Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,35 +3,43 @@ from PIL import Image
 from ocr_engine import extract_weight_from_image
 import urllib.parse
 
+# ✅ App configuration
 st.set_page_config(page_title="⚖️ Auto Weight Logger", layout="centered")
 st.title("⚖️ Auto Weight Logger")
 
+# ✅ Capture image from camera
 img_data = st.camera_input("📷 Capture the weight display")
 
 if img_data is not None:
+    # ✅ Check for file size
     if len(img_data.getvalue()) > 5 * 1024 * 1024:
         st.error("❌ Image too large (>5MB). Please try again.")
         st.stop()
 
+    # ✅ Show captured image
     image = Image.open(img_data)
     st.image(image, caption="📸 Snapshot", use_column_width=True)
 
+    # ✅ Run OCR
     with st.spinner("🔍 Extracting weight..."):
         weight, confidence = extract_weight_from_image(image)
 
-    # DEBUG: This helps you test OCR output
+    # ✅ Show debug info (can be removed later)
     st.write(f"🛠️ DEBUG: weight = {weight}, confidence = {confidence}")
 
+    # ✅ Retry if OCR failed or confidence too low
     if not weight or confidence < 80:
         st.error(f"⚠️ OCR confidence too low ({int(confidence)}%). Please retake the image.")
         if st.button("🔁 Try Again"):
             st.experimental_rerun()
         st.stop()
 
+    # ✅ Show success
     st.success(f"✅ Detected Weight: {weight} g (Confidence: {int(confidence)}%)")
 
+    # ✅ Prepare Salesforce URL
     device_id = "BAL-001"
-    image_url = ""
+    image_url = ""  # Leave blank unless you host image
 
     encoded_weight = urllib.parse.quote(str(weight))
     encoded_device = urllib.parse.quote(device_id)
@@ -42,5 +50,6 @@ if img_data is not None:
         f"weight_logger_page?WeightInput={encoded_weight}&DeviceID={encoded_device}&ImageURL={encoded_image_url}"
     )
 
+    # ✅ Send to Salesforce
     st.markdown("### 📤 Send to Salesforce")
     st.markdown(f"[✅ Click here to confirm and log in Salesforce]({salesforce_url})", unsafe_allow_html=True)