| | |
| | """app.ipynb |
| | |
| | Automatically generated by Colaboratory. |
| | |
| | Original file is located at |
| | https://colab.research.google.com/drive/1QDGbOvpwcdGFAUHlqUqKjWG_UkARrh_A |
| | """ |
| |
|
| | import joblib |
| | import pandas as pd |
| | import streamlit as st |
| |
|
| | SPC_DICT = {'setosa': 0, |
| | 'versicolor': 1, |
| | 'virginica': 2 |
| | } |
| |
|
| |
|
| | model = joblib.load('model.joblib') |
| | unique_values = joblib.load('unique_values.joblib') |
| | |
| | unique_sepal_length = unique_values["sepal_length"] |
| | unique_sepal_width = unique_values["sepal_width"] |
| | unique_petal_length = unique_values["petal_length"] |
| | unique_petal_width = unique_values["petal_width"] |
| |
|
| | def main(): |
| | st.title("Iris") |
| |
|
| | with st.form("questionnaire"): |
| | sepal_length = st.slider("sepal_length", min_value=0, max_value=20) |
| | sepal_width = st.slider("sepal_width", min_value=0, max_value=20) |
| | petal_length = st.slider("petal_length", min_value=0, max_value=20) |
| | petal_width = st.slider("petal_width", min_value=0, max_value=20) |
| | clicked = st.form_submit_button("Predict iris") |
| |
|
| | if clicked: |
| | result = model.predict(pd.DataFrame({ |
| | "sepal_length": [sepal_length], |
| | "sepal_width": [sepal_width], |
| | "petal_length": [petal_length], |
| | "petal_width": [petal_width] |
| | })) |
| | result = 'setosa' if result[0] == 0 result = 'versicolor' elif result[0] == 1 else result = 'virginica' |
| | st.success('The predicted iris is {}'.format(result)) |
| |
|
| | if __name__ == '__main__': |
| | main() |
