Updated line 68 with: image_index = 89 | Updated lines 47,49,51,53,55,57 with: st.write() | Updated line 25 with: st.write('The model summary =', model) | Updated line 22 with model.summary() |

app.py

@@ -19,8 +19,10 @@ st.write('The shape of X_test = ', X_test.shape)
 # Populate the y_test shape
 st.write('The shape of y_test = ', y_test.shape)
 
+model.summary()
+
 # Populate the model summary
-st.write('The model summary =', model.summary())
+st.write('The model summary =', model)
 
 # The output of y_test equals (55505, 2) 2-Dimensional Array [Reduce the Dimensionality into 1D]
 # Ensure y_test is a 1D array (Although, the inference drawn from y_test data is that it is a 2D array)
@@ -42,11 +44,17 @@ def predict_diagnosis(image_data, image_index):
     # Preprocess the image data (e.g., reshape, normalize)
     if len(image_data.shape) == 3:
         st.write('Implementation fails with: # image_data = image_data.reshape(1, image_data.shape[0], image_data.shape[1], image_data.shape[2])')
+        st.write()
         st.write('Reshaping 3-Dimensional array into 4-Dimensional array not possible')
+        st.write()
         st.write('Reshape an array of size 56250000 into a shape of (1,1,50,50)')
+        st.write()
         st.write('Not possible because the total number of elements in the array (56250000)')
+        st.write()
         st.write("Does not match the product of the new shapes dimensions (1150*50 = 5000)")
+        st.write()
         st.write('Adding a new dimension obverse np.expand_dims not reshape')
+        st.write()
         image_data = np.expand_dims(image_data, axis=0)
 
     image_data = image_data.astype('float32') / 255 
@@ -64,8 +72,8 @@ def predict_diagnosis(image_data, image_index):
 
     return predicted_label, true_label 
 
-# Select an image index from the X_test dataset: 88
-image_index = 88
+# Select an image index from the X_test dataset: 89
+image_index = 89
 image_data = X_test[image_index]
 
 # Predict the diagnosis