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import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from dotenv import load_dotenv
import tempfile
# Show title and description.
st.title("📄 Document question answering")
st.write(
"Upload a document below and ask a question about it – Groq will answer! "
"To use this app, you need to provide an Groq API key, which you can get [here](https://console.groq.com/keys). "
)
# Ask user for their Groq API key via `st.text_input`.
# Alternatively, you can store the API key in `./.streamlit/secrets.toml` and access it
# via `st.secrets`, see https://docs.streamlit.io/develop/concepts/connections/secrets-management
# Define model options
model_options = [
"llama3-8b-8192",
"llama3-70b-8192",
"llama-3.1-8b-instant",
"llama-3.1-70b-versatile",
"llama-3.2-1b-preview",
"llama-3.2-3b-preview",
"llama-3.2-11b-text-preview",
"llama-3.2-90b-text-preview",
"mixtral-8x7b-32768",
"gemma-7b-it",
"gemma2-9b-it"
]
# Sidebar elements
with st.sidebar:
selected_model = st.selectbox("Select any Groq Model", model_options)
groq_api_key = st.text_input("Groq API Key", type="password")
if not groq_api_key:
st.info("Please add your Groq API key to continue.", icon="🗝️")
else:
# Create an Groq client.
llm = ChatGroq(groq_api_key=groq_api_key, model_name=selected_model)
prompt = ChatPromptTemplate.from_template(
"""
Answer the questions based on the provided context only.
Please provide the most accurate response based on the question.
<context>
{context}
<context>
Questions: {input}
"""
)
def create_vector_db_out_of_the_uploaded_pdf_file(pdf_file):
if "vector_store" not in st.session_state:
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(pdf_file.read())
pdf_file_path = temp_file.name
st.session_state.embeddings = HuggingFaceEmbeddings(model_name='BAAI/bge-small-en-v1.5', model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
st.session_state.loader = PyPDFLoader(pdf_file_path)
st.session_state.text_document_from_pdf = st.session_state.loader.load()
st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
st.session_state.final_document_chunks = st.session_state.text_splitter.split_documents(st.session_state.text_document_from_pdf)
st.session_state.vector_store = FAISS.from_documents(st.session_state.final_document_chunks, st.session_state.embeddings)
pdf_input_from_user = st.file_uploader("Upload the PDF file", type=['pdf'])
if pdf_input_from_user is not None:
if st.button("Create the Vector DB from the uploaded PDF file"):
if pdf_input_from_user is not None:
create_vector_db_out_of_the_uploaded_pdf_file(pdf_input_from_user)
st.success("Vector Store DB for this PDF file Is Ready")
else:
st.write("Please upload a PDF file first")
# Main section for question input and results
if "vector_store" in st.session_state:
user_prompt = st.text_input("Enter Your Question related to the uploaded PDF")
if st.button('Submit Prompt'):
if user_prompt:
if "vector_store" in st.session_state:
document_chain = create_stuff_documents_chain(llm, prompt)
retriever = st.session_state.vector_store.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
response = retrieval_chain.invoke({'input': user_prompt})
st.write(response['answer'])
else:
st.write("Please embed the document first by uploading a PDF file.")
else:
st.error('Please write your prompt') |