import streamlit as st import os from streamlit_chat import message import numpy as np import pandas as pd from io import StringIO import PyPDF2 from tqdm import tqdm import math # import json # st.config(PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION="python") # from datasets import load_dataset # dataset = load_dataset("wikipedia", "20220301.en", split="train[240000:250000]") # wikidata = [] # for record in dataset: # wikidata.append(record["text"]) # wikidata = list(set(wikidata)) # # print("\n".join(wikidata[:5])) # # print(len(wikidata)) from sentence_transformers import SentenceTransformer import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' if device != 'cuda': st.markdown(f"you are using {device}. This is much slower than using " "a CUDA-enabled GPU. If on colab you can change this by " "clicking Runtime > change runtime type > GPU.") model = SentenceTransformer("all-MiniLM-L6-v2", device=device) st.divider() # Creating a Index(Pinecone Vector Database) import os # import pinecone from pinecone.grpc import PineconeGRPC PINECONE_API_KEY=os.getenv("PINECONE_API_KEY") PINECONE_ENV=os.getenv("PINECONE_ENV") PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT") # pc = PineconeGRPC( api_key=os.environ.get("PINECONE_API_KEY") ) # Now do stuff if 'my_index' not in pc.list_indexes().names(): pc.create_index( name='my_index', dimension=1536, metric='euclidean', spec=ServerlessSpec( cloud='aws', region='us-west-2' ) ) def connect_pinecone(): pinecone = PineconeGRPC(api_key=PINECONE_API_KEY, environment=PINECONE_ENV) # st.code(pinecone) # st.divider() # st.text(pinecone.list_indexes().names()) # st.divider() # st.text(f"Succesfully connected to the pinecone") return pinecone def get_pinecone_semantic_index(pinecone): index_name = "sematic-search" # only create if it deosnot exists if index_name not in pinecone.list_indexes().names(): pinecone.create_index( name=index_name, description="Semantic search", dimension=model.get_sentence_embedding_dimension(), metric="cosine", spec=ServerlessSpec( cloud='gcp', region='us-central1' ) ) # now connect to index index = pinecone.Index(index_name) # st.text(f"Succesfully connected to the pinecone index") return index def chat_actions(): pinecone = connect_pinecone() index = get_pinecone_semantic_index(pinecone) st.session_state["chat_history"].append( {"role": "user", "content": st.session_state["chat_input"]}, ) query_embedding = model.encode(st.session_state["chat_input"]) # create the query vector query_vector = query_embedding.tolist() # now query vector database result = index.query(query_vector, top_k=5, include_metadata=True) # xc is a list of tuples # Create a list of lists data = [] i = 0 for res in result['matches']: i = i + 1 data.append([f"{i}⭐", res['score'], res['metadata']['text']]) # Create a DataFrame from the list of lists resdf = pd.DataFrame(data, columns=['TopRank', 'Score', 'Text']) with st.sidebar: st.markdown("*:red[semantic search results]* with **:green[Retrieval Augmented Generation]** ***(RAG)***.") st.dataframe(resdf) for res in result['matches']: st.session_state["chat_history"].append( { "role": "assistant", "content": f"{res['metadata']['text']}", }, # This can be replaced with your chat response logic ) break; if "chat_history" not in st.session_state: st.session_state["chat_history"] = [] st.chat_input("show me the contents of ML paper published on xxx with article no. xx?", on_submit=chat_actions, key="chat_input") for i in st.session_state["chat_history"]: with st.chat_message(name=i["role"]): st.write(i["content"]) ### Creating a Index(Pinecone Vector Database) # %%writefile .env # PINECONE_API_KEY=os.getenv("PINECONE_API_KEY") # PINECONE_ENV=os.getenv("PINECONE_ENV") # PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT") # import os # import pinecone # from pinecone import Index, GRPCIndex # pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV) # st.text(pinecone) def create_embeddings(): # Get the uploaded file uploaded_file = st.session_state["uploaded_file"] # Read the contents of the file file_contents = uploaded_file.read() st.write("created_embeddings") # Display the contents of the file st.write(file_contents) def print_out(pages): for i in range(len(pages)): text = pages[i].extract_text().strip() st.write(f"Page {i} : {text}") def combine_text(pages): concatenates_text = "" for page in tqdm(pages): text = page.extract_text().strip() concatenates_text += text bytesize = concatenates_text.encode("utf-8") p = math.pow(1024, 2) mbsize = round(len(bytesize) / p, 2) st.write(f"There are {len(concatenates_text)} characters in the pdf with {mbsize}MB size") # def promt_engineer(text): # promt_template = """ # write a concise summary of the following text delimited by triple backquotes. # return your response in bullet points which convers the key points of the text. # ```{text}``` # BULLET POINT SUMMARY: # """ with st.sidebar: st.markdown(""" ***Follow this steps*** - upload pdf file to train the model on your own docs - wait see success message on train completion - Takes couple of mins after upload the pdf - Now Chat with model to get the summarized info or Generative reponse """) uploaded_files = st.file_uploader('Choose your .pdf file', type="pdf", accept_multiple_files=True, key="uploaded_file", on_change=create_embeddings) for uploaded_file in uploaded_files: # To read file as bytes: # bytes_data = uploaded_file.getvalue() # st.write(bytes_data) # To convert to a string based IO: # stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) # st.write(stringio) # To read file as string: # string_data = stringio.read() # st.write(string_data) # Can be used wherever a "file-like" object is accepted: # dataframe = pd.read_csv(uploaded_file) # st.write(dataframe) reader = PyPDF2.PdfReader(uploaded_file) pages = reader.pages print_out(pages) combine_text(pages) # promt_engineer(text)