Update app.py

app.py

@@ -1,78 +1,65 @@
+import gradio as gr  # ← 추가!
 from transformers import pipeline
 from PIL import Image, ImageDraw
-import requests
-import matplotlib.pyplot as plt
 
 detector = pipeline(
     "object-detection",
     model="facebook/detr-resnet-50"
 )
 
-# 객체별 색상 지정
 colors = [
     "#FF0000", "#00FF00", "#0000FF", "#FF00FF",
     "#00FFFF", "#FFA500", "#800080", "#008000"
 ]
 
 def find_obj(image):
-  results = detector(image)
+    results = detector(image)
 
-  # 이미지에 박스 그리기
-  draw_image = image.copy()
-  draw = ImageDraw.Draw(draw_image)
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
 
+    for i, item in enumerate(results):
+        label = item["label"]
+        score = round(item["score"] * 100, 1)
+        box = item["box"]
+        color = colors[i % len(colors)]
 
+        draw.rectangle(
+            [box["xmin"], box["ymin"], box["xmax"], box["ymax"]],
+            outline=color,
+            width=3
+        )
 
-  for i, item in enumerate(results):
-      label = item["label"]
-      score = round(item["score"] * 100, 1)
-      box = item["box"]
-      color = colors[i % len(colors)]
+        text = f"{label} {score}%"
+        draw.rectangle(
+            [box["xmin"], box["ymin"] - 20, box["xmin"] + len(text) * 7, box["ymin"]],
+            fill=color
+        )
 
-      # 네모 박스 그리기
-      draw.rectangle(
-          [box["xmin"], box["ymin"], box["xmax"], box["ymax"]],
-          outline=color,
-          width=3
-      )
+        draw.text(
+            (box["xmin"] + 2, box["ymin"] - 18),
+            text,
+            fill="white"
+        )
 
-      # 라벨 텍스트 배경 박스
-      text = f"{label} {score}%"
-      draw.rectangle(
-          [box["xmin"], box["ymin"] - 20, box["xmin"] + len(text) * 7, box["ymin"]],
-          fill=color
-      )
-
-      # 라벨 텍스트
-      draw.text(
-          (box["xmin"] + 2, box["ymin"] - 18),
-          text,
-          fill="white"
-      )
-
-  summary = f"총 {len(results)}개 객체 탐지됨\n\n"
-  for item in results:
+    summary = f"총 {len(results)}개 객체 탐지됨\n\n"
+    for item in results:
         summary += f"· {item['label']} ({round(item['score']*100, 1)}%)\n"
-  return draw_image, summary       # ← PIL.Image 그대로 반환하면 Gradio가 알아서 표시
-
 
+    return draw_image, summary
 
 demo = gr.Interface(
     fn=find_obj,
     inputs=gr.Image(
-        type="pil",                  # ← 핵심: PIL로 받기
+        type="pil",
         label="이미지 업로드"
     ),
     outputs=[
-        gr.Image(label="탐지 결과"), # ← PIL.Image 반환하면 자동으로 표시
+        gr.Image(label="탐지 결과"),
         gr.Textbox(label="탐지 목록", lines=10)
     ],
     title="🔍 객체 탐지기",
     description="이미지를 업로드하면 객체를 자동으로 탐지합니다.",
-    examples=[
-        ["http://images.cocodataset.org/val2017/000000039769.jpg"]
-    ]
 )
 
-# demo.launch()
-demo.launch(share=True)
+demo.launch()  # ← share=True 제거