Create app.py

app.py

@@ -0,0 +1,54 @@
+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
+
+# Image Augmentation Function
+def augment_images(image_files, num_duplicates):
+    datagen = ImageDataGenerator(
+        rotation_range=40,
+        width_shift_range=0.2,
+        height_shift_range=0.2,
+        zoom_range=0.2,
+        shear_range=0.2,
+        horizontal_flip=True,
+        fill_mode='nearest')
+
+    # Create a temporary directory to store augmented images
+    with tempfile.TemporaryDirectory() as temp_dir:
+        for image_file in image_files:
+            img = Image.open(image_file).convert('RGB')  # Convert to RGB for consistency
+            img = img.resize((256, 256))  # Resize image
+            x = img_to_array(img)  # Convert the image to a numpy array
+            x = x.reshape((1,) + x.shape)  # Reshape for the data generator
+
+            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, dirs, files in os.walk(temp_dir):
+                for file in files:
+                    zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.join(temp_dir, '..')))
+        return zip_name
+
+# Gradio Interface
+def gradio_interface(image_files, num_duplicates):
+    zip_file_path = augment_images(image_files, num_duplicates)
+    return zip_file_path
+
+iface = gr.Interface(fn=gradio_interface,
+                     inputs=[gr.inputs.Image(type="file", label="Upload Images", accept="image/*", multiple=True),
+                             gr.inputs.Number(default=5, label="Number of Duplicates", min_value=1, max_value=20)],
+                     outputs=gr.outputs.File(label="Download Augmented Images"),
+                     title="Image Augmentation App",
+                     description="Upload images to generate augmented versions.")
+
+iface.launch()