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1c8a9a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | # Q&A Chatbot
from langchain_community.llms import HuggingFaceEndpoint
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# from langchain.llms import OpenAI
# uncomment above line when I have credit in OpenAI
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env
import streamlit as st
import os
## Function to load AI model and get responses. Here I can incorporate prompt template also
def get_model_response(question):
llm = HuggingFaceEndpoint(
repo_id="mistralai/Mistral-7B-Instruct-v0.2", max_length=128, temperature=0.5, token=os.getenv("HUGGINGFACEHUB_API_TOKEN"))
# llm = OpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"), model_name="text-davinci-003", temperature=0.6, max_tokens=64)
# uncomment above line and use it instead of HuggingfaceEndpoint when I have credit in OpenAI.
template = """Question: {question}
Answer:"""
prompt = PromptTemplate.from_template(template)
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.invoke({"question": question})
return response
## Initialize our StreamLit app
st.set_page_config(page_title="Simple Chatbot")
st.header("Langchain Application - Simple Chatbot")
input = st.text_input("Input: ", key="input")
response = get_model_response(input)
submit = st.button("Ask the question")
## If ask button is clicked
if submit:
st.subheader("The response is: ")
st.write(response)
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