Update app.py

app.py

@@ -204,7 +204,7 @@ img_size = 224
 
 @st.cache_resource
 def loadModel():
-    model = load_model('Final_CTP_Model.keras')
+    model = load_model('efficientnet-fine-d1.keras')
     return model
 
 model = loadModel()
@@ -231,7 +231,7 @@ class_names = [
     
 def classifyImage(input_image):
     input_image = input_image.resize((img_size, img_size))
-    input_array = tf.keras.utils.img_to_array(input_image) / 255.0
+    input_array = tf.keras.utils.img_to_array(input_image)
 
     # Add a batch dimension 
     input_array = tf.expand_dims(input_array, 0)  # (1, 224, 224, 3)
@@ -239,20 +239,6 @@ def classifyImage(input_image):
     predictions = model.predict(input_array)[0]
     print(f"Predictions: {predictions}")
 
-        
-    probability_sum = predictions.sum() * 100
-    print(f"Sum of predictions BEFORE SOFTMAX(as percentages): {probability_sum:.2f}%")
-
-    
-    predictions = tf.nn.softmax(predictions).numpy() #testing
-    print(f"Predictions AFTER SOFTMAX: {predictions}")
-    
-    probability_sum = predictions.sum() * 100
-    print(f"Sum of predictions AFTER SOFTMAX(as percentages): {probability_sum:.2f}%")
-    
-    for i, confidence in enumerate(predictions):
-        print(f"Class {i} has {confidence*100:.2f}% confidence")
-    
     # Sort predictions to get top 5
     top_indices = np.argsort(predictions)[-5:][::-1]