import base64 import os from dotenv import load_dotenv import openai from langchain.embeddings.openai import OpenAIEmbeddings import streamlit as st from langchain.chains import RetrievalQA from langchain.document_loaders import PDFMinerLoader from langchain.embeddings import SentenceTransformerEmbeddings from langchain.llms import HuggingFacePipeline from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import FAISS from streamlit_chat import message from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline import torch st.set_page_config(layout="wide") load_dotenv() def process_answer(instruction, qa_chain): response = '' generated_text = qa_chain.run(instruction) return generated_text def get_file_size(file): file.seek(0, os.SEEK_END) file_size = file.tell() file.seek(0) return file_size @st.cache_resource def data_ingestion(): for root, dirs, files in os.walk("docs"): for file in files: if file.endswith(".pdf"): print(file) loader = PDFMinerLoader(os.path.join(root, file)) documents = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500) splits = text_splitter.split_documents(documents) # create embeddings here embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") vectordb = FAISS.from_documents(splits, embeddings) vectordb.save_local("faiss_index") @st.cache_resource def initialize_qa_chain(selected_model): # Constants CHECKPOINT = selected_model TOKENIZER = AutoTokenizer.from_pretrained(CHECKPOINT) BASE_MODEL = AutoModelForSeq2SeqLM.from_pretrained(CHECKPOINT, device_map=torch.device('cpu'), torch_dtype=torch.float32) pipe = pipeline( 'text2text-generation', model=BASE_MODEL, tokenizer=TOKENIZER, max_length=256, do_sample=True, temperature=0.3, top_p=0.95, # device=torch.device('cpu') ) llm = HuggingFacePipeline(pipeline=pipe) embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") vectordb = FAISS.load_local("faiss_index", embeddings) # Build a QA chain qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=vectordb.as_retriever(), ) return qa_chain @st.cache_data # function to display the PDF of a given file def display_pdf(file): try: # Opening file from file path with open(file, "rb") as f: base64_pdf = base64.b64encode(f.read()).decode('utf-8') # Embedding PDF in HTML pdf_display = f'' # Displaying File st.markdown(pdf_display, unsafe_allow_html=True) except Exception as e: st.error(f"An error occurred while displaying the PDF: {e}") # Display conversation history using Streamlit messages def display_conversation(history): for i in range(len(history["generated"])): message(history["past"][i], is_user=True, key=f"{i}_user") message(history["generated"][i], key=str(i)) def main(): # Add a sidebar for model selection model_options = [ "meta-llama/Llama-2-13b-chat-hf","MBZUAI/LaMini-T5-738M", "google/flan-t5-base", "google/flan-t5-small"] selected_model = st.sidebar.selectbox("Select Model", model_options) st.markdown("