import streamlit as st from PIL import Image from ocr_engine import extract_weight_from_image import urllib.parse st.set_page_config(page_title="⚖️ Auto Weight Logger", layout="centered") st.title("⚖️ Auto Weight Logger") # Initialize session state if "image_data" not in st.session_state: st.session_state.image_data = None # Clear button to retake image if st.button("🔁 Clear / Retake Photo"): st.session_state.image_data = None # Show camera if no image captured if st.session_state.image_data is None: img_data = st.camera_input("📷 Capture the weight display") if img_data: st.session_state.image_data = img_data st.experimental_rerun() # NOTE: Works fine in most Streamlit cloud spaces # Show captured image and process if st.session_state.image_data: st.success("✅ Image captured successfully!") img_data = st.session_state.image_data if len(img_data.getvalue()) > 5 * 1024 * 1024: st.error("❌ Image too large (>5MB). Please try again.") st.stop() image = Image.open(img_data) st.image(image, caption="📸 Snapshot", use_column_width=True) with st.spinner("🔍 Extracting weight..."): weight, confidence = extract_weight_from_image(image) st.write(f"🛠️ DEBUG: weight = {weight}, confidence = {confidence}") if not weight or confidence < 80: st.error(f"⚠️ OCR confidence too low ({int(confidence)}%). Please retake the photo.") if st.button("🔁 Retake Photo"): st.session_state.image_data = None st.experimental_rerun() st.stop() st.success(f"✅ Detected Weight: {weight} g (Confidence: {int(confidence)}%)") # 🌐 Salesforce URL device_id = "BAL-001" image_url = "" encoded_weight = urllib.parse.quote(str(weight)) encoded_device = urllib.parse.quote(device_id) encoded_image_url = urllib.parse.quote(image_url) salesforce_url = ( "https://autoweightlogger-dev-ed.my.salesforce-sites.com/" f"weight_logger_page?WeightInput={encoded_weight}&DeviceID={encoded_device}&ImageURL={encoded_image_url}" ) st.markdown("### 📤 Send to Salesforce") st.markdown(f"[✅ Click here to confirm and log in Salesforce]({salesforce_url})", unsafe_allow_html=True)