Update main.py

main.py

@@ -27,13 +27,22 @@ model1.add(keras.layers.Conv2D(3, (9, 9), activation='tanh', padding='same'))
 #Loading the weights in the architecture (The file should be stored in the same directory as the code)
 model1.load_weights('modelV13_500trained_1.h5')
 
+#simple image scaling to (nR x nC) size
+def scale(im, nR, nC):
+  nR0 = len(im)     # source number of rows 
+  nC0 = len(im[0])  # source number of columns 
+  return numpy.array([[ im[int(nR0 * r / nR)][int(nC0 * c / nC)]  
+             for c in range(nC)] for r in range(nR)])
+
 def predict(mask):
-    X = numpy.round((mask/255.0))[numpy.newaxis, :, :, numpy.newaxis]
+    print(mask)
+    X = scale(mask, 101, 636)
+    X = numpy.round(X/255.0)[numpy.newaxis, :, :, numpy.newaxis]    
     v = model1.predict(X)*255
     output = (v - v.min()) / (v.max() - v.min())
     print(output.shape)
     return output[0, :, :, 0], output[0, :, :, 1], output[0, :, :, 2]
-    
+
 with gradio.Blocks() as demo:
 
     with gradio.Accordion("✨ Read about the ML model here! ✨", open=False):
@@ -56,5 +65,5 @@ with gradio.Blocks() as demo:
 
     btn.click(fn=predict, inputs=[mask], outputs=[exx, eyy, exy])
 
-demo.launch(debug=True)
+demo.launch()