app.py

import cv2
import numpy as np
import time
import os  


from PIL import Image


import gradio as gr

ch_detection_model1 = cv2.dnn.readNet('tumor_classifier_mixed_datasets.onnx')

def main_func(im):
	im=cv2.resize(im,(224,224))
	im=cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
	im = (im.astype(np.float32)) / 255.0
	im=im[np.newaxis, ...]
	#print(im.shape)
	ch_detection_model1.setInput(im)

	outputs=ch_detection_model1.forward(ch_detection_model1.getUnconnectedOutLayersNames())
	outputs=np.array(outputs)
	outputs=outputs.reshape(-1)
	if outputs[0]>0.49:
		results=("predicted as Tumor with probability :"+str(outputs[0]))
		return results
	if outputs[0]<0.50:
		results=("There is No-Tumor with probability :"+str(1-outputs[0]))
		return results



def final_func():
	gr.Interface(fn=main_func, 
             inputs=gr.Image(),
             outputs='text').launch()
	
if __name__ == "__main__":
    final_func()