Delete app.py

app.py

@@ -1,48 +0,0 @@
-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(shape=None, 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()