Create app.py

app.py

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+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()