Update app.py

app.py

@@ -52,26 +52,35 @@ if __name__ == '__main__':
 
     st.write('---')
 
-    st.header('Detrending : ')
+    st.subheader('Detrending : ')
     st.write('Detrending means removal of the mean value as well as any linear trend that may be in the measurements. A linear trend could be caused by e.g. drift and it is removed by fitting a first order polynomial to each of the measurement channels and then subtracting it afterwards. By default linear trends should be removed since they can disturb the signal processing and modal analysis.')
 
-    st.header('Savitzky-Golay Filter : ')
+    st.subheader('Savitzky-Golay Filter : ')
     st.write('Savitzky-Golay smoothing filters are typically used to "smooth out" a noisy signal. They are also called digital smoothing polynomial filters or least-squares smoothing filters. Savitzky-Golay filters perform better in some applications than standard averaging FIR filters, which tend to filter high-frequency content along with the noise. Savitzky-Golay filters are more effective at preserving high frequency signal components but less successful at rejecting noise.Savitzky-Golay filters are optimal in the sense that they minimize the least-squares error in fitting a polynomial to frames of noisy data.')
 
-    st.header('Moving Average Filter : ')
+    st.subheader('Moving Average Filter : ')
     st.write('The moving average is the most common filter in Signal Processing , mainly because it is the easiest digital filter to understand and use. The moving average filter is optimal for a common task: reducing random noise while retaining a sharp step response.This makes it the premier filter for time domain encoded signals.')
 
-    st.header('Gaussian Filter : ')
+    st.subheader('Gaussian Filter : ')
     st.write('Gaussian filters are widely used for noise reduction due to their edge preserving properties. Gaussian filters are also used as smoothing filters. The Gaussian filter is a low-pass filter that removes the high-frequency components.')
 
     st.write('---')
 
+    with st.container():
+        # show video
+        st.header('Video Demonstration of gathering a respiratory signal')
+        st.video('./SpeedUP_Supine.mp4')
+
+
+    st.write('---')
+
+    st.header('Signal Processing')
 
     with st.container():
         left,right = st.columns(2)
 
         with left:
-            signal_select = st.selectbox('*Please select a signal*',['Linearly increasing sine wave','Resp Signal sample 1','Resp Signal sample 2','Resp Signal sample 3'])
+            signal_select = st.selectbox('*Please select a signal*',['Linearly increasing sine wave','Resp Signal sample 1','Resp Signal sample 2','Resp Signal sample 3'],index=1)
 
         with right:
             st.subheader('Original Signal')