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from dotenv import load_dotenv | |
load_dotenv() | |
import streamlit as st | |
import os | |
#from langchain import HuggingFaceHub | |
from langchain_community.llms import HuggingFaceEndpoint | |
from langchain.chains import LLMChain | |
from langchain.prompts import PromptTemplate | |
print(os.environ["HUGGINGFACEHUB_API_TOKEN"]) | |
def get_llm_response(question): | |
HUGGINGFACEHUB_API_TOKEN=os.environ["HUGGINGFACEHUB_API_TOKEN"] | |
repo_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
template = """Question: {question} | |
Answer: """ | |
prompt = PromptTemplate.from_template(template) | |
llm = HuggingFaceEndpoint( | |
repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN | |
) | |
llm_chain = LLMChain(prompt=prompt, llm=llm) | |
answer=llm_chain.run(question) | |
print(answer) | |
return answer | |
##initialize streamlit | |
st.set_page_config(page_title="Q&A Demo") | |
st.header("Langchain application") | |
question=st.text_input("input: ",key="input") | |
response=get_llm_response(question) | |
submit=st.button("Ask the question") | |
#if submit is clicked | |
if submit: | |
st.subheader("The response is") | |
st.write(response) | |