File size: 2,247 Bytes
b2b301a 8910a49 b2b301a 8910a49 b2b301a 1c3a492 8910a49 a83e01b 8910a49 3ff3b2c 8910a49 74040fc | 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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import streamlit as st
import os
from langchain.llms import HuggingFaceHub
from langchain_huggingface import HuggingFaceEndpoint # for accessing huggingface models
from langchain_huggingface import HuggingFaceEmbeddings # embeding the documents in the vectorstore
from langchain_huggingface import ChatHuggingFace # chat model
from langchain.prompts import ChatPromptTemplate
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter,RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS,Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
hf_tkn = os.getenv("HF_TOKEN")
st.title('Hey Dear! Ask me anything about Kenyan constitution.')
input_text = st.text_area('Write a message...')
pdfloader = PyPDFLoader('Constitution of Kenya 2010.pdf')
docs = pdfloader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=0)
texts = splitter.split_documents(docs)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
db = FAISS.from_documents(texts,embedding=embeddings)
llm = HuggingFaceEndpoint(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
max_new_tokens=512,
do_sample=False,
repetition_penalty=1.03,
huggingfacehub_api_token=hf_tkn
)
chat_model = ChatHuggingFace(llm=llm)
prompt = ChatPromptTemplate.from_template("""
Answer the following question based only on the provided context
Think step by step before providing a detailed answer
<context>
{context}
</context>
Question: {input}""")
retriever = db.as_retriever()
retrieval_chain = (
{"context":retriever,"input":RunnablePassthrough()}
| prompt
| chat_model
| StrOutputParser()
)
def capitalize_first_letter(response):
return response[0].upper() + response[1:] if response else response
if st.button('Response'):
response = retrieval_chain.invoke(input_text).replace("Based on the provided context, ", "")
response = capitalize_first_letter(response)
st.success(response)
|