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metadata
title: FoodNet
emoji: 🍔
colorFrom: purple
colorTo: purple
sdk: streamlit
app_file: foodnet.py

24-679 FoodNet Project

Authors

David Chuan-En Lin: chuanenl@cs.cmu.edu

Mitch Fogelson: mfogelso@andrew.cmu.edu

Sunny Yang: yundiy@andrew.cmu.edu

Shihao Xu: shihaoxu@andrew.cmu.edu

TODO

Must Have

  1. Cooking method (How to do this?) (TBD)
  2. Ingredients -> Recipe (Recipe Querey?) (Mitch)
  3. Cuisine Meta Data (Where to get) (TBD)
  4. Deployment on the cloud -> (David)

Like to have

  1. Images related ->
  1. User Studies

Moonshot

  1. Recipe Masking Prediction

Description

We wanted to help students and households in the Pittsburgh to reduce their food waste. We developed a model that suggests recipes based on current leftovers availible.

Try WebApp

https://huggingface.co/spaces/chuanenlin/foodnet

Quick Start

  1. Clone repository
git clone git@github.com:chuanenlin/foodnet.git
  1. Move into repository
cd foodnet

(Optional Create conda environment)

  1. Install gdown
pip install gdown
  1. Download models
gdown https://drive.google.com/drive/folders/1LlQpd45E71dSfC8FgvIhJjQjqxnlBC9j -O ./models --folder
  1. Download datasets (Optional)
gdown https://drive.google.com/drive/folders/18aA3BFKqzkqNz5L4N5vN6bFnp8Ch2CQV -O ./data --folder
  1. Install Dependencies
pip install -r requirements.txt
  1. Run code
streamlit run foodnet.py

Args

Train new model

streamlit run foodnet.py -d/--dataset ['/PATH/TO/DATASET'] -t/--train True

Load alternative model

streamlit run foodnet.py --model ['/PATH/TO/MODEL']

Requirements

  • python>=3.6
  • gensim>=4.0.x
  • streamlit
  • gdown
  • nltk
  • pickle
  • matplotlib

References

TODO