Update app.py

app.py

@@ -15,7 +15,7 @@ from rdkit import Chem
 from rdkit.Chem import AllChem
 from sklearn.ensemble import RandomForestRegressor
 
-#For KERAS
+
 import random
 import numpy as np
 from keras.wrappers.scikit_learn import KerasRegressor
@@ -30,11 +30,6 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.layers import Dropout
 
-#from keras.layers import Dense
-#from keras.layers import Dropout
-# Function to create model, required for KerasClassifier
-
-
 def create_model(optimizer='RMSprop', learn_rate=0.1, momentum=0.4, activation='sigmoid', dropout_rate=0.0):
     
     keras_model = Sequential()
@@ -51,28 +46,7 @@ def create_model(optimizer='RMSprop', learn_rate=0.1, momentum=0.4, activation='
 
     return keras_model
 
-
-######################
-# Custom function
-######################
-## Calculate molecular descriptors
-
 def get_ecfc(smiles_list, radius=2, nBits=2048, useCounts=True):
-    """
-    Calculates the ECFP fingerprint for given SMILES list
-    
-    :param smiles_list: List of SMILES
-    :type smiles_list: list
-    :param radius: The ECPF fingerprints radius.
-    :type radius: int
-    :param nBits: The number of bits of the fingerprint vector.
-    :type nBits: int
-    :param useCounts: Use count vector or bit vector.
-    :type useCounts: bool
-    :returns: The calculated ECPF fingerprints for the given SMILES
-    :rtype: Dataframe
-    """     
-    
     ecfp_fingerprints=[]
     erroneous_smiles=[]
     for smiles in smiles_list:
@@ -98,8 +72,6 @@ def get_ecfc(smiles_list, radius=2, nBits=2048, useCounts=True):
 
 
 
-
-## generate dataset it is diffrent from origin one  
 import deepchem as dc
 from deepchem.models import GraphConvModel
 
@@ -113,20 +85,13 @@ def generate(SMILES, verbose=False):
     return dataset
 
 
-######################
-# Page Title
-######################
-
-
-
 st.write("""# Accelerated reaction energy prediction for redox batteries  🧪 """)
 st.write('By: [Alishba Imran](https://www.linkedin.com/in/alishba-imran-/)')
 
 
 
 
-#%%
-# About PART
+
 
 about_part = st.expander("Learn More About Project", expanded=False)
 with about_part:
@@ -159,20 +124,12 @@ SMILES = list(filter(None, SMILES))
 
 
 
-# st.header('Input SMILES')
-# SMILES[1:] # Skips the dummy first item
-
 # Use only top 1000
 if len(SMILES)>1000:
     SMILES=SMILES[0:1000]
 	
-## Calculate molecular descriptors
 ecfc_encoder = get_ecfc(SMILES)
 
-#Import pretrained models
-
-#---------------------------------------------------------------------------------
-### generate dataset from SMILES and function generate
 generated_dataset = generate(SMILES)
 
 ### transformer for gcn