Running TryMLEasy locally requires Python 3.8 or newer and pip. The entire setup takes only a few minutes — you clone one repository, install the dependencies listed inDocumentation Index
Fetch the complete documentation index at: https://mintlify.com/Gurneet1928/TryMLEasy/llms.txt
Use this file to discover all available pages before exploring further.
requirements.txt, and launch the Streamlit development server. No database, no environment variables, and no build step are needed.
Prerequisites
Before you begin, confirm the following tools are available in your terminal.| Tool | Minimum Version | Purpose |
|---|---|---|
| Python | 3.8 | Runtime for all application code |
| pip | Latest stable | Installs Python packages |
| git | Any recent version | Clones the repository |
Install Steps
Clone the repository
requirements.txt — into a new TryMLEasy directory and moves you into it.Create a virtual environment (optional but recommended)
A virtual environment keeps TryMLEasy’s dependencies isolated from your system Python installation.Then activate it:Your terminal prompt will change to show
- macOS / Linux
- Windows
(venv) when the environment is active.Install all dependencies
requirements.txt. Allow a few minutes for TensorFlow to download and install.Launch the app
Dependencies
The following packages are declared inrequirements.txt and are installed automatically by pip install -r requirements.txt.
| Package | Purpose |
|---|---|
extra_streamlit_components | Advanced Streamlit UI components including step-progress indicators |
keras | High-level neural network API (used as the TensorFlow front-end) |
matplotlib | Static plots and neural network architecture diagrams |
mpld3 | Converts Matplotlib figures to interactive D3.js-powered charts |
numpy | Numerical computing foundation used across all ML operations |
pandas | Dataset loading, manipulation, and tabular display in the app |
Pillow | Image loading and processing (used for diagrams and UI assets) |
plotly | Interactive charts including the predicted-vs-true scatter plot |
scikit_learn | All traditional ML models, scalers, and decomposition methods |
seaborn | Statistical data visualization, including the correlation heatmap |
st_pages | Multi-page Streamlit navigation and page visibility management |
streamlit | Core web app framework and interactive frontend |
streamlit_extras | Extra utilities including the switch_page navigation helper |
streamlit_plotly_events | Captures user interaction events on Plotly charts within Streamlit |
tensorflow | Deep learning framework powering the neural network builder and training |
Running the App
Once dependencies are installed, you can start the app with either of these equivalent commands:8501 is already in use on your machine, Streamlit will automatically try 8502, 8503, and so on — it will print the actual URL in your terminal output.
Deploying to Streamlit Cloud
TryMLEasy can be deployed for free on Streamlit Cloud so you (or others) can access it from any browser without a local install.- Fork the TryMLEasy repository to your own GitHub account.
- Sign in at https://streamlit.io/cloud with your GitHub account.
- Click New app, select your forked repository, set the main file path to
home_page.py, and click Deploy.
requirements.txt automatically and handles the full installation. Your public app URL will be active within a few minutes. The official deployment lives at https://trymleasy-2-0.streamlit.app/.