Update app.py

app.py

@@ -9,7 +9,7 @@ import numpy as np
 
 # labels= {'Burger King': 0, 'KFC': 1,'McDonalds': 2,'Other': 3,'Starbucks': 4,'Subway': 5}
 HEIGHT,WIDTH=224,224
-
+IMG_SIZE=224
 model=load_model('Models/best_model1.h5')
 
 # def classify_image(inp):
@@ -25,20 +25,31 @@ model=load_model('Models/best_model1.h5')
 #           result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
 #   return result 
 
-def classify_image(inp):
-    np.random.seed(143)
-    labels = {'Cat': 0, 'Dog': 1}
-    NUM_CLASSES = 2
-    #inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
-    #inp = tf.keras.applications.nasnet.preprocess_input(inp)
-    prediction = model.predict(inp)
-    predicted_class_indices = np.argmax(prediction, axis=1)
+# def classify_image(inp):
+#     np.random.seed(143)
+#     labels = {'Cat': 0, 'Dog': 1}
+#     NUM_CLASSES = 2
+#     #inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
+#     #inp = tf.keras.applications.nasnet.preprocess_input(inp)
+#     prediction = model.predict(inp)
+#     predicted_class_indices = np.argmax(prediction, axis=1)
+
+#     label_order = ["Cat","Dog"]
 
-    label_order = ["Cat","Dog"]
+#     result = {label: float(f"{prediction[0][labels[label]]:.6f}") for label in label_order}
+
+#     return result
+
+def classify_image(inp):
+  inp = inp.reshape((-1, IMG_SIZE, IMG_SIZE, 3))
+  inp = tf.keras.applications.vgg16.preprocess_input(inp)
+  prediction = model.predict(inp).flatten()
+  return {labels[i]: float(prediction[i]) for i in range(NUM_CLASSES)}
 
-    result = {label: float(f"{prediction[0][labels[label]]:.6f}") for label in label_order}
+# image = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
+# label = gr.outputs.Label(num_top_classes=2)
 
-    return result
+# gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Cats Vs Dogs',height=600, width=1200,examples=ex,theme='peach').launch(debug=True)
 
     
 image = gr.Image(height=HEIGHT,width=WIDTH,label='Input')