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 os
+import zipfile
+import tempfile
+
+def augment_images(image_file, num_duplicates):
+    datagen = ImageDataGenerator(
+        rotation_range=40,
+        width_shift_range=0.2,
+        height_shift_range=0.2,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True,
+        fill_mode='nearest')
+    
+    img = Image.open(image_file).convert('RGB')  # Convert to RGB
+    img = img.resize((256, 256))  # Resize image
+    x = img_to_array(img)  # Convert image to numpy array
+    x = x.reshape((1,) + x.shape)  # Reshape for data generator
+    
+    with tempfile.TemporaryDirectory() as temp_dir:
+        i = 0
+        for _ in datagen.flow(x, batch_size=1, save_to_dir=temp_dir, save_prefix='aug', save_format='jpeg'):
+            i += 1
+            if i >= num_duplicates:
+                break
+
+        # Zip the augmented images
+        zip_name = tempfile.mktemp(suffix='.zip')
+        with zipfile.ZipFile(zip_name, 'w', zipfile.ZIP_DEFLATED) as zipf:
+            for root, _, files in os.walk(temp_dir):
+                for file in files:
+                    zipf.write(os.path.join(root, file), arcname=file)
+    
+    return zip_name
+
+iface = gr.Interface(
+    fn=augment_images,
+    inputs=[gr.Image(label="Upload Image", source="upload"), gr.Slider(minimum=1, maximum=20, default=5, label="Number of Augmented Samples")],
+    outputs=gr.File(label="Download Augmented Images"),
+    title="Image Augmentation App",
+    description="Upload an image to generate augmented versions. Select the number of augmented duplicates you want for the image."
+)
+
+iface.launch()