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app.py
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app.py
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import streamlit as st
import joblib
import sklearn
import pandas
import numpy as np
from prediction import predict_yield
model = joblib.load(r'Model/Linear_Regression_Optimum_Model.joblib')
st.set_page_config(page_title="Blueberry Yield Prediction",layout="wide")
features = ['clonesize', 'honeybee', 'bumbles', 'andrena', 'osmia','AverageOfUpperTRange','AverageOfLowerTRange','AverageRainingDays']
st.markdown("<h1 style='text-align: center;'>Blueberry Yield Prediction Application</h1>", unsafe_allow_html=True)
def main():
with st.form('prediction_form'):
st.header('Enter the input for following features:')
clonesize = st.number_input('The average blueberry clone size in the field (in m2)')
honeybee = st.number_input('Honeybee density in the field in (in bees/m2/min)')
bumbles = st.number_input('Bumblebee density in the field (in bees/m2/min)')
andrena = st.number_input('Andrena bee density in the field (in bees/m2/min)')
osmia = st.number_input('Osmia bee density in the field (in bees/m2/min)')
AverageOfUpperTRange = st.number_input('The average of the upper band daily air temperature (in ℃)')
AverageOfLowerTRange = st.number_input('The average of the lower band daily air temperature (in ℃)')
AverageRainingDays = st.number_input('The average of raining days of the entire bloom season (in Days)')
submit = st.form_submit_button("Predict Yield")
if submit:
input_values = np.array([clonesize, honeybee, bumbles, andrena, osmia, AverageOfUpperTRange, AverageOfLowerTRange, AverageRainingDays]).reshape(1,-1)
pred = predict_yield(input_values, model)
st.success('The forecasted yield is {}'.format(round(pred[0],2)))
if __name__ == '__main__':
main()