Upload app.py

app.py

@@ -0,0 +1,42 @@
+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()
+
+