| import sys, os |
| sys.path.append(os.path.dirname(__file__)) |
|
|
| import gradio as gr |
| from PIL import Image |
| import pandas as pd |
|
|
| from inference.detect import detect_assets |
| from inference.classify import classify_qc |
|
|
| def run_detect(img, conf): |
| if img is None: |
| return None, "⚠️ Please upload an image.", None |
| |
| annotated, dets = detect_assets(img, conf=conf/100.0) |
| |
| if dets: |
| top = dets[0] |
| status = f"✅ **{top['name']}** — Confidence: {top['confidence']}%" |
| df = pd.DataFrame(dets) |
| else: |
| status = "⚠️ No assets detected. Try another image." |
| df = pd.DataFrame(columns=["name", "confidence", "box"]) |
| |
| return annotated, status, df |
|
|
| def run_qc(img, product_type, threshold_pct): |
| if img is None: |
| return "", "⚠️ Please upload an image.", "" |
| |
| res = classify_qc(img, product_type=product_type, threshold=threshold_pct/100.0) |
| |
| warning_text = "" |
| if res.get("warning"): |
| warning_text = f"### ⚠️ {res['warning']}" |
| |
| status = res["status"] |
| defect = res.get("defect", "") |
| confidence = res["confidence"] |
| source = res.get("source", "Unknown") |
| |
| if status == "QC Approved": |
| result_text = f"### ✅ QC Status: **{status}** · Defect: **None** · Confidence: {confidence}%" |
| else: |
| result_text = f"### ❌ QC Status: **{status}** · Defect: **{defect}** · Confidence: {confidence}%" |
| |
| source_text = f"**ℹ️ Model Source:** {source}" |
| |
| return warning_text, result_text, source_text |
|
|
|
|
| with gr.Blocks(title="Yugensys Vision AI — Manufacturing") as demo: |
| gr.Markdown("# Yugensys Vision AI") |
| |
| with gr.Tabs(): |
| |
| with gr.TabItem("Asset Identification"): |
| gr.Markdown("## Manufacturing Asset Identification") |
| with gr.Row(): |
| with gr.Column(scale=1): |
| img_input1 = gr.Image(type="pil", label="Upload an image") |
| conf_slider = gr.Slider(minimum=0, maximum=100, value=25, step=1, label="Detection Confidence Threshold") |
| btn1 = gr.Button("Detect Assets", variant="primary") |
| with gr.Column(scale=1): |
| img_output1 = gr.Image(label="Detections") |
| status_output1 = gr.Markdown() |
| table_output = gr.Dataframe(headers=["name", "confidence", "box"]) |
| |
| btn1.click(fn=run_detect, inputs=[img_input1, conf_slider], outputs=[img_output1, status_output1, table_output]) |
| |
| |
| with gr.TabItem("Quality Inspection (Auto Fallback)"): |
| gr.Markdown("## Visual Quality Inspection") |
| with gr.Row(): |
| with gr.Column(scale=1): |
| img_input2 = gr.Image(type="pil", label="Upload a product image") |
| product_dropdown = gr.Dropdown(choices=["fmcg_inspection", "steel_inspection"], value="fmcg_inspection", label="Product Type") |
| thresh_slider = gr.Slider(minimum=0, maximum=100, value=80, step=1, label="EfficientNet Confidence Threshold (Triggers YOLO Fallback)") |
| btn2 = gr.Button("Run QC Inspection", variant="primary") |
| with gr.Column(scale=1): |
| warning_output = gr.Markdown() |
| result_output = gr.Markdown() |
| source_output = gr.Markdown() |
| |
| btn2.click(fn=run_qc, inputs=[img_input2, product_dropdown, thresh_slider], outputs=[warning_output, result_output, source_output]) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
