Streamlit ML Dashboards

A collection of machine learning and AI apps deployed as interactive web dashboards using Streamlit. Each app lives in its own folder and runs independently.

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Project Structure

streamlit-web-app/
│
├── ai-product-app/              # AI product idea generator (Gemini)
│   ├── app.py                   # Streamlit entry point
│   ├── requirements.txt
│   ├── .gitignore
│   ├── .env.example             # Template for required environment variables
│   ├── README.md
│   ├── app/
│   │   ├── __init__.py
│   │   ├── main.py              # Streamlit UI entrypoint (streamlit run app/main.py)
│   │   ├── ui.py                # UI layout helpers
│   │   ├── prompts.py           # Prompt templates
│   │   ├── llm/
│   │   │   ├── __init__.py
│   │   │   └── gemini_client.py # Functions that call the Gemini API
│   │   └── core/
│   │       ├── __init__.py
│   │       └── config.py        # Loads env vars and settings
│
├── <next-app>/                  # Each new app follows the same pattern
│   ├── app/
│   │   ├── main.py
│   │   ├── models/              # Saved model files (.pkl, .joblib) if needed
│   │   ├── notebooks/           # Training and EDA notebooks
│   │   ├── data/                # Sample or processed datasets
│   │   └── utils/               # Preprocessing and helper functions
│   ├── requirements.txt
│   ├── .env.example
│   └── README.md
│
├── .env                         # Your local secrets — never commit this
├── .gitignore
└── README.md

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Apps

App	Description	Model / API
ai-product-app	Generates AI-powered product ideas	Gemini
(more coming)

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Getting Started

1. Clone the repository

git clone https://github.com/MikeTX56/streamlit-web-app.git
cd streamlit-web-app

2. Navigate into an app

cd ai-product-app

3. Create a virtual environment

python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate

4. Install dependencies

pip install -r requirements.txt

5. Set up environment variables

Copy the example file and fill in your keys:

cp .env.example .env

GEMINI_API_KEY=your_key_here

Never commit .env to GitHub. It is listed in .gitignore.

6. Run the app

streamlit run app/main.py

The app opens at http://localhost:8501.

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Tech Stack

• Frontend — Streamlit
• LLM / AI — Google Gemini API
• Language — Python 3.10+
• Config — python-dotenv

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Deployment

Each app deploys independently on Streamlit Community Cloud:

1. Push the repo to GitHub
2. Go to share.streamlit.io
3. Select the repo and set the main file path — e.g. ai-product-app/app/main.py
4. Add your API keys under the Secrets tab in the dashboard

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.gitignore

.env
__pycache__/
*.pyc
*.pkl
*.joblib
*.h5
venv/
.DS_Store
.ipynb_checkpoints/

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Author

Mikiyas Tadesse — @MikeTX56