from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
from langchain_core.messages import HumanMessage, AIMessage
from langchain_groq import ChatGroq
from typing import List
import os
from src.for_streamlit.prompts import ASSISTANT_PROMPT
from langchain.memory import ConversationSummaryMemory
from dotenv import load_dotenv
load_dotenv()
os.environ["GROQ_API_KEY"]=os.getenv("GROQ_API_KEY")

class ConversationHandler:
    def __init__(self, model_name="llama-3.3-70b-versatile", temperature=0.7):
        self.chat_model = ChatGroq(
            model_name=model_name,
            temperature=temperature
        )
        self.prompt = ChatPromptTemplate.from_messages([
            ("system", ASSISTANT_PROMPT)])
        self.memory=ConversationSummaryMemory(
            llm=self.chat_model,
            max_token_limit=2000,
            return_messages=True,
            memory_key="chat_history" 
        )
    
    def give_response(self,user_input):
        chain= self.prompt|self.chat_model
        memory_variables = self.memory.load_memory_variables({})
        response=chain.invoke(
            {
                "user_query": user_input,
                "chat_history": memory_variables["chat_history"]

                
            }
        )
        print(response.content)
        self.memory.save_context(
            {"input": user_input},
            {"output": response.content}
        )
        return response
    def summarize_conversation(self) -> str:
        memory_variables =  self.memory.load_memory_variables({})
        return self.memory.predict_new_summary(
            messages=memory_variables["chat_history"],
            existing_summary=""
        )
    
    def clear_memory(self):
         self.memory.clear()