Update app.py

app.py

@@ -205,19 +205,20 @@ if picture is not None:
     webcam_ds = dataset_prod_obj.create_dataset(picture, webcam=True)
     webcam_dl = DataLoader(webcam_ds, batch_size=BATCH_SIZE)
 
-    # Testing the dataloader
-    prod_inputs = next(iter(webcam_dl))
-    st.write(prod_inputs['pixel_values'].shape)
+    ## Testing the dataloader
+    #prod_inputs = next(iter(webcam_dl))
+    #st.write(prod_inputs['pixel_values'].shape)
+
+    with st.spinner("Wait for it...", show_time=True):
+        # Run the predictions
+        prediction = prod_function(model_pretrained, prod_dl, webcam_dl)
+        predictions = torch.cat(prediction, 0).to(device)
+        match_idx = torch.argmin(predictions)
+        st.write(predictions)
+        st.write(image_paths)
     
-    # Run the predictions
-    prediction = prod_function(model_pretrained, prod_dl, webcam_dl)
-    predictions = torch.cat(prediction, 0).to(device)
-    match_idx = torch.argmin(predictions)
-    st.write(predictions)
-    st.write(image_paths)
-
-    # Display the results
-    if predictions[match_idx] <= 0.3:
-      st.write('Welcome: ',image_paths[match_idx].split('/')[-1].split('.')[0])
-    else:
-      st.write("Match not found")
+        # Display the results
+        if predictions[match_idx] <= 0.5:
+          st.write('Welcome: ',image_paths[match_idx].split('/')[-1].split('.')[0])
+        else:
+          st.write("Match not found")