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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() | |