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import streamlit as st |
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from PIL import Image |
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from ocr_engine import extract_weight_from_image |
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import urllib.parse |
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st.set_page_config(page_title="βοΈ Auto Weight Logger", layout="centered") |
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st.title("βοΈ Auto Weight Logger") |
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if "image_data" not in st.session_state: |
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st.session_state.image_data = None |
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if st.button("π Clear / Retake Photo"): |
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st.session_state.image_data = None |
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if st.session_state.image_data is None: |
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img_data = st.camera_input("π· Capture the weight display") |
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if img_data: |
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st.session_state.image_data = img_data |
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st.experimental_rerun() |
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if st.session_state.image_data: |
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st.success("β
Image captured successfully!") |
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img_data = st.session_state.image_data |
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if len(img_data.getvalue()) > 5 * 1024 * 1024: |
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st.error("β Image too large (>5MB). Please try again.") |
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st.stop() |
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image = Image.open(img_data) |
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st.image(image, caption="πΈ Snapshot", use_column_width=True) |
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with st.spinner("π Extracting weight..."): |
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weight, confidence = extract_weight_from_image(image) |
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st.write(f"π οΈ DEBUG: weight = {weight}, confidence = {confidence}") |
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if not weight or confidence < 80: |
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st.error(f"β οΈ OCR confidence too low ({int(confidence)}%). Please retake the photo.") |
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if st.button("π Retake Photo"): |
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st.session_state.image_data = None |
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st.experimental_rerun() |
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st.stop() |
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st.success(f"β
Detected Weight: {weight} g (Confidence: {int(confidence)}%)") |
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device_id = "BAL-001" |
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image_url = "" |
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encoded_weight = urllib.parse.quote(str(weight)) |
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encoded_device = urllib.parse.quote(device_id) |
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encoded_image_url = urllib.parse.quote(image_url) |
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salesforce_url = ( |
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"https://autoweightlogger-dev-ed.my.salesforce-sites.com/" |
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f"weight_logger_page?WeightInput={encoded_weight}&DeviceID={encoded_device}&ImageURL={encoded_image_url}" |
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) |
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st.markdown("### π€ Send to Salesforce") |
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st.markdown(f"[β
Click here to confirm and log in Salesforce]({salesforce_url})", unsafe_allow_html=True) |
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