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import gradio as gr
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader
import os
import fitz
from PIL import Image
# Global variables
COUNT, N = 0, 0
chat_history = []
chain = None # Initialize chain as None
# Function to set the OpenAI API key
def set_apikey(api_key):
os.environ['OPENAI_API_KEY'] = api_key
return disable_box # Update the disable_box
# Function to enable the API key input box
def enable_api_box():
return enable_box # Update the enable_box
# Function to add text to the chat history
def add_text(history, text):
if not text:
raise gr.Error('Enter text')
history = history + [(text, '')]
return history
# Function to process the PDF file and create a conversation chain
def process_file(file):
global chain # Access the global 'chain' variable
if 'OPENAI_API_KEY' not in os.environ:
raise gr.Error('Upload your OpenAI API key')
loader = PyPDFLoader(file.name)
documents = loader.load()
embeddings = OpenAIEmbeddings()
pdfsearch = Chroma.from_documents(documents, embeddings)
chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0.3),
retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
return_source_documents=True)
return chain
# Function to generate a response based on the chat history and query
def generate_response(history, query, btn):
global COUNT, N, chat_history, chain
if not btn:
raise gr.Error(message='Upload a PDF')
if COUNT == 0:
chain = process_file(btn)
COUNT += 1
result = chain({"question": query, 'chat_history': chat_history}, return_only_outputs=True)
chat_history += [(query, result["answer"])]
N = list(result['source_documents'][0])[1][1]['page']
for char in result['answer']:
history[-1][-1] += char # Update the last response
yield history, ''
# Function to render a specific page of a PDF file as an image
def render_file(file):
global N
doc = fitz.open(file.name)
page = doc[N]
pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
return image
# Gradio application setup
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown("""
<style>
.image-container { /* Customize this class name if needed */
height: 680px;
}
</style>
""")
with gr.Row():
show_img = gr.Image(label='Upload PDF')
# Set up event handlers
# Event handler for submitting the OpenAI API key
api_key.submit(fn=set_apikey, inputs=[api_key], outputs=[api_key])
# Event handler for changing the API key
change_api_key.click(fn=enable_api_box, outputs=[api_key])
# Event handler for uploading a PDF
btn.upload(fn=render_first, inputs=[btn], outputs=[show_img])
# Event handler for submitting text and generating response
submit_btn.click(
fn=add_text,
inputs=[chatbot, txt],
outputs=[chatbot],
queue=False
).success(
fn=generate_response,
inputs=[chatbot, txt, btn],
outputs=[chatbot, txt]
).success(
fn=render_file,
inputs=[btn],
outputs=[show_img]
)
demo.queue()
if __name__ == "__main__":
demo.launch() |