Update app.py

app.py

@@ -12,7 +12,7 @@ def predict_flower(image):
     # Preprocess image
     image = image.resize((150, 150))  # Resize the image to 150x150
     image = image.convert('RGB')  # Ensure image has 3 channels
-    image = np.array(image)
+    image = np.array(image) / 255.0  # Normalize the image to [0, 1]
     image = np.expand_dims(image, axis=0)  # Add batch dimension
     
     # Predict
@@ -22,7 +22,7 @@ def predict_flower(image):
     probabilities = tf.nn.softmax(prediction, axis=1)
     
     # Map probabilities to Flower classes
-    class_names = ['daisy', 'dandelion', 'rose','sunflower','tulip']
+    class_names = ['daisy', 'dandelion', 'rose', 'sunflower', 'tulip']
     probabilities_dict = {flower_class: round(float(probability), 2) for flower_class, probability in zip(class_names, probabilities.numpy()[0])}
     
     return probabilities_dict
@@ -42,9 +42,14 @@ if uploaded_image is not None:
     
     predictions = predict_flower(image)
     
+    # Log the predictions
+    st.write("Vorhersagen für jede Klasse:")
+    for flower_class, probability in predictions.items():
+        st.write(f"{flower_class}: {probability}")
+    
     # Find the flower with the highest probability
     highest_probability_flower = max(predictions, key=predictions.get)
     highest_probability_value = predictions[highest_probability_flower]
     
     # Display the flower with the highest probability
-    st.write(f"Es ist eine: **{highest_probability_flower}** .")
+    st.write(f"Es ist eine: **{highest_probability_flower}** mit einer Wahrscheinlichkeit von {highest_probability_value} .")