https://github.com/dataprofessor/bioactivity-prediction-app
https://github.com/dataprofessor/bioactivity-prediction-app
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/dataprofessor/bioactivity-prediction-app
- Owner: dataprofessor
- Created: 2021-02-21T15:20:42.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-22T05:55:05.000Z (almost 5 years ago)
- Last Synced: 2025-04-05T07:33:13.663Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 19.9 MB
- Stars: 89
- Watchers: 1
- Forks: 79
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# bioactivity-prediction-app
# Watch the tutorial video
[Bioinformatics Project from Scratch - Drug Discovery #6 (Deploy Model as Web App) | Streamlit #22](https://youtu.be/htBIP17S-20)
# Reproducing this web app
To recreate this web app on your own computer, do the following.
### Create conda environment
Firstly, we will create a conda environment called *bioactivity*
```
conda create -n bioactivity python=3.7.9
```
Secondly, we will login to the *bioactivity* environement
```
conda activate bioactivity
```
### Install prerequisite libraries
Download requirements.txt file
```
wget https://raw.githubusercontent.com/dataprofessor/bioactivity-prediction-app/main/requirements.txt
```
Pip install libraries
```
pip install -r requirements.txt
```
### Download and unzip contents from GitHub repo
Download and unzip contents from https://github.com/dataprofessor/bioactivity-prediction-app/archive/main.zip
### Generating the PKL file
The machine learning model used in this web app will firstly have to be generated by successfully running the included Jupyter notebook [bioactivity_prediction_app.ipynb](https://github.com/dataprofessor/bioactivity-prediction-app/blob/main/bioactivity_prediction_app.ipynb). Upon successfully running all code cells, a pickled model called acetylcholinesterase_model.pkl will be generated.
### Launch the app
```
streamlit run app.py
```