https://github.com/pazindushane/eco-tree-web-application

eco-tree is a machine learning based we application build using Angular-14 and Flask.
https://github.com/pazindushane/eco-tree-web-application

Last synced: 8 months ago
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eco-tree is a machine learning based we application build using Angular-14 and Flask.

Host: GitHub
URL: https://github.com/pazindushane/eco-tree-web-application
Owner: pazindushane
License: mit
Created: 2022-07-17T15:23:08.000Z (about 3 years ago)
Default Branch: master
Last Pushed: 2023-03-03T01:27:33.000Z (over 2 years ago)
Last Synced: 2025-01-07T00:36:29.002Z (9 months ago)
Language: TypeScript
Size: 5.77 MB
Stars: 1
Watchers: 1
Forks: 1
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# eco-tree Web Application

## About

eco-tree is a machine learning based web application build using Angular-14 and Flask. eco-tree is used for recommending Crop and Fertilizers based on the soil features. The application used three accuractely predciting models to make the final predictions. The link for the python notebooks containing the implementation for the models are present in the All Links section of this Readme.

An API was built using Flask and was deployed on AWS. This Flask API is used to get the recommendation made by the machine learning models in JSON format. The input data is sent as a POST request to the API to get the predicted information.

Angular-14 was used to build the frontend part of the application. MaterialUI was used for frontend part of the web application.

Deployed Web Application Link: http://ecotree-app.s3-website.ap-south-1.amazonaws.com/

Flask API End Points

1. Crop Recommendation End Point: http://ecotreeappfive-env.eba-hphdscvg.ap-south-1.elasticbeanstalk.com/predict_crop
2. Fertilizer Recommendation End Point: http://ecotreeappfive-env.eba-hphdscvg.ap-south-1.elasticbeanstalk.com/predict_fertilizer

About Data returned by Flask API

The data is returned in JSON format:

1. For Crop Recommendation

```
{
xgb_model_prediction: "rice",
xgb_model_probability: 99.2%,
rf_model_prediction: "rice",
rf_model_probability: 99.3%,
knn_model_prediction: "rice",
knn_model_probability: 99.5%,
final_prediction: "rice",
}
```

2. For Fertilizer Recommendation

```
{
xgb_model_prediction: "Urea",
xgb_model_probability: 99.2%,
rf_model_prediction: "Urea",
rf_model_probability: 99.3%,
svm_model_prediction: "Urea",
svm_model_probability: 99.5%,
final_prediction: "Urea",
}
```

Note: This API can be used in your Web Application or Mobile Application by just sending a POST request with the necessary input data to the above mentioned end points.

## Steps to run the Angular-14 Application in local

1. Clone this repo.
2. Open command prompt in the source folder in frontend
3. Install all the npm packages

```
npm install
```

4. Start the application

```
npm start
```

The Application Runs on localhost:3000

## Steps to run the Flask API in local

1. Clone this repo
2. Open command prompt in "Flask_API"
3. Create a virtual environment

```
mkvirtualenv environment_name
```

4. Install all the packages

```
pip install -r requirements.txt
```

5. Run the app.py file