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()