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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
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, processor):
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()
# Create Gradio input and output components
image_input = gr.inputs.Image(type='filepath', label="Input Image")
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: process_image(image, processor), # Pass the processor object using a lambda function
inputs=image_input,
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.
"""
)
# Launch the interface
interface.launch() |