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import streamlit as st
import os
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 PyPDFDirectoryLoader
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from dotenv import load_dotenv
import os
load_dotenv()
# Load the GROQ and OpenAI 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="Llama3-8b-8192")
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(uploaded_files):
if "vectors" not in st.session_state:
st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
# Save the uploaded files and load them
with open("uploaded_files.zip", "wb") as f:
f.write(uploaded_files.getbuffer())
# Extract the uploaded files
os.system("unzip -o uploaded_files.zip -d ./uploaded_data")
st.session_state.loader = PyPDFDirectoryLoader("./uploaded_data") # Data Ingestion
st.session_state.docs = st.session_state.loader.load() # Document Loading
st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) # Chunk Creation
st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs) # Splitting
st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings) # Vector OpenAI embeddings
uploaded_files = st.file_uploader("Upload Your PDF Files", accept_multiple_files=True, type=["pdf"])
if st.button("Documents Embedding"):
if uploaded_files:
vector_embedding(uploaded_files[0])
st.write("Vector Store DB Is Ready")
else:
st.write("Please upload PDF files.")
prompt1 = st.text_input("Enter Your Question From Documents")
import time
if prompt1:
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(f"Response time: {time.process_time() - start} seconds")
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("--------------------------------")
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