README.md

metadata

title: dAIso - Semiconductor Defect Detection
emoji: 🔬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.36.1
python_version: '3.10'
app_file: app.py
pinned: false
license: mit

dAIso - Semiconductor Defect Detection Demo

Agent V10: YOLO + Two-Stage GPT-4o Pipeline

This is an interactive demo showcasing the semiconductor defect detection system developed for the Hyundai NGV AI Agent Hackathon 2026.

Features

1. Dashboard

• Overall analysis statistics
• Classification distribution (Normal vs Abnormal)
• Defect type breakdown
• Performance metrics

2. Inspection Results

• Individual image analysis details
• YOLO detection results (holes, leads, body)
• Stage 1: GPT-4o visual observations
• Stage 2: Decision making and defect scoring
• Recheck information

3. Visualizations

• Classification pie charts
• Defect type bar charts
• Confidence score distributions
• Processing time analysis
• YOLO detection performance

4. AI Assistant (Chatbot)

Ask questions about the analysis results in Korean or English:

• "전체 분석 요약해줘"
• "어떤 결함이 가장 많아?"
• "DEV_005 결과 알려줘"
• "YOLO 탐지 성능은?"

Pipeline Architecture

Image Input
    │
    ▼
┌─────────────────────┐
│  Stage 0: YOLO      │  → Component Detection
│  (Roboflow API)     │  → Bounding boxes
└─────────────────────┘
    │
    ▼
┌─────────────────────┐
│  Stage 1: GPT-4o    │  → Visual Observation
│  (Observation)      │  → Body/Lead analysis
└─────────────────────┘
    │
    ▼
┌─────────────────────┐
│  Stage 2: GPT-4o    │  → Defect scoring
│  (Decision)         │  → Classification
└─────────────────────┘
    │
    ▼
┌─────────────────────┐
│  Conditional        │  → Confidence check
│  Recheck            │  → Weighted voting
└─────────────────────┘

Tech Stack

• LLM: GPT-4o (via Luxia Cloud Bridge API)
• Object Detection: Roboflow Workflow API
• UI: Gradio
• Visualization: Matplotlib

Team

• Dong-Hyeon Lim
• Munkyeong Suh

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Hyundai NGV AI Agent Hackathon 2026