app.py

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 { height: 680px; } 
#         </style> 
#         """)  
#         with gr.Row():
#             enable_box = gr.Textbox(placeholder='Enter OpenAI API key', 
#                                     show_label=False, interactive=True) 
#             disable_box = gr.Textbox(value='OpenAI API key is Set', interactive=False) 
#             change_api_key = gr.Button('Change Key') 
#         with gr.Row():
#             chatbot = gr.Chatbot(value=[], elem_id='chatbot')  
#             show_img = gr.Image(label='Upload PDF')

#     # Set up event handlers

#     # Event handler for submitting the OpenAI API key
#     enable_box.submit(fn=set_apikey, inputs=[enable_box], outputs=[disable_box]) 

#     # Event handler for changing the API key
#     change_api_key.click(fn=enable_api_box, outputs=[enable_box])

    
# def render_first(pdf_file): 
#     # ... Logic to process the PDF 
#     # ... Generate the first image
#     return image  

# with gr.Blocks() as demo:
#     # ... your UI setup ...

#     pdf_upload = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])  

#     # ... other event handlers ... 

#     pdf_upload.upload(fn=render_first, inputs=[pdf_upload], 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()



with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown(""" 
        <style> 
            .image-container { height: 680px; } 
        </style> 
        """)  
        with gr.Row():
            enable_box = gr.Textbox(placeholder='Enter OpenAI API key',  
                                    show_label=False, interactive=True) 
            disable_box = gr.Textbox(value='OpenAI API key is Set', interactive=False) 
            change_api_key = gr.Button('Change Key') 
        with gr.Row():
            chatbot = gr.Chatbot(value=[], elem_id='chatbot')  
            show_img = gr.Image(label='Upload PDF') 
            pdf_upload = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])  # Added

        # Event handlers
        enable_box.submit(fn=set_apikey, inputs=[enable_box], outputs=[disable_box]) 
        change_api_key.click(fn=enable_api_box, outputs=[enable_box]) 
        pdf_upload.upload(fn=render_first, inputs=[pdf_upload], outputs=[show_img])  # Corrected

        submit_btn = gr.Button('Submit')  # Added the Submit button
        submit_btn.click(        
            fn=add_text,        
            inputs=[chatbot, txt],  # Assuming 'txt' is your textbox for query input
            outputs=[chatbot],        
            queue=False    
        ).success(
            fn=generate_response,
            inputs=[chatbot, txt, pdf_upload],  # Changed from 'btn' 
            outputs=[chatbot, txt]   
        ).success(
            fn=render_file,
            inputs=[pdf_upload],  # Changed from 'btn'
            outputs=[show_img]
        )

demo.launch()