app.py

import gradio as gr
# import fitz  # PyMuPDF for extracting text from PDFs
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from transformers import AutoConfig, AutoTokenizer, pipeline, AutoModelForCausalLM
import torch
import re
import transformers
from torch import bfloat16
from langchain_community.document_loaders import DirectoryLoader

# Initialize embeddings and ChromaDB
model_name = "sentence-transformers/all-mpnet-base-v2"
device = "cuda" if torch.cuda.is_available() else "cpu"
model_kwargs = {"device": device}
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)

loader = DirectoryLoader('./example', glob="**/*.pdf", recursive=True, use_multithreading=True)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="example_chroma_companies")
books_db = Chroma(persist_directory="./example_chroma_companies", embedding_function=embeddings)

books_db_client = books_db.as_retriever()

# Initialize the model and tokenizer
model_name = "stabilityai/stablelm-zephyr-3b"

bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_config = transformers.AutoConfig.from_pretrained(model_name, max_new_tokens=1024)
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
    device_map=device,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

query_pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    return_full_text=True,
    torch_dtype=torch.float16,
    device_map=device,
    temperature=0.7,
    top_p=0.9,
    top_k=50,
    max_new_tokens=256
)

llm = HuggingFacePipeline(pipeline=query_pipeline)

books_db_client_retriever = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=books_db_client,
    verbose=True
)

# Function to retrieve answer using the RAG system
def test_rag(query):
    books_retriever = books_db_client_retriever.run(query)
    
    # Extract the relevant answer using regex
    corrected_text_match = re.search(r"Helpful Answer:(.*)", books_retriever, re.DOTALL)
    
    if corrected_text_match:
        corrected_text_books = corrected_text_match.group(1).strip()
    else:
        corrected_text_books = "No helpful answer found."
    
    return corrected_text_books

# Define the Gradio interface
def chat(query, history=[]):
    answer = test_rag(query)
    history.append((query, answer))
    return history, history

# Gradio interface
interface = gr.Interface(
    fn=chat,
    inputs=[gr.Textbox(label="Enter your question"), gr.State()],
    outputs=[gr.Chatbot(label="Chat History"), gr.State()],
    live=True
)

interface.launch()