Update app.py

app.py

@@ -5,49 +5,6 @@ import matplotlib.pyplot as plt
 import argparse, h5py, os, re, pkg_resources
 import streamlit as st
 
-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.')
-
-# Define the file upload widget
-pdf_file = st.file_uploader("Upload PDF file in .gr format", type=["gr"])
-
-# Define the form to get the other parameters
-num_structures = st.number_input("Qmin value", min_value=0, max_value=2, value=0.7)
-#structure_index = st.number_input("Index of structure to visualize", min_value=0, value=3)
-#sigma = st.number_input("Standard deviation for sampling", min_value=0.1, value=3.0)
-
-parser = argparse.ArgumentParser(prog='POMFinder', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-requiredNamed = parser.add_argument_group('required named arguments')
-
-requiredNamed.add_argument("-d", "--data", default=None, type=str,
-                    help="a directory of PDFs or a file.", required=True)
-
-requiredNamed.add_argument("-n", "--nyquist", default="No", type=str,
-                    help="is the data nyquist sampled", required=True)
-
-parser.add_argument("-i", "--Qmin", default=0.7, type=float,
-                    help="Qmin value of the experimental PDF")
-
-parser.add_argument("-a", "--Qmax", default=30, type=float,
-                    help="Qmax value of the experimental PDF")
-
-parser.add_argument("-f", "--file_name", default='', type=str,
-                    help="Name of the output file")
-
-parser.add_argument("-m", "--Qdamp", default=0.04, type=float,
-                    help="Qdamp value of the experimental PDF")
-
-def main(args=None):
-    args = parser.parse_args(args=args)
-    y, y_onehotenc_cat, y_onehotenc_values, POMFinder = get_POMFinder()
-    r, Gr = PDF_Preparation(args.data, args.Qmin, args.Qmax, args.Qdamp, rmax=10, nyquist=args.nyquist)
-    res, y_pred_proba = POMPredicter(POMFinder, Gr, y_onehotenc_cat);
-
-
-
 def get_POMFinder():
     # Get file paths
     load_files = pkg_resources.resource_listdir(__name__, 'Backend/')
@@ -129,3 +86,69 @@ def POMPredicter(POMFinder, Gr, y_onehotenc_cat):
     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.')
+
+# Define the file upload widget
+pdf_file = st.file_uploader("Upload PDF file in .gr format", type=["gr"])
+
+# Define the form to get the other parameters
+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)
+
+parser = argparse.ArgumentParser(prog='POMFinder', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+args = parser.parse_args()
+args.data = "uploaded_file.gr"
+args.nyquist = "True"
+args.Qmin = Qmin
+args.Qmax = Qmax
+args.Qdamp = Qdamp
+args.file_name = "POMFinder_results.txt"
+
+if pdf_file is None:
+        st.warning("Please upload a PDF file.")
+    else:
+        # Get the contents of the file as bytes
+        file_bytes = pdf_file.read()
+    
+        # Save the contents of the file to disk
+        with open("uploaded_file.gr", "wb") as f:
+            f.write(file_bytes)
+
+    #Predict with POMFinder
+    y, y_onehotenc_cat, y_onehotenc_values, POMFinder = get_POMFinder()
+    r, Gr = PDF_Preparation(args.data, args.Qmin, args.Qmax, args.Qdamp, rmax=10, nyquist=args.nyquist)
+    res, y_pred_proba = POMPredicter(POMFinder, Gr, y_onehotenc_cat);
+
+
+
+
+
+
+
+
+
+st.subheader('Cite')
+
+st.write('If you use DeepStruc, our code or results, please consider citing our papers. 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.subheader('LICENSE')
+
+st.write('This project is licensed under the Apache License Version 2.0, January 2004 - see the LICENSE file at https://github.com/EmilSkaaning/DeepStruc/blob/main/LICENSE.md for details.')
+st.write("")
+
+st.subheader('Github')
+st.write('https://github.com/EmilSkaaning/DeepStruc')
+
+st.subheader('Questions')
+st.write('andy@chem.ku.dk or etsk@chem.ku.dk')
+
+
+
+