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import os
import streamlit as st
from langchain_groq import ChatGroq
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 langchain_google_genai import GoogleGenerativeAIEmbeddings
import tempfile
from dotenv import load_dotenv
load_dotenv()
## Load the GROQ and Google API key
groq_api_key = os.getenv('GROQ_API_KEY')
os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY')
st.title("Gemma Model Document Q&A")
llm = ChatGroq(groq_api_key=groq_api_key, model_name="gemma2-9b-it")
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 vector_embedding(pdf_files):
st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
docs = []
for pdf_file in pdf_files:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(pdf_file.read())
temp_file_path = temp_file.name
loader = PyPDFLoader(temp_file_path)
docs.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
final_documents = text_splitter.split_documents(docs[:20])
st.session_state.vectors = FAISS.from_documents(final_documents, st.session_state.embeddings)
# File uploader
uploaded_files = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=["pdf"])
if uploaded_files and st.button("Process Uploaded Files"):
vector_embedding(uploaded_files)
st.write("Vector Store DB is Ready")
prompt1 = st.text_input("What do you want to ask from the documents?")
import time
if prompt1:
if "vectors" in st.session_state:
document_chain = create_stuff_documents_chain(llm, prompt)
retriever = st.session_state.vectors.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
start = time.process_time()
response = retrieval_chain.invoke({'input': prompt1})
st.write(response['answer'])
# With a Streamlit expander
with st.expander("Document Similarity Search"):
# Find the relevant chunks
for i, doc in enumerate(response["context"]):
st.write(doc.page_content)
st.write("--------------------------------")
else:
st.write("Please upload and process PDF files first.")
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