Update app.py

app.py

@@ -26,13 +26,6 @@ detection_model = YOLO(best_yolo_model, task='detect')
 
 
 # Define the image preprocessing function
-def preprocess_imageXX(image):
-    image = image.resize((224, 224))  # Resize to the input size of ResNet50
-    image = np.array(image)  # Convert to numpy array
-    image = preprocess_input(image)  # Preprocess for ResNet50
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    return image
-    
 def preprocess_image(pilimg):
     img = pilimg.resize((224, 224))  # Resize to the input size of ResNet50    
     img_array = image.img_to_array(img)
@@ -69,7 +62,7 @@ def process_image(pilimg):
         #font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
 
         draw.text((250, 10), "Good", fill="green", font=font)
-        #gr.Info("No defect detected,GOOD!", duration=3)
+        #gr.Info("No defect detected, GOOD!", duration=3)
         summary_str = "No defect is detected, the cap is GOOD!"
 
     else:  # Defective
@@ -82,7 +75,7 @@ def process_image(pilimg):
         font = ImageFont.truetype(font_path, 30)
         #font = ImageFont.truetype("arialbd.ttf", 30)  # Use arial.ttf for bold font
 
-        draw.text((330, 10), "Defective", fill="red", font=font)
+        draw.text((300, 10), "Defective", fill="red", font=font)
         
         detections = detection_result[0].boxes.data  # Get detections
         if len(detections) > 0: