Update app.py

app.py

@@ -1,6 +1,16 @@
 from keras.models import load_model
 from PIL import Image, ImageOps
 import numpy as np
+import gradio as gr
+
+gr.Interface(fn=predict, 
+             ),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             
+             
+def greet(name):
+    return "Hello " + name + "!!"
+def predict: 
 
 # Load the model
 model = load_model('keras_model.h5')
@@ -10,7 +20,9 @@ model = load_model('keras_model.h5')
 # 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>')
+
+inputs=gr.inputs.Image(type="pil"
+# 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)
@@ -26,3 +38,5 @@ data[0] = normalized_image_array
 # run the inference
 prediction = model.predict(data)
 print(prediction)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()