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Runtime error
| import sys, os, time # time kept for perf logging in inference calls | |
| sys.path.append(os.path.dirname(os.path.dirname(__file__))) # import inference/ | |
| import streamlit as st | |
| from PIL import Image | |
| from inference.logger import get_logger | |
| log = get_logger("app") | |
| st.set_page_config(page_title="Yugensys Vision AI — Manufacturing", layout="wide") | |
| from inference.detect import detect_assets, _model as get_poc1_model | |
| from inference.classify import classify_qc, get_efficientnet, get_yolo | |
| # --------------------------------------------------------------------------- | |
| # Sidebar + main UI — models load lazily on-demand via @st.cache_resource | |
| # --------------------------------------------------------------------------- | |
| st.sidebar.title("Yugensys Vision AI") | |
| screen = st.sidebar.radio( | |
| "Select POC", | |
| ["Asset Identification", "Quality Inspection (Auto Fallback)"], | |
| ) | |
| # --------------------------------------------------------------------------- | |
| # POC screens | |
| # --------------------------------------------------------------------------- | |
| if screen == "Asset Identification": | |
| st.header("Manufacturing Asset Identification") | |
| conf_pct = st.slider( | |
| "Detection Confidence Threshold", | |
| min_value=0, max_value=100, value=25, step=1, | |
| ) | |
| conf = conf_pct / 100.0 | |
| up = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"], key="poc1") | |
| if up: | |
| img = Image.open(up).convert("RGB") | |
| with st.spinner("Detecting..."): | |
| t0 = time.perf_counter() | |
| annotated, dets = detect_assets(img, conf=conf) | |
| log.info("detect_assets — %.3fs detections=%d", time.perf_counter() - t0, len(dets)) | |
| c1, c2 = st.columns(2) | |
| c1.image(img, caption="Input", use_column_width=True) | |
| c2.image(annotated, caption="Detections", use_column_width=True) | |
| if dets: | |
| st.success(f"**{dets[0]['name']}** — Confidence: {dets[0]['confidence']}%") | |
| st.table(dets) | |
| else: | |
| st.warning("No assets detected. Try another image.") | |
| elif screen == "Quality Inspection (Auto Fallback)": | |
| st.header("Visual Quality Inspection") | |
| threshold_pct = st.slider( | |
| "EfficientNet Confidence Threshold (Triggers YOLO Fallback)", | |
| min_value=0, max_value=100, value=80, step=1, | |
| ) | |
| threshold = threshold_pct / 100.0 | |
| up = st.file_uploader("Upload a product image", type=["jpg", "jpeg", "png"], key="poc2_auto") | |
| if up: | |
| img = Image.open(up).convert("RGB") | |
| with st.spinner("Inspecting..."): | |
| t0 = time.perf_counter() | |
| res = classify_qc(img, threshold=threshold) | |
| log.info("classify_qc — %.3fs source=%s status=%s conf=%.1f%%", | |
| time.perf_counter() - t0, res.get("source"), res.get("status"), res.get("confidence", 0)) | |
| st.image(img, caption="Input", width=400) | |
| if res.get("warning"): | |
| st.warning(res["warning"]) | |
| if res["status"] == "QC Approved": | |
| st.success(f"QC Status: **{res['status']}** · Defect: **{res['defect']}** · Confidence: {res['confidence']}%") | |
| else: | |
| st.error(f"QC Status: **{res['status']}** · Defect: **{res['defect']}** · Confidence: {res['confidence']}%") | |
| st.info(f"Model Source: {res.get('source', 'Unknown')}") | |