| from PIL import Image | |
| import cv2 | |
| import numpy as np | |
| import tensorflow as tf | |
| from keras.models import load_model | |
| import gradio as gr | |
| model =load_model('BrainTumor10Epochs.h5') | |
| def getResult(inp): | |
| inp=np.array(inp) | |
| input_img = np.expand_dims(inp, axis=0) | |
| result=np.max(model.predict(input_img)) | |
| if result==0: | |
| return "No Brain Tumor" | |
| elif result==1: | |
| return "Yes Brain Tumor" | |
| examples = [ | |
| ["example_images/No_1.jpg"], | |
| ["example_images/No_2.jpg"], | |
| ["example_images/No_3.jpg"], | |
| ["example_images/Yes_1.jpg"], | |
| ["example_images/Yes_2.jpg"], | |
| ["example_images/Yes_3.jpg"] | |
| ] | |
| iface = gr.Interface( | |
| fn=getResult, | |
| inputs=gr.Image(shape=(64, 64)), | |
| outputs=gr.Label(num_top_classes=2), | |
| title="Brain Tumor Classification", | |
| description="Upload the MRI Image of the Brain and it will tell whether it has a Brain Tumor or not", | |
| examples=examples | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |