Updated lines 59-82 with: [# notation] | Updated lines 88-94 with: [# notation] | Updated lines 108-110 with: [# notation] | Updated lines 112-117 with: # Output the predictions st.write(f'Class Mappings: {class_mappings}') st.write("\nNormal Image Prediction:", np.round(predictions[0], 3)[0]) st.write("Glioma Image Prediction:", np.round(predictions[1], 3)[0]) st.write("Meningioma Image Prediction:", np.round(predictions[2], 3)[0]) st.write("Pituitary Image Prediction:", np.round(predictions[3], 3)[0])

app.py

@@ -56,42 +56,42 @@ image_paths = [
     'G_1.jpg',
     'M_1.jpg',
     'P_1.jpg',
-    'N_1_BR_.jpg',
-    'G_1_BR_.jpg',
-    'M_1_BR_.jpg',
-    'P_1_BR_.jpg',
-    'N_1_DA_.jpg',
-    'G_1_DA_.jpg',
-    'M_1_DA_.jpg',
-    'P_1_DA_.jpg',
-    'N_1_HF_.jpg',
-    'G_1_HF_.jpg',
-    'M_1_HF_.jpg',
-    'P_1_HF_.jpg',
-    'N_1_RO_.jpg',
-    'G_1_RO_.jpg',
-    'M_1_RO_.jpg',
-    'P_1_RO_.jpg',
-    'N_1_SP_.jpg',
-    'G_1_SP_.jpg',
-    'M_1_SP_.jpg',
-    'P_1_SP_.jpg',
-    'N_1_VF_.jpg',
-    'G_1_VF_.jpg',
-    'M_1_VF_.jpg',
-    'P_1_VF_.jpg'
+    #'N_1_BR_.jpg',
+    #'G_1_BR_.jpg',
+    #'M_1_BR_.jpg',
+    #'P_1_BR_.jpg',
+   #'N_1_DA_.jpg',
+    #'G_1_DA_.jpg',
+    #'M_1_DA_.jpg',
+    #'P_1_DA_.jpg',
+    #'N_1_HF_.jpg',
+    #'G_1_HF_.jpg',
+    #'M_1_HF_.jpg',
+    #'P_1_HF_.jpg',
+    #'N_1_RO_.jpg',
+    #'G_1_RO_.jpg',
+    #'M_1_RO_.jpg',
+    #'P_1_RO_.jpg',
+    #'N_1_SP_.jpg',
+    #'G_1_SP_.jpg',
+    #'M_1_SP_.jpg',
+    #'P_1_SP_.jpg',
+    #'N_1_VF_.jpg',
+    #'G_1_VF_.jpg',
+    #'M_1_VF_.jpg',
+    #'P_1_VF_.jpg'
    
 ]
 
 # True labels for images
 true_labels = [
     'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # Original tumor types
-    'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # BR_ (Brightness)
-    'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # DA_ (Darkness)
-    'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # HF_ (High Frequency)
-    'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # RO_ (Rotation)
-    'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # SP_ (Salt and Pepper)
-    'Notumor', 'Glioma', 'Meningioma', 'Pituitary'   # VF_ (Vertical Flip)
+    #'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # BR_ (Brightness)
+    #'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # DA_ (Darkness)
+    #'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # HF_ (High Frequency)
+    #'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # RO_ (Rotation)
+    #'Notumor', 'Glioma', 'Meningioma', 'Pituitary',  # SP_ (Salt and Pepper)
+    #'Notumor', 'Glioma', 'Meningioma', 'Pituitary'   # VF_ (Vertical Flip)
 ]
 
 # Load and preprocess images, then make predictions
@@ -105,12 +105,16 @@ predicted_labels = [inv_class_mappings[np.argmax(one_hot)] for one_hot in predic
 st.markdown("<h1 style='text-align: center; color: navy;'>Brain Tumor One Hot Encode TF Model</h1>", unsafe_allow_html=True)
 
 # Output the predictions
-st.write(f'Class Mappings: {class_mappings}')
-for i, (prediction, true_label) in enumerate(zip(predicted_labels, true_labels)):
-    st.write(f"Image {i+1} Prediction: {prediction}, True Label: {true_label}")
-
-
+#st.write(f'Class Mappings: {class_mappings}')
+#for i, (prediction, true_label) in enumerate(zip(predicted_labels, true_labels)):
+    #st.write(f"Image {i+1} Prediction: {prediction}, True Label: {true_label}")
 
+# Output the predictions
+st.write(f'Class Mappings: {class_mappings}')
+st.write("\nNormal Image Prediction:", np.round(predictions[0], 3)[0])
+st.write("Glioma Image Prediction:", np.round(predictions[1], 3)[0])
+st.write("Meningioma Image Prediction:", np.round(predictions[2], 3)[0])
+st.write("Pituitary Image Prediction:", np.round(predictions[3], 3)[0])
 
 
 # Display images with predictions