Update app.py

app.py

@@ -63,15 +63,14 @@ if len(uploaded_files) == 0:
     st.write("Please upload an image!")
 else:
     for uploaded_file in uploaded_files:
-        img = Image.open(uploaded_file)
-        rescaled = img.convert("HSV").split()[2].resize((28, 28))
+        rescaled = Image.open(uploaded_file).convert("HSV").split()[2].resize((28, 28))
         st.image(rescaled)
-        brightness = jnp.array(rescaled)
-        input = brightness.reshape(1, 28, 28, 1) / 255.
-        st.write(cnn.apply({"params": params}, input).argmax(axis=1)[0])
+        input = jnp.array(rescaled).reshape(1, 28, 28, 1) / 255.
+        st.write("Model Prediction: " + cnn.apply({"params": params}, input).argmax(axis=1)[0])
 
 def gridify(kernel, grid, kernel_size, scaling=5, padding=1):
-    grid = np.pad(np.array(np.pad(np.repeat(np.repeat(kernel, repeats=scaling, axis=0), repeats=scaling, axis=1), ((padding,),(padding,),(0,),(0,)), 'constant', constant_values=(-1,)).reshape((kernel_size[0]*scaling+2*padding, kernel_size[1]*scaling+2*padding, grid[0], grid[1])).transpose(2,0,3,1).reshape(grid[0]*(kernel_size[0]*scaling+2*padding), grid[1]*(kernel_size[1]*scaling+2*padding))+1)*127., (padding,), 'constant', constant_values=(0,))
+    scaled_and_padded = np.pad(np.repeat(np.repeat(kernel, repeats=scaling, axis=0), repeats=scaling, axis=1), ((padding,),(padding,),(0,),(0,)), 'constant', constant_values=(-1,))
+    grid = np.pad(np.array(scaled_and_padded.reshape((kernel_size[0]*scaling+2*padding, kernel_size[1]*scaling+2*padding, grid[0], grid[1])).transpose(2,0,3,1).reshape(grid[0]*(kernel_size[0]*scaling+2*padding), grid[1]*(kernel_size[1]*scaling+2*padding))+1)*127., (padding,), 'constant', constant_values=(0,))
     st.image(Image.fromarray(np.repeat(np.expand_dims(grid, axis=0), repeats=3, axis=0).astype(np.uint8).transpose(1,2,0), mode="RGB"))
 
 with st.expander("See first convolutional layer"):