Update app.py

app.py

@@ -66,27 +66,55 @@ if uploaded_file is not None:
 
     # class_indices = np.where(preds[0] > threshold)[0]
     # class_probabilities = preds[0][class_indices]
-    class_indices = np.argmax(preds[0])
+    # class_indices = np.argmax(preds[0])
     # st.write(class_indices)
 
     # The corresponding maximum probability
-    class_probabilities = preds[0][class_indices]
+    # class_probabilities = preds[0][class_indices]
 
     # print(f"Class Index: {class_index}, Max Probability: {max_probability}")
     # unprint below
     # results_text = ""
-    predicted_defects = []
+    # predicted_defects = []
     # if len(class_indices) > 0:
     # for i, class_idx in enumerate(class_indices):
-    class_name = class_labels[class_indices]
-    results_text = f"{class_name} (Class {class_indices}): Probability {class_probabilities:.2f}\n"
-    predicted_defects.append(class_name)
+    # class_name = class_labels[class_indices]
+    # results_text = f"{class_name} (Class {class_indices}): Probability {class_probabilities:.2f}\n"
+    # predicted_defects.append(class_name)
     # else:
     #     results_text = "No classes detected with a probability greater than the threshold."
+    
+    # Display classification results in a text box
+    # st.text_area("Classification Results:", value=results_text, height=200)
+
+
 
+
+    # Get the index of the class with the maximum probability
+    class_indices = np.argmax(preds[0])
+    
+    # The corresponding maximum probability
+    class_probabilities = preds[0][class_indices]
+    
+    # Get the class name for the predicted index
+    class_name = class_labels[class_indices]
+    
+    # Prepare the results text
+    results_text = f"{class_name} (Class {class_indices}): Probability {class_probabilities:.2f}\n"
+    
+    # Append the class name to the predicted defects list
+    predicted_defects = [class_name]
+    
     # Display classification results in a text box
     st.text_area("Classification Results:", value=results_text, height=200)
 
+
+
+
+
+
+    
+
     # Encode the uploaded image as Base64
     base64_image = base64.b64encode(file_bytes).decode("utf-8")
     image_data = f"data:image/jpeg;base64,{base64_image}"