Update app.py

app.py

@@ -1,20 +1,31 @@
+import json
+import time
+import zipfile
+import tempfile
+from io import BytesIO
+from concurrent.futures import ThreadPoolExecutor
+
 import streamlit as st
 import roboflow
 import pandas as pd
 import matplotlib.pyplot as plt
-import zipfile
-import tempfile
+
 from shapely.geometry import Polygon
 from PIL import Image
-from io import BytesIO
-from concurrent.futures import ThreadPoolExecutor
-from google.oauth2.credentials import Credentials
+
+import gspread
+from google.oauth2.service_account import Credentials
 from googleapiclient.discovery import build
 from googleapiclient.http import MediaIoBaseUpload
-import gspread
-import time
 
-APP_VERSION = "2.1"
+
+# =========================
+# Page config
+# =========================
+st.set_page_config(page_title="Scratch Assay Segmentation", layout="wide")
+
+APP_VERSION = "2.4"
+
 
 # =========================
 # Roboflow init
@@ -27,22 +38,23 @@ model.confidence = 80
 model.overlap = 25
 dpi_value = 300
 
+
 # =========================
-# Google Drive + Sheets (OAuth2)
+# Google Drive + Sheets (Service Account)  ✅ FIX
 # =========================
-scope = ["https://www.googleapis.com/auth/drive", "https://www.googleapis.com/auth/spreadsheets"]
-credentials = Credentials(
-    token=None,
-    refresh_token=st.secrets["GOOGLE_DRIVE_REFRESH_TOKEN"],
-    token_uri="https://oauth2.googleapis.com/token",
-    client_id=st.secrets["GOOGLE_DRIVE_CLIENT_ID"],
-    client_secret=st.secrets["GOOGLE_DRIVE_CLIENT_SECRET"],
-    scopes=scope,
-)
+SCOPES = [
+    "https://www.googleapis.com/auth/drive",
+    "https://www.googleapis.com/auth/spreadsheets",
+]
+
+sa_info = json.loads(st.secrets["gcp_service_account"])
+credentials = Credentials.from_service_account_info(sa_info, scopes=SCOPES)
+
 drive_service = build("drive", "v3", credentials=credentials)
 sheets_client = gspread.authorize(credentials)
 sheet = sheets_client.open_by_url(st.secrets["feedback_sheet_url"]).sheet1
 
+
 # =========================
 # Helpers
 # =========================
@@ -77,7 +89,13 @@ def find_or_create_folder(folder_name, parent=None):
     query = f"name='{folder_name}' and mimeType='application/vnd.google-apps.folder' and trashed=false"
     if parent:
         query += f" and '{parent}' in parents"
-    results = drive_service.files().list(q=query, spaces="drive", fields="files(id, name)").execute()
+
+    results = drive_service.files().list(
+        q=query,
+        spaces="drive",
+        fields="files(id, name)"
+    ).execute()
+
     folders = results.get("files", [])
     if folders:
         return folders[0]["id"]
@@ -85,6 +103,7 @@ def find_or_create_folder(folder_name, parent=None):
     file_metadata = {"name": folder_name, "mimeType": "application/vnd.google-apps.folder"}
     if parent:
         file_metadata["parents"] = [parent]
+
     file = drive_service.files().create(body=file_metadata, fields="id").execute()
     return file.get("id")
 
@@ -97,6 +116,11 @@ def get_image_bytes(image):
 
 
 def process_image(uploaded_file, fov_um=None, pixel_size_um=None):
+    """
+    - Runs inference through Roboflow
+    - Computes area in px² and, if calibrated, in µm²
+    - Returns buffers for Original / Segmented overlay / Polygon
+    """
     try:
         safe_name = uploaded_file.name.replace(" ", "_")
         image = Image.open(uploaded_file).convert("RGB")
@@ -112,6 +136,7 @@ def process_image(uploaded_file, fov_um=None, pixel_size_um=None):
         with tempfile.NamedTemporaryFile(suffix=".png", delete=True) as temp_file:
             image.save(temp_file.name)
             prediction = safe_predict(temp_file.name)
+
             if not prediction:
                 return {
                     "Imagem": safe_name,
@@ -120,10 +145,13 @@ def process_image(uploaded_file, fov_um=None, pixel_size_um=None):
                     "SemSegmentacao": True,
                     "Exibir": image,
                     "Original": get_image_bytes(image),
+                    "Segmentada": None,
+                    "Poligono": None,
                 }
+
             prediction_data = prediction.json()
 
-        if not prediction_data["predictions"]:
+        if not prediction_data.get("predictions"):
             return {
                 "Imagem": safe_name,
                 "Área Segmentada (px²)": None,
@@ -131,20 +159,23 @@ def process_image(uploaded_file, fov_um=None, pixel_size_um=None):
                 "SemSegmentacao": True,
                 "Exibir": image,
                 "Original": get_image_bytes(image),
+                "Segmentada": None,
+                "Poligono": None,
             }
 
         points = prediction_data["predictions"][0]["points"]
-        area_px2 = calculate_polygon_area(points)
 
+        area_px2 = calculate_polygon_area(points)
         area_um2 = None
         if effective_pixel_size_um is not None:
-            area_um2 = area_px2 * (effective_pixel_size_um**2)
+            area_um2 = area_px2 * (effective_pixel_size_um ** 2)
 
         x = [p["x"] for p in points] + [points[0]["x"]]
         y = [p["y"] for p in points] + [points[0]["y"]]
 
         original_buffer = get_image_bytes(image)
 
