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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(cm)", 0.0, 10.0, 0.1) | |
sepal_width = st.slider("Sepal_width(cm)", 0.0, 10.0, 0.1) | |
petal_length = st.slider("Petal_length(cm)", 0.0, 10.0, 0.1) | |
petal_width = st.slider("Petal_width(cm)", 0.0, 10.0, 0.1) | |
# 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] == 0: | |
result = "Iris-setosa" | |
elif result[0] == 1: | |
result = "Iris-versicolor" | |
else: | |
result = "Iris-virginica" | |
st.success(f"Your predicted class is {result}") | |
if __name__ == "__main__": | |
main() | |