Update app.py

app.py

@@ -3,14 +3,16 @@ import numpy as np
 import tensorflow as tf
 from PIL import Image
 
-# --- Config ---
+# Downloaded the image classification model we trained in lab 6.1  and uploaded it to the huggingface space
 MODEL_PATH = "cats-vs-dogs-finetuned.keras"
-IMAGE_SIZE = (180, 180)  # change if your model expects a different size
+# the input size the model expects to see
+IMAGE_SIZE = (180, 180)  
 
-# Load model once
+# Load model
 model = tf.keras.models.load_model(MODEL_PATH)
 
 def predict_image(img: Image.Image):
+    #if there is no input image, the probability of cat and dog is 50-50
     if img is None:
         return {"Cat": 0.5, "Dog": 0.5}
 
@@ -20,7 +22,7 @@ def predict_image(img: Image.Image):
     x = np.expand_dims(x, 0)  # (1, H, W, 3)
 
     # Predict: model outputs shape (1,1) with sigmoid for "Dog" probability
-    p_dog = float(model.predict(x, verbose=0)[0, 0])  # cast to Python float
+    p_dog = float(model.predict(x)[0, 0])  
     return {"Cat": 1.0 - p_dog, "Dog": p_dog}
 
 demo = gr.Interface(