+        # Segmented overlay
         segmented_buffer = BytesIO()
         fig, ax = plt.subplots(figsize=(6, 6), dpi=dpi_value)
         ax.imshow(image)
@@ -153,6 +184,7 @@ def process_image(uploaded_file, fov_um=None, pixel_size_um=None):
         plt.savefig(segmented_buffer, format="png", bbox_inches="tight", pad_inches=0)
         plt.close()
 
+        # Polygon only
         polygon_buffer = BytesIO()
         fig2, ax2 = plt.subplots(figsize=(6, 6), dpi=dpi_value)
         ax2.plot(x, y, "r-", linewidth=2)
@@ -178,23 +210,34 @@ def process_image(uploaded_file, fov_um=None, pixel_size_um=None):
 
 
 def save_feedback(result, avaliacao, observacao):
+    """
+    - Appends feedback to Google Sheet
+    - Uploads images to Drive for curation (Acceptable/Bad/No segmentation)
+    """
     image_name = result["Imagem"]
 
-    # 1) Sheet
+    # 1) Sheet (same structure as you had)
     sheet.append_row([image_name, avaliacao, observacao])
 
-    # 2) Drive curation
+    # 2) Drive curation (same logic as before)
     if avaliacao in ["Acceptable", "Bad", "No segmentation"]:
-        sufixo = "aceitavel" if avaliacao == "Acceptable" else "ruim" if avaliacao == "Bad" else "sem_segmentacao"
+        sufixo = (
+            "aceitavel" if avaliacao == "Acceptable"
+            else "ruim" if avaliacao == "Bad"
+            else "sem_segmentacao"
+        )
+
         parent_folder = find_or_create_folder("Feedback Segmentacoes")
         subfolder = find_or_create_folder(image_name.replace(".png", ""), parent_folder)
 
+        # Original
         resized_original = resize_image(result["Exibir"])
         buf = BytesIO()
         resized_original.save(buf, format="PNG")
         buf.seek(0)
         upload_to_drive(buf, f"original_{sufixo}.png", subfolder)
 
+        # Segmented + Polygon only if segmentation exists
         if avaliacao != "No segmentation" and result.get("Segmentada") and result.get("Poligono"):
             resized_segmented = resize_image(Image.open(BytesIO(result["Segmentada"].getvalue())))
             resized_polygon = resize_image(Image.open(BytesIO(result["Poligono"].getvalue())))
@@ -237,20 +280,17 @@ def render_feedback_block(result, prefix_key=""):
 
 
 # =========================
-# Layout / UI
+# UI
 # =========================
-st.set_page_config(page_title="Scratch Assay Segmentation", layout="wide")
-
 st.title("Scratch Assay Segmentation Tool")
 st.caption(f"Version {APP_VERSION} · Deep learning–based wound closure segmentation")
-
 st.markdown("---")
 
-# Upload block
+# Input block
 st.markdown("### Input")
 upload_option = st.radio("Choose upload type:", ["Single image", "Image folder"], horizontal=True)
 
-# Advanced settings (collapsed by default)
+# Advanced settings (collapsed)
 with st.expander("⚙️ Advanced Settings", expanded=False):
     model.confidence = st.slider("Model confidence (%)", 20, 100, 80)
     st.markdown(
@@ -281,6 +321,7 @@ results = []
 # =========================
 if upload_option == "Single image":
     uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg", "tiff"])
+
     if uploaded_file:
         st.markdown("---")
         st.markdown("### Result")
@@ -292,9 +333,7 @@ if upload_option == "Single image":
             st.markdown(f"#### {result['Imagem']}")
 
             if result["SemSegmentacao"]:
-                col = st.columns(1)[0]
-                with col:
-                    st.image(result["Exibir"], caption="Original", use_container_width=True)
+                st.image(result["Exibir"], caption="Original", use_container_width=True)
                 st.warning("No segmentation was detected for this image.")
             else:
                 col1, col2, col3 = st.columns(3)
@@ -340,9 +379,7 @@ elif upload_option == "Image folder":
 
         falhas = [f.name for f, r in zip(uploaded_files, processed) if r and r.get("SemSegmentacao")]
         if falhas:
-            st.warning(
-                f"{len(falhas)} image(s) with no segmentation detected:\n\n- " + "\n- ".join(falhas)
-            )
+            st.warning(f"{len(falhas)} image(s) with no segmentation detected:\n\n- " + "\n- ".join(falhas))
 
         zip_images_buffer = BytesIO()
         with zipfile.ZipFile(zip_images_buffer, "w") as zip_file:
@@ -351,6 +388,7 @@ elif upload_option == "Image folder":
                     continue
 
                 results.append(result)
+
                 st.markdown("---")
                 st.markdown(f"### Result {idx} · {result['Imagem']}")
 
@@ -368,7 +406,6 @@ elif upload_option == "Image folder":
 
                     render_metrics(result)
 
-                    # Build ZIP
                     zip_file.writestr(f"segmentada_{result['Imagem']}.png", result["Segmentada"].getvalue())
                     zip_file.writestr(f"poligono_{result['Imagem']}.png", result["Poligono"].getvalue())
 
@@ -423,4 +460,4 @@ elif upload_option == "Image folder":
                     file_name="segmented_images.zip",
                     mime="application/zip",
                     use_container_width=True,
-                )
+                )