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from pypdf import PdfReader
import torch
import PyPDF2
from io import BytesIO
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
import gradio as gr
import time
from langchain.memory import ConversationBufferMemory
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from langchain.document_loaders import PyPDFDirectoryLoader
CHUNK_SIZE = 1000
# Using HuggingFaceEmbeddings with the chosen embedding model
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2",model_kwargs = {"device": "cuda"})
# transformer model configuration
quant_config = BitsAndBytesConfig(
bnb_4bit_compute_dtype=torch.bfloat16
)
def load_llm():
model_id = "Deci/DeciLM-6b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
trust_remote_code=True,
device_map = "auto",
quantization_config=quant_config)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
temperature=0,
num_beams=5,
no_repeat_ngram_size=4,
early_stopping=True,
max_new_tokens=50,
)
llm = HuggingFacePipeline(pipeline=pipe)
return llm
def add_text(history, text):
if not text:
raise gr.Error('Enter text')
history = history + [(text, '')]
return history
def upload_file(file):
# file_path = [file.name for file in files]
print(type(file))
return file
def process_file(files):
# loader = PyPDFLoader(file_path= file.name)
# document = loader.load()
pdf_text = ""
for file in files:
# pdf_stream = BytesIO(file.name.content)
pdf = PyPDF2.PdfReader(file.name)
for page in pdf.pages:
pdf_text += page.extract_text()
# split into smaller chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=200)
splits = text_splitter.create_documents([pdf_text])
# create a FAISS vector store db
# embedd the chunks and store in the db
vectorstore_db = FAISS.from_documents(splits, embeddings)
#create a custom prompt
custom_prompt_template = """Given the uploaded files, generate a pecise answer to the question asked by the user.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Context= {context}
History = {history}
Question= {question}
Helpful Answer:
"""
prompt = PromptTemplate(template=custom_prompt_template, input_variables=["question", "context", "history"])
# set QA chain with memory
qa_chain_with_memory = RetrievalQA.from_chain_type(llm=load_llm(),
chain_type='stuff',
return_source_documents=True,
retriever=vectorstore_db.as_retriever(),
chain_type_kwargs={"verbose": True,
"prompt": prompt,
"memory": ConversationBufferMemory(
input_key="question",
memory_key="history",
return_messages=True) })
# get answers
return qa_chain_with_memory
def generate_bot_response(history,query, btn):
if not btn:
raise gr.Error(message='Upload a PDF')
qa_chain_with_memory = process_file(btn)
bot_response = qa_chain_with_memory({"query": query})
# return bot_response["result"]
for char in bot_response['result']:
history[-1][-1] += char
time.sleep(0.05)
yield history,''
with gr.Blocks() as demo:
with gr.Row():
with gr.Row():
chatbot = gr.Chatbot(label="DeciLM-6b-instruct bot", value=[], elem_id='chatbot')
with gr.Row():
file_output = gr.File(label="Your PDFs")
with gr.Column():
btn = gr.UploadButton("📁 Upload a PDF(s)", file_types=[".pdf"], file_count="multiple")
with gr.Column():
with gr.Column():
txt = gr.Text(show_label=False, placeholder="Enter question")
with gr.Column():
submit_btn = gr.Button('Ask')
# Event handler for uploading a PDF
btn.upload(fn=upload_file, inputs=[btn], outputs=[file_output])
submit_btn.click(
fn= add_text,
inputs=[chatbot, txt],
outputs=[chatbot],
queue=False
).success(
fn=generate_bot_response,
inputs=[chatbot, txt, btn],
outputs=[chatbot, txt]
).success(
fn=upload_file,
inputs=[btn],
outputs=[file_output]
)
if __name__ == "__main__":
demo.launch() |