Update app.py

app.py

@@ -1,76 +1,15 @@
+import numpy as np
 import gradio as gr
-from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator
-from PIL import Image
-import os
-import zipfile
 
-# Define where augmented images will be temporarily stored
-TEMP_DIR = "temp_augmented_images"
-
-# Ensure the temp directory exists
-if not os.path.exists(TEMP_DIR):
-    os.makedirs(TEMP_DIR)
-
-# Image Augmentation Function
-def augment_image(image_file, datagen, num_duplicates):
-    try:
-        img = Image.open(image_file).convert('RGB')  # Convert to RGB
-        img = img.resize((256, 256))  # Resize for consistency
-        x = img_to_array(img)  # Image to array
-        x = x.reshape((1,) + x.shape)  # Reshape for data generator
-        
-        # Augment image
-        i = 0
-        for batch 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
-    except Exception as e:
-        print(f"Error in augmenting image: {e}")
-
-def create_zip_from_temp(directory=TEMP_DIR):
-    zip_path = f"{directory}/augmented_images.zip"
-    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
-        for root, _, files in os.walk(directory):
-            for file in files:
-                if file.endswith(".jpeg"):  # Ensure only augmented images are added
-                    zipf.write(os.path.join(root, file), arcname=file)
-    return zip_path
-
-def process_images(images, num_duplicates):
-    # Data generator for augmentation
-    datagen = ImageDataGenerator(
-        rotation_range=40,
-        width_shift_range=0.2,
-        height_shift_range=0.2,
-        zoom_range=0.2,
-        fill_mode='nearest')
-
-    # Process each uploaded image
-    for image_file in images:
-        augment_image(image_file, datagen, num_duplicates)
-    
-    # Create a zip file with all augmented images
-    zip_file = create_zip_from_temp()
-    
-    # Clean up augmented images to avoid clutter
-    for file in os.listdir(TEMP_DIR):
-        if file.endswith(".jpeg"):  # Clean up only augmented images, not the zip
-            os.remove(os.path.join(TEMP_DIR, file))
-    
-    return zip_file
-
-# Gradio Interface
-demo = gr.Interface(
-    fn=process_images, 
-    inputs=[
-        gr.Files(type="file", label="Upload Images", accept=["image/jpeg", "image/png"], multiple=True),
-        gr.Slider(minimum=1, maximum=20, default=5, label="Number of Duplicates per Image")
-    ],
-    outputs=gr.File(label="Download Augmented Images"),
-    title="Image Augmentation App",
-    description="Upload images to augment them with random transformations. Download the augmented images as a zip file."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+def sepia(input_img):
+    sepia_filter = np.array([
+        [0.393, 0.769, 0.189], 
+        [0.349, 0.686, 0.168], 
+        [0.272, 0.534, 0.131]
+    ])
+    sepia_img = input_img.dot(sepia_filter.T)
+    sepia_img /= sepia_img.max()
+    return sepia_img
+
+demo = gr.Interface(sepia, gr.Image(), "image")
+demo.launch()