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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)
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