Update app.py

app.py

@@ -7,18 +7,18 @@ import os
 import pandas as pd
 import logging
 
-# Disable GPU if not available (for Hugging Face Spaces)
+
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
-# Setup logging
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Configuration
+
 MODEL_REPO = "Ahmedhassan54/Image-Classification-Model"
 MODEL_FILE = "best_model.keras"
 
-# Initialize model
+
 model = None
 
 def load_model():
@@ -33,7 +33,7 @@ def load_model():
         )
         logger.info(f"Model path: {model_path}")
         
-        # Explicitly disable GPU
+       
         with tf.device('/CPU:0'):
             model = tf.keras.models.load_model(model_path)
         logger.info("Model loaded successfully!")
@@ -41,7 +41,7 @@ def load_model():
         logger.error(f"Model loading failed: {str(e)}")
         model = None
 
-# Load model at startup
+
 load_model()
 
 def classify_image(image):
@@ -49,23 +49,23 @@ def classify_image(image):
         if image is None:
             return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
             
-        # Convert to PIL Image if numpy array
+       
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image.astype('uint8'))
             
-        # Preprocess
+     
         image = image.resize((150, 150))
         img_array = np.array(image) / 255.0
         if len(img_array.shape) == 3:
             img_array = np.expand_dims(img_array, axis=0)
             
-        # Predict
+    
         if model is not None:
             with tf.device('/CPU:0'):
                 pred = model.predict(img_array, verbose=0)
             confidence = float(pred[0][0])
         else:
-            confidence = 0.75  # Demo value
+            confidence = 0.75 
             
         results = {
             "Cat": round(1 - confidence, 4),
@@ -83,7 +83,7 @@ def classify_image(image):
         logger.error(f"Error: {str(e)}")
         return {"Error": str(e)}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
 
-# Interface
+
 with gr.Blocks() as demo:
     gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
     
@@ -105,7 +105,7 @@ with gr.Blocks() as demo:
         outputs=[label_out, plot_out]
     )
 
-    # Examples section
+  
     gr.Examples(
         examples=[
             ["https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg"],