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Forked from sunilkumardash9/
Created December 27, 2023 01:13
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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 = ''
enable_box = gr.Textbox.update(value=None, placeholder='Upload your OpenAI API key', interactive=True)
disable_box = gr.Textbox.update(value='OpenAI API key is Set', interactive=False)
# Function to set the OpenAI API key
def set_apikey(api_key):
os.environ['OPENAI_API_KEY'] = api_key
return disable_box
# Function to enable the API key input box
def enable_api_box():
return 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):
if 'OPENAI_API_KEY' not in os.environ:
raise gr.Error('Upload your OpenAI API key')
loader = PyPDFLoader(
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 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
yield history, ''
# Function to render a specific page of a PDF file as an image
def render_file(file):
global N
doc =
page = doc[N]
# Render the page as a PNG image with a resolution of 300 DPI
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:
# Chatbot and image display sections
with gr.Column():
with gr.Row():
with gr.Column(scale=0.8):
api_key = gr.Textbox(placeholder='Enter OpenAI API key', show_label=False, interactive=True).style(container=False)
with gr.Column(scale=0.2):
change_api_key = gr.Button('Change Key')
with gr.Row():
chatbot = gr.Chatbot(value=[], elem_id='chatbot').style(height=650)
show_img = gr.Image(label='Upload PDF', tool='select').style(height=680)
# Text input and PDF upload sections
with gr.Row():
with gr.Column(scale=0.70):
txt = gr.Textbox(
placeholder="Enter text and press enter",
with gr.Column(scale=0.15):
submit_btn = gr.Button('Submit')
with gr.Column(scale=0.15):
btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"]).style()
# Set the OpenAI API key and handle interactions
api_key.submit(fn=set_apikey, inputs=[api_key], outputs=[api_key]), outputs=[api_key])
btn.upload(fn=render_first, inputs=[btn], outputs=[show_img])
# Perform actions on text input and PDF upload, 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])
if __name__ == "__main__":
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