Upload streamlit_app.py

src/streamlit_app.py

@@ -153,13 +153,14 @@ div[data-testid="stFileUploader"] * {
 # ═══════════════════════════════════════════════════════════════
 # MODELS & OCR LOGIC
 # ═══════════════════════════════════════════════════════════════
-
+# THE KILL-SWITCH: show_spinner=False completely deletes the un-styleable white cache boxes
+@st.cache_resource(show_spinner=False)
 def load_vision_engine():
     import logging
     logging.getLogger("easyocr").setLevel(logging.ERROR)
     return easyocr.Reader(['en'], gpu=torch.cuda.is_available())
 
-
+@st.cache_resource(show_spinner=False)
 def load_trocr_model(model_path):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     
@@ -262,9 +263,10 @@ def main():
         if run_scan_trigger:
             start = time.time()
             
-            # This triggers the @st.cache_resource loaders
-            proc, model, device = load_trocr_model(m_map[model_choice])
-            crops = extract_lines(st.session_state.image_data, reader)
+            # The fully-styled, dark-mode spinner handles the wait time so the app doesn't freeze or refresh weirdly
+            with st.spinner("Allocating Neural Resources & Loading Weights..."):
+                proc, model, device = load_trocr_model(m_map[model_choice])
+                crops = extract_lines(st.session_state.image_data, reader)
             
             decoded, scores = [], []
             total_crops = len(crops)