YieldBrew is a simple AI-powered tool designed to estimate coffee yield (in kg/ha) based on environmental and agricultural conditions. It was built as a lightweight proof-of-concept for a hackathon focused on sustainable and tech-enabled coffee farming.

YieldBrew uses a regression-based neural network trained on synthetic agricultural data to predict coffee yield from various input features like rainfall, temperature, fertilizer usage, soil pH, and more.

The model is wrapped in a simple interactive Gradio interface for easy demonstration and experimentation.

Prerequisites: Python 3.9+, PyTorch, scikit-learn, Gradio, NumPy, pandas

1. Clone the repo: git clone https://github.com/NicodeScripts/YieldBrew.git cd YieldBrew
2. Install Dependencies: pip install -r requirements.txt
3. Train Model cd model jupyter notebook -> model_train.ipynb
4. Run Demo cd demo python gradio_app.py