Create app.py

app.py

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+import pandas as pd
+import joblib
+from sklearn.ensemble import RandomForestRegressor
+import gzip
+from rdkit.Chem import MolFromSmiles, rdMolDescriptors
+from rdkit.Chem.Descriptors import CalcMolDescriptors
+import lightgbm as lgb
+from sklearn.ensemble import ExtraTreesRegressor
+import streamlit as st
+
+class Molecule:
+	def __init__(self, smiles: str):
+		if not smiles :
+			print("Empty smiles are given")
+			sys.exit()
+		self.smiles = smiles
+		self.mol = MolFromSmiles(smiles)
+	def descriptor_generator(self):
+		return CalcMolDescriptors(self.mol)
+
+SMI = st.text_input('Input SMILE', 'O=Cc1ccc(Cl)cc1')
+st.write('The input SMILE is', str(SMI))
+mol = MolFromSmiles(SMI)
+
+formula = rdMolDescriptors.CalcMolFormula(MolFromSmiles(SMI))
+descriptors = CalcMolDescriptors(mol)
+descriptors_dataframe = pd.DataFrame([list(descriptors.values())], columns= list(descriptors.keys()))
+st.markdown(''':rainbow[ABSORPTION]''')
+
+st.markdown(''':rainbow[Lipophilicity]''')
+
+st.markdown(''':orange[LGBMRegressor]''')
+
+
+with gzip.GzipFile('model/Absorption_Lipophilicity_Prediction_lgbm_model.joblib.gz', 'rb') as fa:  
+   Absorption_Lipophilicity_Prediction_lgbm_model = joblib.load(fa)
+
+st.write("Absorption Lipophilicity Result for LGBM Regressor : ", round(Absorption_Lipophilicity_Prediction_lgbm_model.predict(descriptors_dataframe)[0],4))
+
+
+with gzip.GzipFile('model/Absorption_Lipophilicity_Prediction_etr_model.joblib.gz', 'rb') as fe:  
+   Absorption_Lipophilicity_Prediction_etr_model = joblib.load(fe)
+
+st.markdown(''':orange[ExtraTreesRegressor]''')
+st.write("Absorption_Lipophilicity Result for ExtraTreesRegressor : ", round(Absorption_Lipophilicity_Prediction_etr_model.predict(descriptors_dataframe)[0],4))
+
+
+with gzip.GzipFile('model/Absorption_Lipophilicity_Prediction_rf_model.joblib.gz', 'rb') as fi:  
+   Absorption_Lipophilicity_Prediction_rf_model = joblib.load(fi)
+
+
+st.markdown(''':orange[RandomForestRegressor]''')
+st.write("Absorption_Lipophilicity Result for RandomForestRegressor : ", round(Absorption_Lipophilicity_Prediction_rf_model.predict(descriptors_dataframe)[0],4))
+
+
+with gzip.GzipFile('model/Absorption_Lipophilicity_Prediction_rf_model_optimised.joblib.gz', 'rb') as fo:  
+   Absorption_Lipophilicity_Prediction_rf_model_optimised = joblib.load(fo)
+
+st.markdown(''':orange[RandomForestRegressor Optimised]''')
+st.write("Absorption_Lipophilicity Result for Optimised RandomForestRegressor : ", round(Absorption_Lipophilicity_Prediction_rf_model_optimised.predict(descriptors_dataframe)[0],4))
+
+with gzip.GzipFile('model/Absorption_Lipophilicity_Prediction_etr_model_optimised.joblib.gz', 'rb') as fu:  
+   Absorption_Lipophilicity_Prediction_etr_model_optimised = joblib.load(fu)
+
+st.markdown(''':orange[ExtraTreesRegressor Optimised]''')
+st.write("Absorption_Lipophilicity Result for Optimised ExtraTreesRegressor : ", round(Absorption_Lipophilicity_Prediction_etr_model_optimised.predict(descriptors_dataframe)[0],4))
+
+st.markdown(''':orange[LGBMRegressor Optimised]''')
+with gzip.GzipFile('model/Absorption_Lipophilicity_Prediction_lgbm_model_optimised.joblib.gz', 'rb') as fb:  
+   Absorption_Lipophilicity_Prediction_lgbm_model_optimised = joblib.load(fb)
+
+st.write("Absorption Lipophilicity Result for Optimised LGBM Regressor : ", round(Absorption_Lipophilicity_Prediction_lgbm_model_optimised.predict(descriptors_dataframe)[0],4))