img resizer is functional

app.py

@@ -1,7 +1,37 @@
 import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+import os
+from logic.imgPreprocess import resize_img
 
-def greet(name):
-    return "Hello " + name + "!!"
+def preprocess_image(image: Image.Image, size: int):
+    # Resize using the resize_img function directly on the PIL Image
+    resized_np = resize_img(image, size=(size, size))
+    # Convert back to RGB for display (cv2 uses BGR)
+    resized_rgb = cv2.cvtColor(resized_np, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(resized_rgb)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+# Only show example images for upload, no grid or extra info
+example_dir = "data/images"
+example_files = [os.path.join(example_dir, f) for f in [
+    "bird.JPG", "cheetah.JPG", "lion.JPG", "oryx.JPG", "ostrich.JPG", "rhino.JPG", "zebra.JPG"
+]]
+
+inputs = [
+    gr.Image(type="pil", label="Upload or Select Example Image"),
+]
+
+outputs = gr.Image(type="pil", label="Resized Image")
+
+examples = [[f, 400] for f in example_files]
+
+demo = gr.Interface(
+    fn=preprocess_image,
+    inputs=inputs,
+    outputs=outputs,
+    examples=examples,
+    title="Image Resizer"
+)
 demo.launch()

logic/imgPreprocess.py

@@ -0,0 +1,45 @@
+import numpy as np
+import cv2
+from sklearn.cluster import KMeans
+from PIL import Image
+
+# can try different img sizes and different sampling times
+# try NEAREST, BILINEAR, CUBIC as well
+def resize_img(image: Image.Image, size=(400, 400)) -> np.ndarray:
+    """
+    Resize a PIL Image to a fixed size (default 800x800) and return as a NumPy array (BGR format).
+    Args:
+        image (PIL.Image.Image): Input image.
+        size (tuple): Target size as (width, height).
+    Returns:
+        np.ndarray: Resized image as a NumPy array (BGR format).
+    """
+    image_np = np.array(image)
+    # Convert RGB (PIL) to BGR (OpenCV)
+    if image_np.shape[-1] == 3:
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+    resized_np = cv2.resize(image_np, size, interpolation=cv2.INTER_LANCZOS4)
+    return resized_np
+
+
+# def color_quantize(image: Image.Image, n_colors: int = 8) -> Image.Image:
+#     """
+#     Apply color quantization to an image using KMeans clustering.
+#     Args:
+#         image (PIL.Image.Image): Input image.
+#         n_colors (int): Number of colors for quantization.
+#     Returns:
+#         PIL.Image.Image: Quantized image.
+#     """
+#     # Convert image to numpy array
+#     img_np = np.array(image)
+#     shape = img_np.shape
+#     # Flatten the image to (num_pixels, 3)
+#     img_flat = img_np.reshape(-1, 3)
+#     # Fit KMeans
+#     kmeans = KMeans(n_clusters=n_colors, random_state=42)
+#     labels = kmeans.fit_predict(img_flat)
+#     quantized_flat = kmeans.cluster_centers_[labels].astype(np.uint8)
+#     # Reshape back to original image shape
+#     quantized_img = quantized_flat.reshape(shape)
+#     return Image.fromarray(quantized_img)

requirements.txt

@@ -1 +1,6 @@
 gradio
+numpy
+scipy
+Pillow
+opencv-python
+scikit-learn

resize_image.py

@@ -0,0 +1,12 @@
+from PIL import Image
+
+def resize_image(image: Image.Image, size: tuple = (800, 800)) -> Image.Image:
+    """
+    Resize the input image to a fixed size (default 800x800 pixels).
+    Args:
+        image (PIL.Image.Image): Input image.
+        size (tuple): Target size as (width, height).
+    Returns:
+        PIL.Image.Image: Resized image.
+    """
+    return image.resize(size, Image.LANCZOS)