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import joblib | |
import pandas as pd | |
import streamlit as st | |
model = joblib.load('model_XG.joblib') | |
unique_values = joblib.load('unique_values_XG.joblib') | |
def main(): | |
st.title("Iris's Class") | |
with st.form("questionaire"): | |
sepal_length = st.slider("Sepal_length", min_value=0, max_value=6) | |
sepal_width = st.slider("Sepal_width", min_value=0, max_value=6) | |
petal_length = st.slider("Petal_length", min_value=0, max_value=6) | |
petal_width = st.slider("Petal_width", min_value=0, max_value=6) | |
# clicked==True only when the button is clicked | |
clicked = st.form_submit_button("Predict class") | |
if clicked: | |
result=model.predict(pd.DataFrame({"sepal_length": [sepal_length], | |
"sepal_width": [sepal_width], | |
"petal_length": [petal_length], | |
"petal_width": [petal_width]})) | |
# Show prediction | |
if result[0] == 'Iris-setosa': | |
st.write("Iris-setosa") | |
elif result[0] == 'Iris-versicolor': | |
st.write("Iris-versicolor") | |
elif result[0] == 'Iris-virginica': | |
st.write("Iris-virginica") | |
st.success("Your predicted class is"+result[0]) | |
if __name__ == "__main__": | |
main() | |