Update app.py

app.py

@@ -1,44 +1,45 @@
 import gradio as gr
-import numpy as np
-import time
+from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator
 from PIL import Image
+import numpy as np
+import os
+import zipfile
 import io
 
-def sepia(input_img):
-    """
-    Applies a sepia filter to the input image.
-    """
-    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
+# 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,
+        fill_mode='nearest')
+
+    augmented_images = []
+    for image_file in image_files:
+        try:
+            img = Image.open(image_file).convert('RGB')
+            img = img.resize((256, 256))
+            x = img_to_array(img)
+            x = x.reshape((1,) + x.shape)
+            i = 0
+            for _ in datagen.flow(x, batch_size=1):
+                i += 1
+                augmented_images.append(x[0])
+                if i >= num_duplicates:
+                    break
+        except Exception as e:
+            print(f"Error processing image: {e}")
 
-def fake_diffusion_and_sepia(steps):
-    """
-    A generator function that simulates a fake diffusion process for a specified number of steps.
-    After the final step, applies a sepia filter to the image.
-    """
-    rng = np.random.default_rng()
-    for i in range(steps):
-        # Simulate the diffusion process
-        time.sleep(1)  # Wait to simulate processing time
-        image = rng.random(size=(256, 256, 3))  # Generate a random image
-        if i == steps - 1:  # Apply sepia filter on the last step
-            image = sepia(image)
-        yield image
+    return augmented_images
 
-# Define Gradio interface
+# Gradio UI
 demo = gr.Interface(
-    fn=fake_diffusion_and_sepia,
-    inputs=gr.Slider(minimum=1, maximum=10, default=5, step=1, label="Number of Steps"),
-    outputs=gr.Image(type="numpy", label="Sepia Image"),
-    title="Fake Diffusion with Sepia Filter",
-    description="Generates a series of images simulating a diffusion process. The final image is processed with a sepia filter."
-)
+    fn=augment_images,
+    inputs=gr.File(label="Upload Images", multiple=True, file_types=["jpg", "jpeg", "png"]),
+    outputs=gr.Image(label="Augmented Images"),
+    examples=[["images/cat.jpg"], ["images/dog.jpg"]],
+    description="Image Augmentation App",
+    allow_flagging=False)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()