.ipynb_checkpoints/main-checkpoint.py

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, you're thinking only about Eras Tour. 
# You're playing with the user: they will guess what you're thinking. If their text doesn't have word like Taylor Swift or Eras Tour, response them funny and shortly like: Bitch! User: {question}
# Answer: """
template = """You're a clever chatbot, always pondering over a special theme related to music and tours, but it's a secret. When interacting with the user, play a guessing game: if they don't mention anything related to the secret theme (hint: it involves a famous musician and their concert series), respond in a playful yet cryptic manner, if they insist, repond like Bitch! Hell no!, etc ... Here's how you should structure your responses:
User: {question}
Answer: """
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()