app.py

#Hello! It seems like you want to import the Streamlit library in Python. Streamlit is a powerful open-source framework used for building web applications with interactive data visualizations and machine learning models. To import Streamlit, you'll need to ensure that you have it installed in your Python environment.
#Once you have Streamlit installed, you can import it into your Python script using the import statement,

import streamlit as st

from langchain_openai import OpenAI
from langchain_huggingface import HuggingFaceEndpoint

#When deployed on huggingface spaces, this values has to be passed using Variables & Secrets setting, as shown in the video :)
#import os
#os.environ["OPENAI_API_KEY"] = "sk-PLfFwPq6y24234234234FJ1Uc234234L8hVowXdt"

import os
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"

#Function to return the response
def load_answer(question):
    #llm = OpenAI(model_name="gpt-3.5-turbo-instruct",temperature=0)
    llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3") # Model link : https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3

    answer=llm.invoke(question)
    return answer


#App UI starts here
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("LangChain Demo")

#Gets the user input
def get_text():
    input_text = st.text_input("You: ", key="input")
    return input_text


user_input=get_text()
if user_input!= "":
    response = load_answer(user_input)

submit = st.button('Generate')  

#If generate button is clicked
if submit:

    st.subheader("Answer:")

    st.write(response)