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ce16aa1 | 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 66 67 68 69 70 | import io
import streamlit as st
from PyPDF2 import PdfReader
from dotenv import load_dotenv
from langchain_groq import ChatGroq
from langchain_community.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from PyPDF2 import PdfReader
import io
from PyPDF2 import PdfReader
import io
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(io.BytesIO(pdf))
for page in pdf_reader.pages:
text += page.extract_text() or ""
return text
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = HuggingFaceInstructEmbeddings(model_name="all-MiniLM-L12-v2")
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = ChatGroq(model="gemma2-9b-it")
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True
)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
def handle_userinput(user_question):
if 'conversation' not in st.session_state:
st.error("Conversation not initialized. Please upload and process PDF documents first.")
return
conversation_chain = st.session_state.conversation
# Process user input using the appropriate method
response = conversation_chain.run({'question': user_question})
final_answer = response.get('answer', 'Sorry, I couldn\'t find an answer.')
st.markdown(f"**Response:** {final_answer}")
st.markdown("---")
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