Spaces:
Configuration error
Configuration error
| import streamlit as st | |
| import pandas as pd | |
| from PIL import Image | |
| import subprocess | |
| import os | |
| import base64 | |
| import pickle | |
| # Molecular descriptor calculator | |
| def desc_calc(): | |
| # Performs the descriptor calculation | |
| bashCommand = "java -Xms2G -Xmx2G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -fingerprints -descriptortypes ./PaDEL-Descriptor/PubchemFingerprinter.xml -dir ./ -file descriptors_output.csv" | |
| process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE,shell=True) | |
| output, error = process.communicate() | |
| os.remove('molecule.smi') | |
| # File download | |
| def filedownload(df): | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions | |
| href = f'<a href="data:file/csv;base64,{b64}" download="prediction.csv">Download Predictions</a>' | |
| return href | |
| # Model building | |
| def build_model(input_data): | |
| # Reads in saved regression model | |
| load_model = pickle.load(open('acetylcholinesterase_model.pkl', 'rb')) | |
| # Apply model to make predictions | |
| prediction = load_model.predict(input_data) | |
| st.header('**Prediction output**') | |
| prediction_output = pd.Series(prediction, name='pIC50') | |
| molecule_name = pd.Series(load_data[1], name='molecule_name') | |
| df = pd.concat([molecule_name, prediction_output], axis=1) | |
| st.write(df) | |
| st.markdown(filedownload(df), unsafe_allow_html=True) | |
| # Logo image | |
| image = Image.open('logo.png') | |
| st.image(image, use_column_width=True) | |
| # Page title | |
| st.markdown(""" | |
| # Better Than a Placebo - Optum Hackathon 2022 | |
| This app allows you to predict the bioactivity towards inhibting the `Acetylcholinesterase` enzyme. `Acetylcholinesterase` is a drug target for Alzheimer's disease. | |
| **Credits** | |
| - App built in `Python` + `Streamlit` by Better Than a Placebo team - Optum Hackathon 2022 | |
| - Descriptor calculated using [PaDEL-Descriptor](http://www.yapcwsoft.com/dd/padeldescriptor/) [[Read the Paper]](https://doi.org/10.1002/jcc.21707). | |
| --- | |
| """) | |
| # Sidebar | |
| with st.sidebar.header('1. Upload your CSV data'): | |
| uploaded_file = st.sidebar.file_uploader("Upload your input file", type=['txt']) | |
| st.sidebar.markdown(""" | |
| [Example input file](https://raw.githubusercontent.com/dataprofessor/bioactivity-prediction-app/main/example_acetylcholinesterase.txt) | |
| """) | |
| if st.sidebar.button('Predict'): | |
| load_data = pd.read_table(uploaded_file, sep=' ', header=None) | |
| load_data.to_csv('molecule.smi', sep = '\t', header = False, index = False) | |
| st.header('**Original input data**') | |
| st.write(load_data) | |
| with st.spinner("Calculating descriptors..."): | |
| desc_calc() | |
| # Read in calculated descriptors and display the dataframe | |
| st.header('**Calculated molecular descriptors**') | |
| desc = pd.read_csv('descriptors_output.csv') | |
| st.write(desc) | |
| st.write(desc.shape) | |
| # Read descriptor list used in previously built model | |
| st.header('**Subset of descriptors from previously built models**') | |
| Xlist = list(pd.read_csv('descriptor_list.csv').columns) | |
| desc_subset = desc[Xlist] | |
| st.write(desc_subset) | |
| st.write(desc_subset.shape) | |
| # Apply trained model to make prediction on query compounds | |
| build_model(desc_subset) | |
| else: | |
| st.info('Upload input data in the sidebar to start!') | |