Upload main.py

main.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import re
+from PIL import Image
+import webbrowser
+
+from rdkit import Chem
+from rdkit.Chem import AllChem
+from rdkit.Chem import Draw
+from rdkit.Chem import rdChemReactions as Reactions
+
+import tensorflow as tf
+from tensorflow import keras
+from keras.preprocessing import sequence
+from keras.utils import pad_sequences
+import keras
+from keras import backend as K
+from keras.models import load_model
+import argparse
+import h5py
+import pdb
+
+
+seq_rdic = ['A', 'I', 'L', 'V', 'F', 'W', 'Y', 'N', 'C', 'Q', 'M',
+    'S', 'T', 'D', 'E', 'R', 'H', 'K', 'G', 'P', 'O', 'U', 'X', 'B', 'Z']
+seq_dic = {w: i+1 for i, w in enumerate(seq_rdic)}
+
+
+@st.cache(allow_output_mutation=True)
+def encodeSeq(seq, seq_dic):
+    if pd.isnull(seq):
+        return [0]
+    else:
+        return [seq_dic[aa] for aa in seq]
+
+
+@st.cache(allow_output_mutation=True)
+def load_modelfile(model_string):
+	loaded_model = tf.keras.models.load_model(model_string)
+	return loaded_model
+
+
+@st.cache(allow_output_mutation=True)
+def prot_feature_gen_from_str_input(prot_input_str, prot_len=2500):
+    Prot_ID = prot_input_str.split(':')[0]
+    Prot_seq = prot_input_str.split(':')[1]
+    prot_dataframe = pd.DataFrame(
+        {'Protein_ID': Prot_ID, 'Sequence': Prot_seq}, index=[0])
+    prot_dataframe.set_index('Protein_ID')
+
+    prot_dataframe["encoded_sequence"] = prot_dataframe.Sequence.map(
+        lambda a: encodeSeq(a, seq_dic))
+    prot_feature = pad_sequences(
+        prot_dataframe["encoded_sequence"].values, prot_len)
+
+    return prot_feature, Prot_ID
+
+
+@st.cache(allow_output_mutation=True)
+def mol_feature_gen_from_str_input(mol_str, kegg_id_flag, kegg_df):
+
+	if kegg_id_flag == 1:
+		KEGG_ID = mol_str
+		kegg_id_loc = kegg_df.index[kegg_df.Compound_ID == KEGG_ID][0]
+		KEGG_ID_info = kegg_df.loc[kegg_id_loc]
+		KEGG_ID_info_df = KEGG_ID_info.to_frame().T.set_index('Compound_ID')
+
+		final_return = KEGG_ID_info_df
+		final_id = KEGG_ID
+
+	else:
+		try:
+			mol_ID = mol_str.split(':')[0]
+			mol_smiles = mol_str.split(':')[1]
+			mol = Chem.MolFromSmiles(mol_smiles)
+			fp1 = AllChem.GetMorganFingerprintAsBitVect(
+			    mol, useChirality=True, radius=2, nBits=2048)
+			fp_list = list(np.array(fp1).astype(float))
+			fp_str = list(map(str, fp_list))
+			mol_fp = '\t'.join(fp_str)
+
+			mol_dict = {}
+			mol_dict['Compound_ID'] = mol_ID
+			mol_dict['Smiles'] = mol_smiles
+			mol_dict['morgan_fp_r2'] = mol_fp
+
+			mol_info_df = pd.DataFrame(mol_dict, index=[0])
+			mol_info_df = mol_info_df.set_index('Compound_ID')
+
+			final_return = mol_info_df
+			final_id = mol_ID
+
+		except Exception as error:
+			print('Something wrong with molecule input string...' + repr(error))
+
+	return final_return, final_id
+
+
+@st.cache(allow_output_mutation=True)
+def act_df_gen_mol_feature(mol_id, prot_id):
+	act_df = pd.DataFrame(
+	    {'Protein_ID': prot_id, 'Compound_ID': mol_id}, index=[0])
+
+	return act_df
+
+
+@st.cache(allow_output_mutation=True)
+def compound_feature_gen_df_input(act_df, comp_df, comp_len=2048, comp_vec='morgan_fp_r2'):
+	act_df = pd.merge(act_df, comp_df, left_on='Compound_ID', right_index=True)
+	comp_feature = np.stack(act_df[comp_vec].map(lambda fp: fp.split("\t")))
+	comp_feature = comp_feature.astype('float')
