# 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()