Update app.py

app.py

@@ -19,81 +19,28 @@ def detect(image):
     left = int(results['xmin'])
     width = int(results['xmax'] - results['xmin'])
     height = int(results['ymax'] - results['ymin'])
-    return top, left, width, height
-  except:
-    return 0,0,0,0
-
-def format_label(label):
-  """
-  From '0 class 1\n' to 'class 1'
-  """
-  return label[:-1]
-
-
-def predict(image):
-  top, left, width, height = detect(image)
-
-  if (top == 0) and (left == 0) and (width == 0) and (height==0):
     return {
-      "predictions": {}, 
       'bbox': {
-          "top": 0,
-          "left": 0,
-          "width": 0,
-          "height": 0
+        "top": top,
+        "left": left,
+        "width": width,
+        "height": height
       }
     }
-
-  if width > height:
-    height = width
-  else:
-    width = height
-  
-  # Crop the turtle
-  image = image.crop((left, top, left + width, top + height))
-
-  # Create the array of the right shape to feed into the keras model
-  # The 'length' or number of images you can put into the array is
-  # determined by the first position in the shape tuple, in this case 1.
-  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-  
-  #resize the image to a 224x224 with the same strategy as in TM2:
-  #resizing the image to be at least 224x224 and then cropping from the center
-  size = (224, 224)
-  image = ImageOps.fit(image, size, Image.ANTIALIAS)
-
-  #turn the image into a numpy array
-  image_array = np.asarray(image)
-  # Normalize the image
-  normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-  # Load the image into the array
-  data[0] = normalized_image_array
-
-  # run the inference
-  pred = classify_model.predict(data)
-  pred = pred.tolist()
-
-  with open('labels.txt','r') as f:
-    labels = f.readlines()
-    
-  result = {format_label(labels[i]): round(pred[0][i],2) for i in range(len(pred[0]))}
-  sorted_result = {k: v for k, v in sorted(result.items(), key=lambda item: item[1], reverse=True) if v > 0}
-
-  
-  return json.dumps({
-      "predictions": sorted_result, 
+  except:
+    return {
       'bbox': {
-          "top": top,
-          "left": left,
-          "width": width,
-          "height": height
+        "top": 0,
+        "left": 0,
+        "width": 0,
+        "height": 0
       }
-    })
+    }
     
 title = "🐆"
 
 gr.Interface(
-    fn=predict, 
+    fn=detect, 
     inputs=gr.Image(type="pil", label="Input Image"),
     outputs=[gr.JSON()],
   #  live=True,