src/streamlit_app.py

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")

img_data = st.camera_input("📷 Capture the weight display")

if img_data:
    # ✅ BRD: Check file size limit
    if len(img_data.getvalue()) > 5 * 1024 * 1024:
        st.error("❌ Image too large (>5MB). Please try again.")
        st.stop()

    # ✅ Preview the image
    image = Image.open(img_data)
    st.image(image, caption="📸 Snapshot", use_column_width=True)

    # ✅ Extract weight and confidence
    with st.spinner("🔍 Extracting weight..."):
        weight, confidence = extract_weight_from_image(image)

    # ✅ BRD: Confidence threshold check
    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 extracted weight
    st.success(f"✅ Detected Weight: {weight} g (Confidence: {int(confidence)}%)")

    # ✅ Set device ID and image URL
    device_id = "BAL-001"
    image_url = ""  # optional, leave blank unless you host the image

    # ✅ Encode URL parameters
    encoded_weight = urllib.parse.quote(str(weight))
    encoded_device = urllib.parse.quote(device_id)
    encoded_image_url = urllib.parse.quote(image_url)

    # ✅ Salesforce link
    salesforce_url = (
        f"https://autoweightlogger-dev-ed.my.salesforce-sites.com/"
        f"weight_logger_page?WeightInput={encoded_weight}&DeviceID={encoded_device}&ImageURL={encoded_image_url}"
    )

    # ✅ Show link to confirm weight in Salesforce
    st.markdown("### 📤 Send to Salesforce")
    st.markdown(f"[✅ Click here to confirm and log in Salesforce]({salesforce_url})", unsafe_allow_html=True)