Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Potato Disease Classification - Training, Rest APIs, and Frontend to test.
| Date | Stars |
|---|---|
| 2026-07-24 | 267 |
| 2026-07-25 | 267 |
| 2026-07-28 | 267 |
| 2026-07-30 | 267 |
| 2026-08-06 | 267 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Potato Disease Classification ## Setup for Python: 1. Install Python ([Setup instructions](https://wiki.python.org/moin/BeginnersGuide)) 2. Install Python packages ``` pip3 install -r training/requirements.txt pip3 install -r api/requirements.txt ``` 3. Install Tensorflow Serving ([Setup instructions](https://www.tensorflow.org/tfx/serving/setup)) ## Setup for ReactJS 1. Install Nodejs ([Setup instructions](https://nodejs.org/en/download/package-manager/)) 2. Install NPM ([Setup instructions](https://www.npmjs.com/get-npm)) 3. Install dependencies ```bash cd frontend npm install --from-lock-json npm audit fix ``` 4. Copy `.env.example` as `.env`. 5. Change API url in `.env`. ## Setup for React-Native app 1. Go to the [React Native environment setup](https://reactnative.dev/docs/environment-setup), then select `React Native CLI Quickstart` tab. 2. Install dependencies ```bash cd mobile-app yarn install ``` - 2.1 Only for mac users ```bash cd ios && pod install && cd ../ ``` 3. Copy `.env.example` as `.env`. 4. Change API url in `.env`. ## Training the Model 1. Download the data from [kaggle](https://www.kaggle.com/arjuntejaswi/plant-village). 2. Only keep folders related to Potatoes. 3. Run Jupyter Notebook in Browser. ```bash jupyter notebook ``` 4. Open `training/potato-disease-training.ipynb` in Jupyter Notebook. 5. In cell #2, update the path to dataset. 6. Run all the Cells one by one. 7. Copy the model generated and save it with the version number in the `models` folder. ## Running the API ### Using FastAPI 1. Get inside `api` folder ```bash cd api ``` 2. Run the FastAPI Server using uvicorn ```bash uvicorn main:app --reload --host 0.0.0.0 ``` 3. Your API is now running at `0.0.0.0:8000` ### Using FastAPI & TF Serve 1. Get inside `api` folder ```bash cd api ``` 2. Copy the `models.config.example` as `models.config` and update the paths in file. 3. Run the TF Serve (Update config file path below) ```bash docker run -t --rm -p 8501:8501 -v C:/Code/potato-disease-classification:/potato-disease-classification tensorflow/serving --rest_api_port=8501 --model_config_file=/potato-disease-classification/models.config ``` 4. Run the FastAPI Server using uvicorn For this you can directly run it from your main.py or main-tf-serving.py using pycharm run option (as shown in the video tutorial) OR you can run it from command prompt as shown below, ```bash uvicorn main-tf-serving:app --reload --host 0.0.0.0 ``` 5. Your API is now running at `0.0.0.0:8000` ## Running the Frontend 1. Get inside `api` folder ```bash cd frontend ``` 2. Copy the `.env.example` as `.env` and update `REACT_APP_API_URL` to API URL if needed. 3. Run the frontend ```bash npm run start ``` ## Running the app 1. Get inside `mobile-app` folder ```bash cd mobile-app ``` 2. Copy the `.env.example` as `.env` and update `URL` to API URL if needed. 3. Run the app (android/iOS) ```bash npm run android ``` or ```bash npm run ios ``` 4. Creating public ([signed APK](https://reactnative.dev/docs/signed-apk-android)) ## Creating the TF Lite Model 1. Run Jupyter Notebook in Browser. ```bash jupyter notebook ``` 2. Open `training/tf-lite-converter.ipynb` in Jupyter Notebook. 3. In cell #2, update the path to dataset. 4. Run all the Cells one by one. 5. Model would be saved in `tf-lite-models` folder. ## Deploying the TF Lite on GCP 1. Create a [GCP account](https://console.cloud.google.com/freetrial/signup/tos?_ga=2.25841725.1677013893.1627213171-706917375.1627193643&_gac=1.124122488.1627227734.Cj0KCQjwl_SHBhCQARIsAFIFRVVUZFV7wUg-DVxSlsnlIwSGWxib-owC-s9k6rjWVaF4y7kp1aUv5eQaAj2kEALw_wcB). 2. Create a [Project on GCP](https://cloud.google.com/appengine/docs/standard/nodejs/building-app/creating-project) (Keep note of the project id). 3. Create a [GCP bucket](https://console.cloud.google.com/storage/browser/). 4. Upload the potatoes.h5 model in the bucket in the path `models/potatos.h5`. 5. Install Google Cloud SD
Excerpt of 5,816 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d640220000c4761d, topic:deep-learning, topic:tensorflow