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import gradio as gr
import fitz # PyMuPDF
import re
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import ChatOpenAI
from langchain_experimental.text_splitter import SemanticChunker
import os
openai_api_key = os.getenv("OPENAI_API_KEY")
vectorstore = None
llm = None
qa_instance = None
chat_history = [] # Global chat history
def extract_text_from_pdf(pdf_bytes):
document = fitz.open("pdf", pdf_bytes)
text = ""
for page_num in range(len(document)):
page = document.load_page(page_num)
text += page.get_text()
document.close()
return text
def clean_text(text):
cleaned_text = re.sub(r'\s+', ' ', text)
cleaned_text = re.sub(r'(.)\1{2,}', r'\1', cleaned_text)
cleaned_text = re.sub(r'\b(\w+)\b(?:\s+\1\b)+', r'\1', cleaned_text)
return cleaned_text.strip()
def initialize_chatbot(cleaned_text, openai_api_key):
global vectorstore, llm, qa_instance
if vectorstore is None: # Only create embeddings and Chroma once
embeddings = OpenAIEmbeddings(api_key=openai_api_key)
text_splitter = SemanticChunker(embeddings)
docs = text_splitter.create_documents([cleaned_text])
vectorstore = Chroma.from_documents(documents=docs, embedding=embeddings)
if llm is None:
llm = ChatOpenAI(api_key=openai_api_key, temperature=0.5, model="gpt-4o", verbose=True)
retriever = MultiQueryRetriever.from_llm(retriever=vectorstore.as_retriever(), llm=llm)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
qa_instance = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)
def setup_qa_system(pdf_file):
global chat_history
if pdf_file is None:
return [("Please upload a PDF file.", "")]
extracted_text = extract_text_from_pdf(pdf_file)
cleaned_text = clean_text(extracted_text)
initialize_chatbot(cleaned_text, openai_api_key)
chat_history = [("Chatbot initialized. Please ask a question.", "")]
return chat_history
def answer_query(question):
global chat_history
if qa_instance is None:
return [("Please upload a PDF and initialize the system first.", "")]
if not question.strip():
return [("Please enter a question.", "")]
result = qa_instance({"question": question})
chat_history.append((question, result['answer']))
return chat_history
with gr.Blocks() as demo:
upload = gr.File(label="Upload PDF", type="binary", file_types=["pdf"])
chatbot = gr.Chatbot(label="Chatbot")
question = gr.Textbox(label="Ask a question", placeholder="Type your question after uploading PDF...")
upload.change(setup_qa_system, inputs=[upload], outputs=[chatbot])
question.submit(answer_query, inputs=[question], outputs=[chatbot])
if __name__ == "__main__":
demo.launch()
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