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Runtime error
| 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',examples=["Y10.jpg","Y109.jpeg","20 no.jpg"]).launch() | |
| if __name__ == "__main__": | |
| final_func() | |