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# Q&A Chatbot
#from langchain.llms import OpenAI
from langchain import HuggingFaceHub
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env.
import streamlit as st
import os
huggingface_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
## Function to load OpenAI model and get respones
os.environ["HUGGINGFACEHUB_API_TOKEN"]=huggingface_token
llm_huggingface=HuggingFaceHub(repo_id="google/flan-t5-xxl",model_kwargs={"temperature":0.6,"max_length":64})
def get_openai_response(question):
#llm=OpenAI(model_name="text-davinci-003",temperature=0.5)
return llm_huggingface(question)
##initialize our streamlit app
st.set_page_config(page_title="Q&A Demo")
st.title("Q&A Chatbot using LangChain")
st.markdown(
"""
<style>
body {
background-color: #f0f2f6;
color: #333333;
}
</style>
""",
unsafe_allow_html=True
)
st.sidebar.title("Options")
st.sidebar.info("Welcome to the Q&A Chatbot!")
st.write("Ask me anything and I'll do my best to answer!")
input=st.text_input("Question: ",key="input",value=" ")
response=get_openai_response(input)
submit=st.button("Ask the question")
## If ask button is clicked
if submit:
st.subheader("The Answer is")
st.write(response)
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