Update app.py

app.py

@@ -6,43 +6,36 @@ import numpy as np
 
 path_to_model = "nova.h5"
 
-# Load the trained model
 model = tf.keras.models.load_model(path_to_model)
 
-# Set the default height and width of the uploaded image
+#default height and width of the uploaded image
 IMG_HEIGHT = 180
 IMG_WIDTH = 180
 
-# Define a function to preprocess the image
-# Define a function to preprocess the image
+# Preprocessing
 def preprocess_image(image):
-    # Resize the image to the expected input size of the model
-    img = image.resize((IMG_HEIGHT, IMG_WIDTH))
-    # Reshape the image array to match the input shape of the model
-    img_array = np.array(img).reshape((1, IMG_HEIGHT, IMG_WIDTH, 3))
-    # Normalize the pixel values
+    if image.size != (IMG_HEIGHT, IMG_WIDTH):
+        image = image.resize((IMG_HEIGHT, IMG_WIDTH))
+
+    img_array = img_to_array(image)
     img_array = img_array / 255.0
+    img_array = np.reshape(img_array, (1, IMG_HEIGHT, IMG_WIDTH, 3))
     return img_array
 
 
+
 # Create the Streamlit app
 def main():
-    # Set the title and a brief description
     st.title("Nova'23 Classification model")    
-    # Add an image upload option
     uploaded_image = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
     
-    # If an image has been uploaded, display it and make a prediction
+    # Display the image
     if uploaded_image is not None:
-        # Display the uploaded image
         image = Image.open(uploaded_image)
         st.image(image, caption='Uploaded Image', use_column_width=True)
         
-        # Preprocess the image for the model
-        input_image = preprocess_image(image)
-        
-        # Make a prediction using the model
-        prediction = model.predict(input_image)
+        input_image = preprocess_image(image) #preprocessing
+        prediction = model.predict(input_image) #predicting
         
         # Display the prediction
         if prediction[0][0] > prediction[0][1]: