Create app.py file

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.applications import imagenet_utils
+from tensorflow.keras.utils import img_to_array
+from tensorflow.keras.models import load_model
+import numpy as np
+import cv2
+import pickle
+
+
+
+
+
+def Prediction_VGG16(image):
+    
+    #Prepare image
+    IMG_SIZE = 224
+    image = img_to_array(image)
+    image = image**1.0/255.0
+    img_prepared = image.reshape((-1,IMG_SIZE,IMG_SIZE,3))
+    print(img_prepared)
+
+    #Load model vgg6 package
+    path = ".\model\model_vgg16.h5"
+    my_model =load_model(path)
+
+
+
+    #Prediction
+    classes = ["Brain Tumor","Healthy"]
+    prediction = my_model.predict(img_prepared)[0]
+    prediction = prediction.tolist()
+
+
+    return {k:v for k,v in zip(classes,prediction)}
+
+
+
+
+demo = gr.Interface(Prediction_VGG16, gr.inputs.Image(shape=(224,224)),gr.Label(num_top_classes=2))
+
+demo.launch()