Create app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+
+path = "./"
+model = AutoModelForImageClassification.from_pretrained(path)
+processor = AutoImageProcessor.from_pretrained(path)
+
+ 알려줌)
+def classify_image(image):
+    
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # 모델 예측
+    with torch.no_grad():
+        outputs = model(**inputs)
+        
+    # 확률(Softmax) 계산
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
+    
+    
+    labels = model.config.id2label
+    result = {labels[i]: float(probs[i]) for i in range(len(labels))}
+    
+    return result
+
+
+iface = gr.Interface(
+    fn=classify_image, 
+    inputs=gr.Image(type="pil"), 
+    outputs=gr.Label(num_top_classes=3),
+    title="재활용품분류기 🤖",
+    description=" precision    recall  f1-score   support
+
+       0_Pet       0.85      0.88      0.86       218
+       1_Can       0.95      0.89      0.92       283
+     2_Glass       0.93      0.86      0.89       221
+     3_Paper       0.90      0.98      0.94       315
+   4_Plastic       0.91      0.93      0.92       308
+     5_Vinyl       0.95      0.94      0.94       282
+
+    accuracy                           0.92      1627
+   macro avg       0.91      0.91      0.91      1627
+weighted avg       0.92      0.92      0.91      1627
+"
+)
+
+# 실행
+iface.launch()