Update app.py

app.py

@@ -8,15 +8,16 @@ from ultralytics import YOLO
 model = YOLO('best_V4.pt')
 
 def predict(image):
+    
     results = model(image, conf=0.8)
     
     detected = False
     LABEL_MAP = {
     0: "Other",
     1: "Pneumonia"
-}
-
-    # วาด bounding boxes และ labels บนภาพ
+    }
+    
+    labels_found = []
     for result in results:
         boxes = result.boxes.xyxy.cpu().numpy()
         confidences = result.boxes.conf.cpu().numpy()
@@ -27,17 +28,24 @@ def predict(image):
             label = LABEL_MAP.get(int(class_id), "Unknown")
             cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
             label_text = f"{label} {confidence:.2f}"
-            cv2.putText(image, label_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
+            labels_found.append(label_text)
+            
+            cv2.putText(image, label_text, (x1, y1 - 10), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
             detected = True 
 
-    if not detected: 
-        result_rgb = cv2.cvtColor(image_copy, cv2.COLOR_BGR2RGB)
-        return Image.fromarray(result_rgb), "No detected"
+    if not detected:
+        return None, "No detected"
 
-    # แปลงภาพกลับเป็นรูปแบบที่ Gradio สามารถแสดงได้
     pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-    return pil_image
+    message = ", ".join(labels_found)
+    return pil_image, message
 
-demo = gr.Interface(fn=predict, inputs=gr.Image(type="numpy"), outputs=[gr.Image(type="pil", label="Detection Result"), gr.Textbox(label="Message")])
+demo = gr.Interface(fn=predict, 
+                    inputs=gr.Image(type="numpy"), 
+                    outputs=[
+                        gr.Image(type="pil", label="Detection Result"), 
+                        gr.Textbox(label="Message")
+                    ]
+                   )
 demo.launch()