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# import os
# import streamlit as st
# from dotenv import load_dotenv
# from PyPDF2 import PdfReader
# from langchain.text_splitter import CharacterTextSplitter
# from langchain_openai import OpenAIEmbeddings
# from langchain.vectorstores import FAISS
# # from langchain_community.vectorstores import FAISS
# from langchain.embeddings import HuggingFaceEmbeddings
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import ConversationalRetrievalChain
# from langchain.chat_models import ChatOpenAI
# from htmlTemplates import css, bot_template, user_template
# from langchain.embeddings import HuggingFaceInstructEmbeddings
# from langchain.llms import HuggingFaceHub
# import os
# def get_pdf_text(pdf_doc):
# text = ""
# for pdf in pdf_doc:
# pdf_reader = PdfReader(pdf)
# for page in pdf_reader.pages:
# text += page.extract_text()
# return text
# def get_text_chunk(row_text):
# text_splitter = CharacterTextSplitter(
# separator="\n",
# chunk_size = 1000,
# chunk_overlap = 200,
# length_function = len
# )
# chunk = text_splitter.split_text(row_text)
# return chunk
# def get_vectorstore(text_chunk):
# #embeddings = OpenAIEmbeddings(openai_api_key = os.getenv("OPENAI_API_KEY"))
# embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# vector = FAISS.from_texts(text_chunk,embeddings)
# return vector
# def get_conversation_chain(vectorstores):
# #llm = ChatOpenAI(openai_api_key = os.getenv("OPENAI_API_KEY"))
# llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature":0.5, "max_length":512})
# memory = ConversationBufferMemory(memory_key = "chat_history",return_messages = True)
# conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
# retriever=vectorstores.as_retriever(),
# memory=memory)
# return conversation_chain
# def user_input(user_question):
# response = st.session_state.conversation({"question":user_question})
# st.session_state.chat_history = response["chat_history"]
# for indx, msg in enumerate(st.session_state.chat_history):
# if indx % 2==0:
# st.write(user_template.replace("{{MSG}}",msg.content), unsafe_allow_html=True)
# else:
# st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
# def main():
# # load secret key
# load_dotenv()
# # config the pg
# st.set_page_config(page_title="Chat with multiple PDFs" ,page_icon=":books:")
# st.write(css, unsafe_allow_html=True)
# if "conversation" not in st.session_state:
# st.session_state.conversation = None
# st.header("Chat with multiple PDFs :books:")
# user_question = st.text_input("Ask a question about your docs")
# if user_question:
# user_input(user_question)
# # st.write(user_template.replace("{{MSG}}","Hello Robot"), unsafe_allow_html=True)
# # st.write(bot_template.replace("{{MSG}}","Hello Human"), unsafe_allow_html=True)
# # create side bar
# with st.sidebar:
# st.subheader("Your Documents")
# pdf_doc = st.file_uploader(label="Upload your documents",accept_multiple_files=True)
# if st.button("Process"):
# with st.spinner(text="Processing"):
# # get pdf text
# row_text = get_pdf_text(pdf_doc)
# # get the text chunk
# text_chunk = get_text_chunk(row_text)
# # st.write(text_chunk)
# # create vecor store
# vectorstores = get_vectorstore(text_chunk)
# # st.write(vectorstores)
# # create conversation chain
# st.session_state.conversation = get_conversation_chain(vectorstores)
# if __name__ == "__main__":
# main()
import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from pdf2image import convert_from_path
from langchain.text_splitter import CharacterTextSplitter
from sentence_transformers import SentenceTransformer
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFaceHub
from htmlTemplates import css, bot_template, user_template
from transformers import pipeline
# Function to extract text from PDF
def get_pdf_text(pdf_doc):
text = ""
for pdf in pdf_doc:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
# Function to extract images from PDF
def get_pdf_images(pdf_doc):
images = []
for pdf in pdf_doc:
images.extend(convert_from_path(pdf))
return images
# Function to split text into chunks
def get_text_chunk(row_text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunk = text_splitter.split_text(row_text)
return chunk
# Function to create vector store
def get_vectorstore(text_chunk):
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(text_chunk)
vector = FAISS.from_embeddings(embeddings)
return vector
# Function to create conversation chain
def get_conversation_chain(vectorstores):
llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature": 0.5, "max_length": 512})
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstores.as_retriever(),
memory=memory
)
return conversation_chain
# Function to handle user input
def user_input(user_question):
response = st.session_state.conversation({"question": user_question})
st.session_state.chat_history = response["chat_history"]
for indx, msg in enumerate(st.session_state.chat_history):
if indx % 2 == 0:
st.write(user_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
else:
st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
# Function to generate images from text using a DALL-E model
def generate_image_from_text(prompt):
# Ensure you have a DALL-E or similar model for text-to-image generation
generator = pipeline("text-to-image", model="dalle-mini/dalle-mini")
images = generator(prompt)
return images
# Main function
def main():
# Load secret key
load_dotenv()
# Config the page
st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
st.write(css, unsafe_allow_html=True)
if "conversation" not in st.session_state:
st.session_state.conversation = None
st.header("Chat with multiple PDFs :books:")
user_question = st.text_input("Ask a question about your docs")
if user_question:
user_input(user_question)
# Create side bar
with st.sidebar:
st.subheader("Your Documents")
pdf_doc = st.file_uploader(label="Upload your documents", accept_multiple_files=True, type=["pdf"])
if st.button("Process"):
with st.spinner(text="Processing"):
# Get PDF text
row_text = get_pdf_text(pdf_doc)
# Get the text chunk
text_chunk = get_text_chunk(row_text)
# Create vector store
vectorstores = get_vectorstore(text_chunk)
# Create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstores)
# Extract and display images from PDFs
images = get_pdf_images(pdf_doc)
for img in images:
st.image(img)
# Generate and display images from text using DALL-E
if user_question:
generated_images = generate_image_from_text(user_question)
for gen_img in generated_images:
st.image(gen_img)
if __name__ == "__main__":
main()
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