Update app.py

app.py

@@ -194,8 +194,9 @@ prod_dl = DataLoader(prod_ds, batch_size=BATCH_SIZE)
 #st.write(prod_inputs['pixel_values'].shape) 
 
 
-#st.markdown("# AI Face Recognition app for automated employee attendance")
+st.markdown("# AI Face Recognition app for automated employee attendance")
 #about_tab, app_tab = st.tabs(["About the app", "Face Recognition"])
+app_tab = st.tabs(["Face Recognition"])
 ## About the app Tab
 #with about_tab:
 #    st.markdown(
@@ -223,33 +224,33 @@ prod_dl = DataLoader(prod_ds, batch_size=BATCH_SIZE)
 #        """)
 
 # Gesture recognition Tab
-#with app_tab:
-# Read image from Camera
-enable = st.checkbox("Enable camera")
-picture = st.camera_input("Take a picture", disabled=not enable)
-if picture is not None:
-    #img = Image.open(picture)
-    #picture.save(webcam_path, "JPEG")
-    #st.write('Image saved as:',webcam_path)
-
-    ## Create DataLoader for Webcam Image
-    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)
-
-    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)
+with app_tab:
+    # Read image from Camera
+    enable = st.checkbox("Enable camera")
+    picture = st.camera_input("Take a picture", disabled=not enable)
+    if picture is not None:
+        #img = Image.open(picture)
+        #picture.save(webcam_path, "JPEG")
+        #st.write('Image saved as:',webcam_path)
     
-        # 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")
+        ## Create DataLoader for Webcam Image
+        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)
+    
+        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)
+        
+            # 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")