/Iris-Flower-Classificator-with-Scikit-Learn-Streamlit-and-Deployed-with-Flask-

Iris Flower Classificator with Scikit Learn, Streamlit and Deployed with Flask

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Iris Flower Classificator with Scikit Learn, Streamlit and Deployed with Flask

Iris Flower Classificator with Scikit Learn, Numpy, Pandas, Streamlit and Deployed with Flask

The Model was trained with Tabular Iris Flower Data and with the SVC Scikit-Learn Architecture. The Model predicts if a given Iris Flower is either Setosa, Versicolo or Virginica, also the U.I. to select the parameters of the Iris Flower was built with Streamlit and the API with Flask.

Check-it out the App Deployed in the Streamlit Services

Iris Flower Classificator App Deployed at: https://iris-flower.streamlit.app/

Run it Locally

Test it Locally by running the app.py file, built with Streamlit, and the api.py file with Flask. Remember first to run the api.py file, copy the http url and saved in the API variable of the app.py file, and uncomment the code lines.

App made with Streamlit

streamlit run app.py

Deployed with Flash

python3 api.py

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