Update app.py

app.py

@@ -1,21 +1,33 @@
-import os
-import numpy as np
 import gradio as gr
-
 import tensorflow as tf
 from tensorflow.keras.models import load_model
+import numpy as np
+
+class_names = ["bird", "cat", "deer", "dog"]
 
 #CloudDeploymentTest/
-model = load_model('model.keras')
-labels = ["bird", "cat", "deer", "dog"]
+model = load_model("model.keras")
+
+def classify(input_img):
+    # We need to "normalize" the input.
+    # Input pixels are between 0 and 255,
+    # but neural net expects values 0 to 1.
+    input_img = np.array(input_img) / 255
+
+    # Add a batch dimension of size 1.
+    input_img = np.array([input_img])
+
+    # Run our image through the model.
+    prediction = model.predict(input_img)
+
+    # Remove batch dimension from output.
+    prediction = prediction[0]
 
-def pred(input_img):
-    prediction = model.predict(np.array([input_img])/255) #/255 to normalize (rgb)
+    # Turn softmax output into index.
     prediction = np.argmax(prediction)
-    prediction = labels[prediction]
-    return prediction
 
-demo = gr.Interface(pred, gr.Image(), "text")
+    # Turn index into class name
+    return class_names[prediction]
 
-if __name__ == "__main__":
-    demo.launch()
+demo = gr.Interface(classify, gr.Image(), "text")
+demo.launch()