Update app.py

app.py

@@ -4,30 +4,37 @@ import numpy as np
 import gradio as gr
 
 
+# Load the model
+model = load_model('keras_model.h5')
+
+
+
 def greet(name):
     return "Hello " + name + "!!"
 
-def predict(img): 
-    # Load the model
-    model = load_model('keras_model.h5')
-    # 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)
-    # Replace this with the path to your image
-    image = img
-    # image = Image.open('<IMAGE_PATH>')
-    #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)
-    #turn the image into a numpy array
-    
-    
-    data[0] = np.asarray(image)
-    # run the inference
-    prediction = model.predict(data)
-    gr.outputs.Label = open(labels.txt)
+def predict(img):  
+
+# 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)
+# Replace this with the path to your image
+image = Image.open('<IMAGE_PATH>')
+#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
+prediction = model.predict(data)
+print(prediction)
     return prediction