "assistant", "content": response})
        logging.info("Zwrócono odpowiedź: %s", response)
        return response

    def clear_history(self):
        logging.info("Czyszczenie historii konwersacji.")
        self.conversation_history = [
            {"role": "system", "content": SYSTEM_PROMPT}
        ]