Upload app (2).py

app (2).py

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+
+import gradio as gr
+import tensorflow as tf 
+import tensorflow.keras 
+import matplotlib.pyplot as plt
+import cv2
+import tensorflow_io as tfio
+import numpy as np
+
+loaded_model = tf.keras.models.load_model( 'brain1.h5')
+
+def take_img(img):
+  
+  resize = tf.image.resize(img, (128,128))
+  gray = tfio.experimental.color.bgr_to_rgb(resize)
+  yhat = loaded_model.predict(np.expand_dims(gray/255, 0))
+  label_names = {
+  "1": "Tumor",
+  "2": "Normal"}
+  classes_x=np.argmax(yhat,axis=1)
+  a = classes_x[0]
+  input_value = a + 1
+  input_str = str(input_value)
+  predicted_label = label_names[input_str]
+  tumor = yhat[0][0]
+  tumor = str(tumor)
+  normal = yhat[0][1]
+  normal = str(normal)
+  return {'Tumour': tumor, 'Normal':normal}
+
+
+
+image = gr.inputs.Image(shape=(128,128))
+
+label = gr.outputs.Label('ok')
+gr.Interface(fn=take_img, inputs=image, outputs="label",interpretation='default').launch(debug='True')