Upload 3 files

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+
+# 모델 로드
+model = YOLO("best.pt")
+
+def predict(image):
+    # YOLO 추론
+    results = model(image)
+    
+    # 상의/하의 좌표 및 라벨 추출 (기존 프로젝트 JSON 형식에 맞춰 가공)
+    detections = []
+    for r in results:
+        for box in r.boxes:
+            x1, y1, x2, y2 = box.xyxy[0].tolist()
+            label = model.names[int(box.cls[0])]
+            conf = float(box.conf[0])
+            detections.append({
+                "label": label,
+                "confidence": conf,
+                "box": [int(x1), int(y1), int(x2), int(y2)]
+            })
+    return detections
+
+# Gradio 인터페이스 설정 (API 전용이므로 단순하게 구성)
+demo = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="numpy"), 
+    outputs="json"
+)
+
+demo.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef40b5c571b1e43220aa45e4a85a94bf2de27a5724e37467f0aaca725a7677a1
+size 19164186

requirements.txt

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+ultralytics==8.3.30
+opencv-python-headless==4.9.0.80
+gradio
+numpy==1.26.4
+Pillow==10.4.0