app.py

import gradio as gr
import cv2
import numpy as np
from PIL import Image
import base64
from io import BytesIO
from models.image_text_transformation import ImageTextTransformation
import argparse
import torch

parser = argparse.ArgumentParser()
parser.add_argument('--gpt_version', choices=['gpt-3.5-turbo', 'gpt4'], default='gpt-3.5-turbo')
parser.add_argument('--image_caption', action='store_true', dest='image_caption', default=True, help='Set this flag to True if you want to use BLIP2 Image Caption')
parser.add_argument('--dense_caption', action='store_true', dest='dense_caption', default=True, help='Set this flag to True if you want to use Dense Caption')
parser.add_argument('--semantic_segment', action='store_true', dest='semantic_segment', default=False, help='Set this flag to True if you want to use semantic segmentation')
parser.add_argument('--image_caption_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, gpu memory larger than 14G is recommended')
parser.add_argument('--dense_caption_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, < 6G GPU is not recommended>')
parser.add_argument('--semantic_segment_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, gpu memory larger than 14G is recommended')
parser.add_argument('--contolnet_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, <6G GPU is not recommended>')

args = parser.parse_args()

device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda":
    args.image_caption_device = "cuda"
    args.dense_caption_device = "cuda"
    args.semantic_segment_device = "cuda"
    args.contolnet_device = "cuda"
else:
    args.image_caption_device = "cpu"
    args.dense_caption_device = "cpu"
    args.semantic_segment_device = "cpu"
    args.contolnet_device = "cpu"

def pil_image_to_base64(image):
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    return img_str

def add_logo():
    with open("examples/logo.png", "rb") as f:
        logo_base64 = base64.b64encode(f.read()).decode()
    return logo_base64

def process_image(image_src, options, processor):
    processor.args.semantic_segment = "Semantic Segment" in options
    gen_text = processor.image_to_text(image_src)
    gen_image = processor.text_to_image(gen_text)
    gen_image_str = pil_image_to_base64(gen_image)
    # Combine the outputs into a single HTML output
    custom_output = f'''
    <h2>Image->Text->Image:</h2>
    <div style="display: flex; flex-wrap: wrap;">
        <div style="flex: 1;">
            <h3>Image2Text</h3>
            <p>{gen_text}</p>
        </div>
        <div style="flex: 1;">
            <h3>Text2Image</h3>
            <img src="data:image/jpeg;base64,{gen_image_str}" width="100%" />
        </div>
    </div>
    '''

    return custom_output

processor = ImageTextTransformation(args)

# Create Gradio input and output components
image_input = gr.inputs.Image(type='filepath', label="Input Image")
semantic_segment_checkbox = gr.inputs.Checkbox(label="Semantic Segment", default=False)

logo_base64 = add_logo()
# Create the title with the logo
title_with_logo = f'<img src="data:image/jpeg;base64,{logo_base64}" width="400" style="vertical-align: middle;"> Understanding Image with Text'

# Create Gradio interface
interface = gr.Interface(
    fn=lambda image, options, devices: process_image(image, options, devices, processor),
    inputs=[image_input,        
            gr.CheckboxGroup(
            label="Options",
            choices=["Semantic Segment"],
            ),
            ],
    outputs=gr.outputs.HTML(),
    title=title_with_logo,
    description="""
    This code support image to text transformation. Then the generated text can do retrieval, question answering et al to conduct zero-shot.
    \n Semantic segment is very slow in cpu(~8m), best use on gpu or run local.
    """
)

# Launch the interface
interface.launch()