Update app.py

app.py

@@ -2,7 +2,7 @@ from sklearn.preprocessing import OrdinalEncoder
 import xgboost as xgb
 import numpy as np
 import matplotlib.pyplot as plt
-import argparse, h5py, os, re, pkg_resources
+import argparse, h5py, os, re
 import streamlit as st
 
 def get_POMFinder():
@@ -79,19 +79,17 @@ def POMPredicter(POMFinder, Gr, y_onehotenc_cat):
     res = sorted(range(len(y_pred_proba)), key = lambda sub: y_pred_proba[sub]);
     res.reverse();
     st.markdown(f'<span style="font-size: 24px; color: green;">The 1st guess from the model is: <b>{str(y_onehotenc_cat.categories_[0][res[0]])[2:-2]+"cale.xyz"}</b></span> <hr/>',unsafe_allow_html=True,)
-    #st.write("The 1st guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[0]])[2:-2]+"cale.xyz")
-    st.write("The 2nd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[1]])[2:-2]+"cale.xyz")
-    st.write("The 3rd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[2]])[2:-2]+"cale.xyz")
-    st.write("The 4th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[3]])[2:-2]+"cale.xyz")
-    st.write("The 5th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[4]])[2:-2]+"cale.xyz")
+    st.markdown(f'<span style="font-size: 24px; color: green;">The 2nd guess from the model is: <b>{str(y_onehotenc_cat.categories_[0][res[1]])[2:-2]+"cale.xyz"}</b></span> <hr/>',unsafe_allow_html=True,)
+    st.markdown(f'<span style="font-size: 24px; color: green;">The 3rd guess from the model is: <b>{str(y_onehotenc_cat.categories_[0][res[2]])[2:-2]+"cale.xyz"}</b></span> <hr/>',unsafe_allow_html=True,)
+    st.markdown(f'<span style="font-size: 24px; color: green;">The 4th guess from the model is: <b>{str(y_onehotenc_cat.categories_[0][res[3]])[2:-2]+"cale.xyz"}</b></span> <hr/>',unsafe_allow_html=True,)
+    st.markdown(f'<span style="font-size: 24px; color: green;">The 5th guess from the model is: <b>{str(y_onehotenc_cat.categories_[0][res[4]])[2:-2]+"cale.xyz"}</b></span> <hr/>',unsafe_allow_html=True,)
     
     return res, y_pred_proba
 
 
 st.title('POMFinder')
-
-st.write('Welcome to DeepStruc that is a Deep Generative Model which has been trained to solve a mono-metallic structure (<200 atoms) based on a PDF!')
-st.write('Upload a PDF to use DeepStruc to predict the structure.')
+st.write('Welcome to POMFinder which is a tree-based supervised learning algorithm that can predict the polyoxometalate cluster from a Pair Distribution Function.
+st.write('Upload a PDF to use POMFinder to predict the structure.')
 
 # Define the file upload widget
 pdf_file = st.file_uploader("Upload PDF file in .gr format", type=["gr"])
@@ -100,7 +98,7 @@ 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)
+nyquist = st.checkbox("Is the data nyquist sampled", value=False)
 
 parser = argparse.ArgumentParser(prog='POMFinder', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 args = parser.parse_args()
@@ -129,10 +127,9 @@ else:
 
 st.subheader('Cite')
 
-st.write('If you use DeepStruc, our code or results, please consider citing our papers. Thanks in advance!')
+st.write('If you use POMFinder, our code or results, please consider citing our paper. Thanks in advance!')
 
-st.write('DeepStruc: Towards structure solution from pair distribution function data using deep generative models **2023** (https://pubs.rsc.org/en/content/articlehtml/2022/dd/d2dd00086e)')
-st.write('Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models **2020** (https://par.nsf.gov/biblio/10300745)')
+st.write('Title **2023** (Link)')
 
 st.subheader('LICENSE')