import streamlit as st
from langchain.llms import HuggingFaceEndpoint

# Function to return the response
def load_answer(question):
    llm = HuggingFaceEndpoint(
        repo_id="mistralai/Mistral-7B-Instruct-v0.2")  # Model link: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

    if question.strip() == "":
        return "Question cannot be empty. Please enter a valid question."
    
    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 by Sankar")

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

user_input = get_text()

submit = st.button('Generate')

# If generate button is clicked
if submit:
    if user_input.strip() == "":
        st.subheader("Answer:")
        st.write("Question cannot be empty. Please enter a valid question.")
    else:
        response = load_answer(user_input)
        st.subheader("Answer:")
        st.write(response)