Update src/streamlit_app.py

src/streamlit_app.py

@@ -114,54 +114,39 @@ if run_button:
     elif is_multisensor and not sensor_select:
         st.session_state.error = "Please select a sensor or 'All sensors' before generating CSV."
     else:
-        with st.spinner("Processing samples..."):
-            try:
-                target_classes = parse_classes(classes_input)
-                client = init_client(api_key)
-                metrics = []
-                for sample in samples_objects:
-                    try:
-                        label = client.get_label(sample.uuid)
-                        labelset = getattr(label, 'labelset', '') or ''
-                        labeled_by = getattr(label, 'created_by', '') or ''
-                        reviewed_by = getattr(label, 'reviewed_by', '') or ''
-                        if is_multisensor and sensor_select and sensor_select != 'All sensors':
-                            frames_list = export_sensor_frames_and_annotations(label, sensor_select)
-                            sensor_val = sensor_select
-                            num_frames = len(frames_list)
-                            total_annotations = sum(len(f['annotations']) for f in frames_list)
-                            matching_annotations = sum(
-                                1
-                                for f in frames_list
-                                for ann in f['annotations']
-                                if getattr(ann, 'category_id', None) in target_classes
-                            )
-                        elif is_multisensor and (not sensor_select or sensor_select == 'All sensors'):
-                            all_sensor_frames = export_all_sensor_frames_and_annotations(label)
-                            for sensor_name, frames_list in all_sensor_frames.items():
-                                num_frames = len(frames_list)
-                                total_annotations = sum(len(f['annotations']) for f in frames_list)
-                                matching_annotations = sum(
-                                    1
-                                    for f in frames_list
-                                    for ann in f['annotations']
-                                    if getattr(ann, 'category_id', None) in target_classes
-                                )
-                                metrics.append({
-                                    'name': getattr(sample, 'name', sample.uuid),
-                                    'uuid': sample.uuid,
-                                    'labelset': labelset,
-                                    'sensor': sensor_name,
-                                    'num_frames': num_frames,
-                                    'total_annotations': total_annotations,
-                                    'matching_annotations': matching_annotations,
-                                    'labeled_by': labeled_by,
-                                    'reviewed_by': reviewed_by
-                                })
-                            continue
-                        else:
-                            frames_list = export_frames_and_annotations(label)
-                            sensor_val = ''
+        # Show loader/status message while checking dataset type and generating CSV
+        status_ctx = None
+        try:
+            status_ctx = st.status("Checking dataset type...", expanded=True)
+        except AttributeError:
+            st.info("Checking dataset type...")
+        try:
+            target_classes = parse_classes(classes_input)
+            client = init_client(api_key)
+            metrics = []
+            # Update loader after dataset type check
+            if status_ctx is not None:
+                status_ctx.update(label="Dataset type checked. Processing samples...", state="running")
+            for sample in samples_objects:
+                try:
+                    label = client.get_label(sample.uuid)
+                    labelset = getattr(label, 'labelset', '') or ''
+                    labeled_by = getattr(label, 'created_by', '') or ''
+                    reviewed_by = getattr(label, 'reviewed_by', '') or ''
+                    if is_multisensor and sensor_select and sensor_select != 'All sensors':
+                        frames_list = export_sensor_frames_and_annotations(label, sensor_select)
+                        sensor_val = sensor_select
+                        num_frames = len(frames_list)
+                        total_annotations = sum(len(f['annotations']) for f in frames_list)
+                        matching_annotations = sum(
+                            1
+                            for f in frames_list
+                            for ann in f['annotations']
+                            if getattr(ann, 'category_id', None) in target_classes
+                        )
+                    elif is_multisensor and (not sensor_select or sensor_select == 'All sensors'):
+                        all_sensor_frames = export_all_sensor_frames_and_annotations(label)
+                        for sensor_name, frames_list in all_sensor_frames.items():
                             num_frames = len(frames_list)
                             total_annotations = sum(len(f['annotations']) for f in frames_list)
                             matching_annotations = sum(
@@ -170,25 +155,52 @@ if run_button:
                                 for ann in f['annotations']
                                 if getattr(ann, 'category_id', None) in target_classes
                             )
-                        metrics.append({
-                            'name': getattr(sample, 'name', sample.uuid),
-                            'uuid': sample.uuid,
-                            'labelset': labelset,
-                            'sensor': sensor_val if is_multisensor else '',
-                            'num_frames': num_frames,
-                            'total_annotations': total_annotations,
-                            'matching_annotations': matching_annotations,
-                            'labeled_by': labeled_by,
-                            'reviewed_by': reviewed_by
-                        })
-                    except Exception as e:
+                            metrics.append({
+                                'name': getattr(sample, 'name', sample.uuid),
+                                'uuid': sample.uuid,
+                                'labelset': labelset,
+                                'sensor': sensor_name,
+                                'num_frames': num_frames,
+                                'total_annotations': total_annotations,
+                                'matching_annotations': matching_annotations,
+                                'labeled_by': labeled_by,
+                                'reviewed_by': reviewed_by
+                            })
                         continue
-                if not metrics:
-                    st.session_state.error = "No metrics could be generated for the dataset."
-                else:
-                    st.session_state.csv_content = generate_csv(metrics, dataset_identifier)
-            except Exception as e:
-                st.session_state.error = f"An error occurred: {e}"
+                    else:
+                        frames_list = export_frames_and_annotations(label)
+                        sensor_val = ''
+                        num_frames = len(frames_list)
+                        total_annotations = sum(len(f['annotations']) for f in frames_list)
+                        matching_annotations = sum(
+                            1
+                            for f in frames_list
+                            for ann in f['annotations']
+                            if getattr(ann, 'category_id', None) in target_classes
+                        )
+                    metrics.append({
+                        'name': getattr(sample, 'name', sample.uuid),
+                        'uuid': sample.uuid,
+                        'labelset': labelset,
+                        'sensor': sensor_val if is_multisensor else '',
+                        'num_frames': num_frames,
+                        'total_annotations': total_annotations,
+                        'matching_annotations': matching_annotations,
+                        'labeled_by': labeled_by,
+                        'reviewed_by': reviewed_by
+                    })
+                except Exception as e:
+                    continue
+            if not metrics:
+                st.session_state.error = "No metrics could be generated for the dataset."
+            else:
+                st.session_state.csv_content = generate_csv(metrics, dataset_identifier)
+            if status_ctx is not None:
+                status_ctx.update(label="CSV generated!", state="complete")
+        except Exception as e:
+            st.session_state.error = f"An error occurred: {e}"
+            if status_ctx is not None:
+                status_ctx.update(label="Error occurred.", state="error")
 
 if st.session_state.error:
     st.error(st.session_state.error)