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import os
import streamlit as st
from dotenv import load_dotenv
from langchain.llms import HuggingFaceEndpoint
load_dotenv()
os.environ["HUGGINGFACEHUB_API_TOKEN"]=os.getenv("HF_TOKEN")
huggingface_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
#Function to return the response
def load_answer(question):
# "text-davinci-003" model is depreciated, so using the latest one https://platform.openai.com/docs/deprecations
if question:
llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2")
#Last week langchain has recommended to use invoke function for the below please :)
answer=llm.invoke(question)
return answer
#App UI starts here
st.set_page_config(page_title="LangChain Demo - Mistral", page_icon=":robot:")
st.header("LangChain Demo - Mistral")
#Gets the user input
def get_text():
input_text = st.text_input("You: ", key="input")
return input_text
user_input=get_text()
response = load_answer(user_input)
submit = st.button('Generate')
#If generate button is clicked
if submit:
st.subheader("Answer:")
st.write(response)
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