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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")
# ✅ Manage camera state
if "clear" not in st.session_state:
st.session_state.clear = False
# 🔘 Clear / Retake Photo button
if st.button("🔁 Clear / Retake Photo"):
st.session_state.clear = True
st.rerun() # ✅ use st.rerun, not experimental_rerun
# 🧼 Skip this run if clearing
if st.session_state.clear:
st.session_state.clear = False
st.stop()
# 📷 Camera Input
img_data = st.camera_input("📷 Capture the weight display")
if img_data:
st.success("✅ Image captured successfully!")
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.clear = True
st.rerun()
st.stop()
st.success(f"✅ Detected Weight: {weight} g (Confidence: {int(confidence)}%)")
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)
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