+	return comp_feature
+
+
+@st.cache(allow_output_mutation=True)
+def model_prediction(compound_feature, enz_feature, model):
+    prediction_vals = model.predict([compound_feature, enz_feature])
+
+    return prediction_vals[0][0]
+
+
+# loaded_model = load_modelfile('./../CNN_results/model_final.model')
+
+# KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')
+# kegg_df = KEGG_compound_read.reset_index()
+
+
+def main():
+	graph = tf.compat.v1.get_default_graph()
+	ld_model = tf.keras.models.load_model('./../CNN_results_split_final/Final_model.model')
+
+	KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')
+	kegg_df = KEGG_compound_read.reset_index()
+	
+	
+    # def img_to_bytes(img_path):
+    #     img_bytes = Path(img_path).read_bytes()
+    #     encoded = base64.b64encode(img_bytes).decode()
+    #     return encoded
+    # # st.title('dGPredictor')
+
+    # header_html = "<img src='../figures/header.png'>"
+
+    # st.markdown(
+    #     header_html, unsafe_allow_html=True,
+    # )
+
+
+	st.image('./header.png', use_column_width=True)
+
+	st.subheader('Enzyme-Substrate Activity Predictor ')
+
+	st.subheader('Enzyme sequence')
+	st.caption('Please follow the input format show in the text box--> id:Sequence')
+
+	enz_str = st.text_input('', value="A0A4P8WFA8:MTKRVLVTGGAGFLGSHLCERLLSEGHEVICLDNFGSGRRKNIKEFEDHPSFKVNDRDVRISESLPSVDRIYHLASRASPADFTQFPVNIALANTQGTRRLLDQARACDARMVFASTSEVYGDPKVHPQPETYTGNVNIRGARGCYDESKRFGETLTVAYQRKYDVDARTVRIFNTYGPRMRPDDGRVVPTFVTQALRGDDLTIYGDGEQTRSFCYVDDLIEGLISLMRVDNPEHNVYNIGKENERTIKELAYEVLGLTDTESDIVYEPLPEDDPGQRRPDITRAKTELDWEPKISLREGLEDTITYFDN")
+
+    # url = 'https://www.genome.jp/dbget-bin/www_bget?rn:R00801'
+    # if st.button('KEformat example'):
+    #     webbrowser.open_new_tab(url)
+
+	st.subheader('Substrate ')
+	st.caption('Please follow the input format show in the text box--> KEGG id or click the checkbox')
+
+	comp_str = st.text_input('', value="C00149")
+	if st.checkbox('If you are entering smiles string along with KEGG ID'):
+		add_info = st.text_area('Additional information (id: Smiles):', "C00149:O[C@@H](CC([O-])=O)C([O-])=O")
+	else:
+		add_info = ''
+
+	if st.button("Predict"):
+    # if session_state.button_search:
+# 		st.subheader('Enzyme-Substrate activity score')
+		with st.spinner('Calculating...'):
+			try:
+# 				st.write('I am inside')
+				prot_feature, prot_id = prot_feature_gen_from_str_input(enz_str)
+				if len(add_info) == 0:
+					kegg_id_flag = 1
+					comp_feature, comp_id = mol_feature_gen_from_str_input(comp_str, kegg_id_flag, kegg_df)
+				else:
+					kegg_id_flag = 0
+					comp_feature, comp_id = mol_feature_gen_from_str_input(add_info, kegg_id_flag, kegg_df)
+				
+				act_dataframe = act_df_gen_mol_feature(comp_id, prot_id)
+# 				st.write(act_dataframe)
+				compound_feature = compound_feature_gen_df_input(act_dataframe, comp_feature)
+# 				st.write(compound_feature)
+            
+			except Exception as e:
+				st.write('Error somewhere...' + repr(e))
+				
+# 			st.write(compound_feature)
+# 			st.write(prot_feature)
+# 			keras.backend.clear_session()
+
+			y = ld_model.predict([compound_feature, prot_feature])
+			
+			subheaderstring = 'EnzRank Score for '+ prot_id +  '-' + comp_id + ' pair:'
+			st.subheader(subheaderstring)
+			st.write(str(y[0][0]))
+			
+if __name__ == '__main__':
+    main()