Update app.py

app.py

@@ -17,12 +17,26 @@ import time
 
 st.set_page_config(page_title="Scratch Assay Segmentation", layout="wide")
 
-APP_VERSION = "2.5"
+APP_VERSION = "3.0"
 DEFAULT_IMGSZ = 640
 
+# Model nomenclature aligned with the companion manuscript (Mariotto et al.,
+# Cytometry Part A 2026): Model 2 = Roboflow 3.0 Instance Segmentation Extra
+# Large with black-edge padding (Roboflow project version 24); Model 6 =
+# YOLOv11 Instance Segmentation Accurate variant with white-edge padding
+# (Roboflow project version 37). The .pt filenames are kept as the upstream
+# Roboflow version identifiers for traceability with the private model repo.
 MODEL_OPTIONS = {
-    "24": "24.pt",
-    "37": "37.pt",
+    "Model 2": "24.pt",
+    "Model 6": "37.pt",
+}
+
+# Stable, filesystem-safe key used for Drive folder names and Sheet logging.
+# Decoupled from the user-visible label so future relabeling does not affect
+# stored data.
+MODEL_STORAGE_KEY = {
+    "Model 2": "Model_2",
+    "Model 6": "Model_6",
 }
 
 
@@ -234,9 +248,12 @@ def process_image(uploaded_file, model, model_confidence, fov_um=None, pixel_siz
 def save_feedback(result, avaliacao, observacao, selected_model_label):
     image_name = result["Imagem"]
     image_base_name = image_name.rsplit(".", 1)[0]
+    storage_key = MODEL_STORAGE_KEY[selected_model_label]
 
-    # 1) Sheet
-    sheet.append_row([image_name, avaliacao, observacao, selected_model_label, APP_VERSION])
+    # 1) Sheet - store the stable storage key (Model_2 / Model_6) rather than
+    # the user-visible label, so the spreadsheet stays clean across future
+    # label tweaks.
+    sheet.append_row([image_name, avaliacao, observacao, storage_key, APP_VERSION])
 
     # 2) Drive curation
     if avaliacao in ["Acceptable", "Bad", "No segmentation"]:
@@ -247,14 +264,14 @@ def save_feedback(result, avaliacao, observacao, selected_model_label):
         )
 
         parent_folder = find_or_create_folder("Feedback Segmentacoes")
-        model_folder = find_or_create_folder(f"model_{selected_model_label}", parent_folder)
+        model_folder = find_or_create_folder(storage_key, parent_folder)
         subfolder = find_or_create_folder(image_base_name, model_folder)
 
         resized_original = resize_image(result["Exibir"])
         buf = BytesIO()
         resized_original.save(buf, format="PNG")
         buf.seek(0)
-        upload_to_drive(buf, f"original_model_{selected_model_label}_v{APP_VERSION}_{sufixo}.png", subfolder)
+        upload_to_drive(buf, f"original_{storage_key}_v{APP_VERSION}_{sufixo}.png", subfolder)
 
         if avaliacao != "No segmentation" and result.get("Segmentada") and result.get("Poligono"):
             resized_segmented = resize_image(Image.open(BytesIO(result["Segmentada"].getvalue())))
@@ -266,7 +283,7 @@ def save_feedback(result, avaliacao, observacao, selected_model_label):
                 buf.seek(0)
                 upload_to_drive(
                     buf,
-                    f"{nome}_model_{selected_model_label}_v{APP_VERSION}_{sufixo}.png",
+                    f"{nome}_{storage_key}_v{APP_VERSION}_{sufixo}.png",
                     subfolder,
                 )
 
@@ -316,11 +333,11 @@ with col_input_1:
     upload_option = st.radio("Choose upload type:", ["Single image", "Image folder"], horizontal=True)
 
 with col_input_2:
-    selected_model_label = st.selectbox("Model checkpoint", list(MODEL_OPTIONS.keys()), index=0)
+    selected_model_label = st.selectbox("Segmentation model", list(MODEL_OPTIONS.keys()), index=0)
 
 model = load_model(MODEL_OPTIONS[selected_model_label])
 
-st.caption(f"Selected model checkpoint: {selected_model_label}")
+st.caption(f"Selected model: {selected_model_label}")
 
 with st.expander("⚙️ Advanced Settings", expanded=False):
     model_confidence = st.slider("Model confidence (%)", 20, 100, 80)
@@ -352,10 +369,17 @@ with st.sidebar:
     with st.expander("About / Citation", expanded=False):
         st.markdown(
             """
-This tool was developed by the **Medical Physics Laboratory** of the Department of **Biophysics and Pharmacology – IBB, UNESP**.  
-**FAPESP Process:** 2024/01849-4.  
-**Coordination:** Prof. Allan Alves.  
+This tool was developed by the **Medical Physics Laboratory** of the Department of **Biophysics and Pharmacology – IBB, UNESP**.
+**FAPESP Process:** 2024/01849-4.
+**Coordination:** Prof. Allan Alves.
 **Development:** Nycolas Mariotto.
+
+Model nomenclature in this interface follows the companion manuscript
+(Mariotto et al., *Cytometry Part A*, 2026):
+- **Model 2**: Roboflow 3.0 Instance Segmentation Extra Large with black-edge padding (Roboflow project version 24).
+- **Model 6**: YOLOv11 Instance Segmentation Accurate variant with white-edge padding (Roboflow project version 37).
+
+Companion archive: Zenodo DOI [10.5281/zenodo.20298129](https://doi.org/10.5281/zenodo.20298129).
             """
         )