Update app.py

app.py

@@ -1,57 +1,44 @@
 import gradio as gr
 import numpy as np
-from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array
+import time
 from PIL import Image
 import io
-import zipfile
 
-def augment_images(image_file, num_duplicates):
+def sepia(input_img):
     """
-    Augments uploaded images based on specified transformations and number of duplicates,
-    then compresses them into a ZIP file for download.
+    Applies a sepia filter to the input image.
     """
-    # Augmentation configuration
-    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')
-    
-    # Initialize ZIP file in memory
-    zip_buffer = io.BytesIO()
-    
-    with zipfile.ZipFile(zip_buffer, 'a', zipfile.ZIP_DEFLATED) as zipf:
-        img = Image.open(image_file.name).convert('RGB')
-        img = img.resize((256, 256)) # Resize image
-        x = img_to_array(img)  # Convert image to np.array
-        x = np.expand_dims(x, axis=0)  # Add batch dimension
-        
-        # Generate and save augmented images
-        for i in range(num_duplicates):
-            it = datagen.flow(x, batch_size=1)
-            batch = next(it)
-            image = Image.fromarray(batch[0].astype('uint8'), 'RGB')
-            img_byte_arr = io.BytesIO()
-            image.save(img_byte_arr, format='JPEG')
-            img_byte_arr.seek(0)
-            zipf.writestr(f"augmented_image_{i}.jpeg", img_byte_arr.getvalue())
-    
-    # Prepare ZIP file for download
-    zip_buffer.seek(0)
-    
-    return zip_buffer, 'augmented_images.zip'
+    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
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=augment_images,
-    inputs=[gr.inputs.Image(type='file', label="Upload Image"), gr.inputs.Number(default=5, label="Number of Augmented Images")],
-    outputs=[gr.outputs.File(label="Download ZIP of Augmented Images"), "text"],
-    title="Image Augmentation Tool",
-    description="Upload an image and select the number of augmented images to generate. Download the result as a ZIP file."
+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
+
+# Define Gradio interface
+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."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()