Update app.py

app.py

@@ -19,11 +19,11 @@ def predict_labels_and_probabilities(image_path):
     img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     
     # Scan the image in a grid-like fashion
-    step_size = 100  # Step size for the grid
+    step_size = 50  # Step size for the grid
     predictions = []
     
-    for y in range(0, img_rgb.shape[0] - img_height, step_size):
-        for x in range(0, img_rgb.shape[1] - img_width, step_size):
+    for y in range(0, img_rgb.shape[0] - img_height + 1, step_size):
+        for x in range(0, img_rgb.shape[1] - img_width + 1, step_size):
             patch = img_rgb[y:y+img_height, x:x+img_width]
             patch_resized = cv2.resize(patch, (img_height, img_width))
             patch_array = image.img_to_array(patch_resized)
@@ -68,4 +68,10 @@ if uploaded_file is not None:
         st.write(f"Location: ({x1}, {y1}) to ({x2}, {y2})")
         
     # Display the image with rectangles
-    st.image(img_with_boxes, caption='Detected Ingredients', use_column_width=True)
+    st.image(img_with_boxes, caption='Detected Ingredients', use_column_width=True)
+
+    # Debugging information
+    if not predictions:
+        st.write("No ingredients detected with probability above the threshold.")
+    else:
+        st.write(f"Total detected patches: {len(predictions)}")