Update app.py

app.py

@@ -103,17 +103,26 @@ def sepia(input_img):
         class_prob = np.mean(logits.numpy()[0][mask])
         class_probabilities[class_name] = class_prob
 
-    # 모든 물체의 확률 출력
-    print("Predicted class probabilities:")
+    # Gradio Interface에 출력할 문자열 생성
+    output_text = "Predicted class probabilities:\n"
     for class_name, prob in class_probabilities.items():
-        print(f"{class_name}: {prob:.4f}")
+        output_text += f"{class_name}: {prob:.4f}\n"
 
     # 정확성이 가장 높은 물체 정보 출력
     max_prob_class = max(class_probabilities, key=class_probabilities.get)
     max_prob_value = class_probabilities[max_prob_class]
-    print(f"\nPredicted class with highest probability: {max_prob_class}, Probability: {max_prob_value:.4f}")
+    output_text += f"\nPredicted class with highest probability: {max_prob_class}, Probability: {max_prob_value:.4f}"
+
+    return fig, output_text
+
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(400, 600)),
+                    outputs=['plot', 'text'],
+                    examples=["citiscapes-1.jpeg", "citiscapes-2.jpeg"],
+                    allow_flagging='never')
+
+demo.launch()
 
-    return fig, f"Predicted class with highest probability: {max_prob_class}, Probability: {max_prob_value:.4f}"
 
 
 demo = gr.Interface(fn=sepia,