Adding metadata + new class features

app.py

@@ -1,13 +1,14 @@
 import gradio as gr
 import random
 import csv
+import datetime
 
 class_names = ['cat', 'dog']
 
 def update_dropdown(className):
     class_names.append(className)
     updated_choices = gr.Dropdown(choices=class_names)
-    return updated_choices
+    return updated_choices, updated_choices
 
 def show_picked_class(className):
     return className
@@ -27,7 +28,7 @@ def image_classifier(inp):
 
     labeled_result = {name:score for name, score in zip(class_names, normalized_percentages)}
 
-    return labeled_result, gr.Dropdown(choices=class_names)
+    return labeled_result
 
 demo = gr.Blocks()
 
@@ -50,10 +51,9 @@ with demo as app:
     b2 = gr.Button("Show me the picked class")
     picked_class = gr.Textbox()
 
-    b1.click(update_dropdown, inputs=text_input, outputs=text_options)
     b2.click(show_picked_class, inputs=text_options, outputs=picked_class)
 
-    process_btn.click(image_classifier, inputs=inp_img, outputs=[out_txt, text_options])
+    process_btn.click(image_classifier, inputs=inp_img, outputs=out_txt)
     clear_btn.click(lambda:(
         gr.update(value=None),
         gr.update(value=None)
@@ -83,6 +83,8 @@ with demo as app:
         images_label = gr.Dropdown(class_names, label="Class Label", multiselect=False)
         b3 = gr.Button("Save and change the label using dropdown")
 
+        b1.click(update_dropdown, inputs=text_input, outputs=[text_options, images_label])
+
         multiple_inputs.upload(show_to_gallery, inputs=multiple_inputs, outputs=[gallery, imgs])
 
         gallery.select(get_select_index, None, selected)
@@ -95,23 +97,39 @@ with demo as app:
 
         b3.click(change_labels, [imgs, selected, images_label], [imgs, gallery])
 
+        gr.Markdown('### Save Metadata Into .csv')
         b4 = gr.Button("Upload to metadata")
 
         def upload_metadata(imgs):
-            with open('metadata.csv', mode='w', newline='') as csv_file:
+            time_uploaded = datetime.datetime.now()
+            time_str = time_uploaded.strftime("%m-%d-%Y_%H-%M-%S")
+
+            with open(f'{time_str}.csv', mode='w', newline='') as csv_file:
                 # Create a CSV writer
                 csv_writer = csv.writer(csv_file)
 
                 # Write the header row
-                csv_writer.writerow(['File_Path', 'Label'])
+                csv_writer.writerow(['image_path', 'ground_truth', 'time_uploaded', 'prediction_label', 'prediction_conf'])
+
+                for image in imgs:
+                    image.append(time_str)
+                    model_output = image_classifier(image)
+                    # Sort the label and confidence output in descending order
+                    sorted_output = dict(sorted(model_output.items(), key=lambda item: item[1], reverse=True))
+
+                    # Extract the label with the highest value
+                    label_prediction = next(iter(sorted_output))
+                    image.append(label_prediction)
+
+                    label_confidence = model_output[label_prediction]
+                    image.append(label_confidence)
 
                 # Write the data rows
                 csv_writer.writerows(imgs)
 
             print(f"Metadata CSV file has been created.")
-            return imgs
 
-        b4.click(upload_metadata, imgs, imgs)
+        b4.click(upload_metadata, inputs=imgs)
 
 
 demo.launch(debug=True)