app.py

import gradio as gr
from PIL import Image, ImageDraw
import json

# Define the function that will annotate the image
def annotate_image(input_image, annotations):
    # Load the image and create a drawing context
    img = Image.fromarray(input_image.astype('uint8'), 'RGB')
    draw = ImageDraw.Draw(img)

    # Draw any existing annotations on the image
    for annotation in annotations:
        x1, y1, x2, y2 = annotation['box']
        draw.rectangle([x1, y1, x2, y2], outline='red', width=2)

    # Return the annotated image as a numpy array
    return img

# Define the function that will save the annotations to a file
def save_annotations(annotations):
    with open('annotations.json', 'w') as f:
        json.dump(annotations, f)

# Define the Gradio interface
inputs = [
    gr.inputs.Image(type='numpy', label='Input Image'),
    gr.inputs.Textbox(type='json', label='Annotations', default='[]')
]

def predict(input_image, annotations):
    # Parse the annotations from the input string
    annotations = json.loads(annotations)

    # Annotate the image
    output_image = annotate_image(input_image, annotations)

    # Save the annotations to a file
    save_annotations(annotations)

    # Return the annotated image as a numpy array
    return output_image

outputs = [
    gr.outputs.Image(type='numpy', label='Output Image')
]

gr.Interface(predict, inputs, outputs, title='Image Annotator', 
             description='Annotate images using bounding boxes').launch()