Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator
+from PIL import Image
+import numpy as np
+import io
+import zipfile
+
+def augment_images(images, num_duplicates):
+    datagen = ImageDataGenerator(
+        rotation_range=40,
+        width_shift_range=0.2,
+        height_shift_range=0.2,
+        zoom_range=0.2,
+        fill_mode='nearest')
+    
+    # Initialize in-memory zip file
+    zip_buffer = io.BytesIO()
+    
+    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for i, img_file in enumerate(images):
+            img = Image.open(img_file).convert('RGB')
+            img = img.resize((256, 256))
+            x = img_to_array(img)
+            x = np.expand_dims(x, axis=0)
+            
+            for j in range(num_duplicates):
+                it = datagen.flow(x, batch_size=1)
+                batch = next(it)
+                image = Image.fromarray(batch[0].astype('uint8'), 'RGB')
+                img_byte_arr = io.BytesIO()
+                image.save(img_byte_arr, format='JPEG')
+                img_byte_arr.seek(0)
+                zipf.writestr(f"augmented_image_{i}_{j}.jpeg", img_byte_arr.getvalue())
+    
+    # Prepare the zip file for downloading
+    zip_buffer.seek(0)
+    return zip_buffer, 'augmented_images.zip'
+
+demo = gr.Interface(
+    fn=augment_images,
+    inputs=[gr.Files(label="Upload Images"), gr.Slider(minimum=1, maximum=10, default=5, label="Number of Augmented Images per Original")],
+    outputs=[gr.File(label="Download Augmented Images Zip"), 'file'],
+    title="Image Augmentation App",
+    description="Upload images to generate augmented versions. Adjust the number of augmented images per original image using the slider."
+)
+
+if __name__ == "__main__":
+    demo.launch()