Update app.py

app.py

@@ -60,7 +60,9 @@ def PDF_Preparation(Your_PDF_Name, Qmin, Qmax, Qdamp, rmax, nyquist):
 
     # Create a new figure object
     fig, ax = plt.subplots()
-    
+
+    print (r)
+    print (Gr[0,3:])
     # Plot the transformation to make sure everything is alright
     ax.plot(PDF[:,0], PDF[:,1], label="Original Data")
     ax.plot(r, Gr[0,3:], label="Gr ready for ML")
@@ -99,11 +101,12 @@ pdf_file = st.file_uploader("Upload PDF file in .gr format", type=["gr"])
 Qmin = st.number_input("Qmin value of the experimental PDF", min_value=0.0, max_value=2.0, value=0.7)
 Qmax = st.number_input("Qmax value of the experimental PDF", min_value=15.0, max_value=40.0, value=30.0)
 Qdamp = st.number_input("Qdamp value of the experimental PDF", min_value=0.00, max_value=0.08, value=0.04)
+nyquist = st.checkbox("Is the data nyquist sampled", value=True)
 
 parser = argparse.ArgumentParser(prog='POMFinder', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 args = parser.parse_args()
 args.data = "uploaded_file.gr"
-args.nyquist = "True"
+args.nyquist = nyquist
 args.Qmin = Qmin
 args.Qmax = Qmax
 args.Qdamp = Qdamp
@@ -121,11 +124,8 @@ else:
 
     #Predict with POMFinder
     y, y_onehotenc_cat, y_onehotenc_values, POMFinder = get_POMFinder()
-    st.write("POMfinder loaded")
     r, Gr = PDF_Preparation(args.data, args.Qmin, args.Qmax, args.Qdamp, rmax=10, nyquist=args.nyquist)
-    st.write("Data loaded")
     res, y_pred_proba = POMPredicter(POMFinder, Gr, y_onehotenc_cat);
-    st.write("Predictions is: ", res)
 
 
 st.subheader('Cite')