from langchain_community.llms import HuggingFaceEndpoint
from langchain.prompts import PromptTemplate
from langchain.schema import AIMessage, HumanMessage
from langchain.chains import LLMChain
import gradio as gr
import os

from dotenv import load_dotenv

load_dotenv()

repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
llm = HuggingFaceEndpoint(
    repo_id = repo_id, 
    # huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
)


template = """You're a good chatbot. Answer this request: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template=template)
llm_chain = LLMChain(llm=llm, prompt=prompt)


def predict(message, history):
    history_langchain_format = []
    # for human, ai in history:
    #     history_langchain_format.append(HumanMessage(content=human))
    #     history_langchain_format.append(AIMessage(content=ai))
    # history_langchain_format.append(HumanMessage(content=message))
    # gpt_response = llm(history_langchain_format)
    response = llm_chain.invoke(message)['text']
    return response

gr.ChatInterface(predict).launch()