app.py

import fitz
import io
import base64
from PIL import Image
import gradio as gr
import cv2
import requests
import tempfile
import os
import openai

prompt = "Type and press Enter"

def pdf_to_img(pdf_path):
    pdf_document = fitz.open(pdf_path)
    counter = 1
    img_list = []
    for page_number in range(len(pdf_document)):
        page = pdf_document[page_number]
        image_list = page.get_images()
        for image in image_list:
            base_img = pdf_document.extract_image(image[0])
            image_data = base_img["image"]
            img = Image.open(io.BytesIO(image_data))
            extention = base_img['ext']
            img.save(open(f"image{counter}.{extention}","wb"))
            img_list.append(f"image{counter}.{extention}")
            counter += 1
    # img_list = [(
    #     (
    #         img_list
    #     ), f"label {i}")
    #     for i in range(len(img_list))
    # ]
    return (img_list)

# def show_images(image_name):
#     return image_name

def concate_image(img_input):
    img_input = img_input.replace(' ','')
    img_input = img_input.replace("'","")
    img_name_lst = img_input.split(',')
    if len(img_name_lst) == 1 :
        return img_name_lst[0]
    else:
        img1 = cv2.imread(img_name_lst[0]) 
        img2 = cv2.imread(img_name_lst[1])
        target_width = img1.shape[0]
        target_height = img1.shape[1]
        img2 = cv2.resize(img2,(target_height,target_width))
        im_v = cv2.vconcat([img1, img2])
        # im_v = cv2.cvtColor(im_v, cv2.COLOR_BGR2RGB)
        cv2.imwrite('merge_image.jpg', im_v)
        return 'merge_image.jpg'

def encode_image(image_path):
    file_path = concate_image(image_path)
    with open(file_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def api_calling(image, prompt, api_key):
    base64_image = encode_image(image)
    if len(prompt) == 0:
        prompt = "Compare the two graphs shown in the image and list the differences?"
    
    
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    payload = {
        "model": "gpt-4-vision-preview",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        "max_tokens": 500
    }
    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
    img_preview = response.json()
    return img_preview["choices"][0]["message"]["content"]


def message_and_history(img_input_name, input, history, api_key):
    history = history or []
    s = list(sum(history, ()))
    s.append(input)
    inp = '  '.join(s)
    output = api_calling(img_input_name,inp, api_key)
    if len(input) == 0:
        input = "Difference between the graphs."
        history.append((input, output))
    else:
        history.append((input, output))
    return history, history
    
title = "Compare the Images"


with gr.Blocks(theme=gr.themes.Glass(primary_hue=gr.themes.colors.slate)) as demo:
    gr.Markdown(f'<h1 style="text-align: center;">{title}</h1>')
    with gr.Row():
        with gr.Row():
            with gr.Column():
                file_input = gr.File(type="filepath", label="Upload .pdf file")
                upload_button = gr.Button(value="Show Images")
                img_gallery = gr.Gallery(label="Generated images", show_label=True, elem_id="gallery", object_fit="contain", height="auto",allow_preview=True)
                
                # output_img_thumb = gr.Image(type="filepath",label="Image Thumbnail")
                # output_img_thumb = gr.Textbox(label="Image names")
        with gr.Row():
            with gr.Column():
                 
                image_name = gr.Textbox(label="Name of the Images to be Compared")
                image_name_lst = []
                image_id_lst = []
                image_name_str = ''
                def get_select_index(evt: gr.SelectData):
                    image_name = evt.value['image']['path']
                    image_name_lst = image_name.split('/')
                    if len(image_id_lst) >= 2 :
                        image_id_lst[0] = image_id_lst[1]
                        image_id_lst[1] = image_name_lst[4]
                    else:
                        image_id_lst.append(image_name_lst[4])
                    if len(image_id_lst) == 1 :
                        image_name_str = image_id_lst[0]
                        return image_name_str
                    else:
                        image_name_str = image_id_lst[0] + ',' + image_id_lst[1]
                        return image_name_str
                img_gallery.select(get_select_index, None, image_name)
                show_button = gr.Button(value="Show Images")
                output_img = gr.Image(type="filepath",label="Selected Images")
                
        with gr.Row():
            with gr.Column():
                api_input = gr.Textbox(label= "Enter Api-key")
                show_concat = gr.Button(value="Compare Images")
                chatbot = gr.Chatbot()
                message = gr.Textbox(label="User", placeholder=prompt)
                state = gr.State()
        
    upload_button.click(pdf_to_img, inputs=file_input, outputs=[img_gallery])
    # upload_button.click(pdf_to_img, inputs=file_input, outputs=[output_img_name])
    show_button.click(concate_image, inputs=image_name, outputs=output_img)  
    show_concat.click(message_and_history, inputs=[output_img, message, state, api_input], outputs=[chatbot, state])
    message.submit(message_and_history, inputs=[output_img, message, state, api_input], outputs=[chatbot, state])
    message.submit(lambda: None, None, message, queue=False)
            
demo.launch()