Update app.py

app.py

@@ -17,28 +17,16 @@ classifier = VisionClassifierInference(
 
 # Define a function to classify and overlay the label on the image
 def classify_image_with_overlay(img):
-    # Predict the label
-    label = classifier.predict(img_path=img)
-
-    # Load the image using OpenCV
-    image = cv2.imread(img)
-
-    # Add a white rectangle for the label
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    org = (10, 30)
-    font_scale = 1
-    color = (255, 255, 255)  # White color
-    thickness = 2
-    text_size = cv2.getTextSize(label, font, font_scale, thickness)[0]
-    cv2.rectangle(image, (org[0] - 10, org[1] - text_size[1] - 10), (org[0] + text_size[0], org[1]), color, cv2.FILLED)
-
-    # Put the label text on the white rectangle
-    cv2.putText(image, label, org, font, font_scale, (0, 0, 0), thickness, cv2.LINE_AA)
-
-    # Convert the image to RGB format for Gradio
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    return image_rgb
+    # Converte a imagem NumPy ndarray para um objeto Pillow Image
+    img_pil = Image.fromarray(img)
+    
+    # Realiza a classificação usando o modelo e adiciona o rótulo previsto à imagem
+    image_with_text = classificar_imagem(img_pil)
+    
+    # Converte a imagem resultante de volta para NumPy ndarray
+    image_with_text_np = np.array(image_with_text)
+    
+    return image_with_text_np
 
 iface = gr.Interface(
     fn=classify_image_with_overlay,