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| import streamlit as st | |
| import joblib | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| # Load the models | |
| scaler = joblib.load('scaler.joblib') | |
| model = joblib.load('best_rf.joblib') | |
| sensory_feature_column = ['sweet', | |
| 'sour', | |
| 'bitter', | |
| 'aromatic_impact', | |
| 'fruity_impact', | |
| 'art_sweetener_chewing', | |
| 'chews', | |
| 'art_sweetener_after', | |
| 'stickiness_with_fingers', | |
| 'springiness_with_fingers', | |
| 'abrasive', | |
| 'hardness_with_molars', | |
| 'uniformity_of_bite', | |
| 'gritty', | |
| 'springiness_during_chew', | |
| 'cohesiveness_of_mass', | |
| 'moistness_of_mass', | |
| 'toothsticking', | |
| 'toothpacking', | |
| 'adhesiveness_to_molars', | |
| 'oily_mouthcoating'] | |
| def scale_output(prediction_matrix): | |
| # Calculate the sum for each row | |
| row_sums = np.sum(prediction_matrix, axis=1) | |
| # Calculate the surplus or lack from 1 for each row | |
| delta = 1 - row_sums | |
| # Adjust each row proportionally | |
| adjusted_matrix = prediction_matrix + delta[:, np.newaxis] * (prediction_matrix / row_sums[:, np.newaxis]) | |
| print(f'scaled probability: {adjusted_matrix.sum(axis=1)}') | |
| return adjusted_matrix | |
| # Streamlit App | |
| def main(): | |
| st.title("Consumer liking score distribution prediction") | |
| st.markdown("Please adjust the values of 21 sensory attributes (assumed to have the range from 1 - 10), and see the liking score distribution below") | |
| with st.expander("Feature importance heatmap of sensory attributes on liking score"): | |
| st.image("mmr-corr.png") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| sweet = st.slider('sweet', 0.00, 10.00, 0.01) | |
| aromatic_impact = st.slider('aromatic impact', 0.00, 10.00, 0.01) | |
| chew = st.slider('chew', 0.00, 10.00, 0.01) | |
| springiness_with_fingers = st.slider('springiness_with_fingers', 0.00, 10.00, 0.01) | |
| uniformity_of_bite = st.slider('uniformity_of_bite', 0.00, 10.00, 0.01) | |
| cohesiveness_of_mass = st.slider('cohesiveness_of_mass', 0.00, 10.00, 0.01) | |
| toothpacking = st.slider('toothpacking', 0.00, 10.00, 0.01) | |
| with col2: | |
| sour = st.slider('sour', 0.00, 10.00, 0.01) | |
| fruity_impact = st.slider('fruity impact',0.00, 10.00, 0.01) | |
| art_sweetener_after = st.slider('art_sweetener_after', 0.00, 10.00, 0.01) | |
| abrasiveness = st.slider('abrasiveness', 0.00, 10.00, 0.01) | |
| gritty = st.slider('gritty', 0.00, 10.00, 0.01) | |
| moisture_of_mass = st.slider('moisture_of_mass', 0.00, 10.00, 0.01) | |
| adhesiveness_to_molars = st.slider('adhesiveness_to_molars', 0.00, 10.00, 0.01) | |
| with col3: | |
| bitter = st.slider('bitter', 0.00, 10.00, 0.01) | |
| art_sweetener_chewing = st.slider('art_sweetener_chewing',0.00, 10.00, 0.01) | |
| stickiness_with_fingers = st.slider('stickiness_with_fingers', 0.00, 10.00, 0.01) | |
| hardness_with_molars = st.slider('hardness_with_molars', 0.00, 10.00, 0.01) | |
| springiness_during_chew = st.slider('springiness_during_chew', 0.00, 10.00, 0.01) | |
| toothsticking = st.slider('toothsticking', 0.00, 10.00, 0.01) | |
| oily_mouthcoating = st.slider('oily_mouthcoating', 0.00, 10.00, 0.01) | |
| data_input = [sweet, sour, bitter, aromatic_impact, fruity_impact, art_sweetener_chewing,chew, | |
| art_sweetener_after, stickiness_with_fingers, springiness_with_fingers , abrasiveness, hardness_with_molars , uniformity_of_bite, gritty, | |
| springiness_during_chew, cohesiveness_of_mass, moisture_of_mass , toothsticking , toothpacking, adhesiveness_to_molars, oily_mouthcoating] | |
| test_df = pd.DataFrame(np.array(data_input).reshape(1, 21)) | |
| test_df.columns = sensory_feature_column | |
| scaled_data_input = scaler.transform(test_df) | |
| handout_prediction = scale_output(model.predict(scaled_data_input)) | |
| handout_pred_df = pd.DataFrame(handout_prediction) | |
| handout_pred_df.columns = [str(i) for i in range(1,10)] | |
| handout_pred_df['prod'] = 'prediction' | |
| handout_pred_df = handout_pred_df.melt('prod') | |
| # st.write(f"prediction: {handout_prediction }") | |
| st.write(f"sum of probability: {np.round(handout_prediction.sum(), 2)}") | |
| fig = px.histogram(data_frame=handout_pred_df, x= 'variable',y='value', nbins= 9, | |
| text_auto=True, title="Probability prediction of liking score from sensory attributes", | |
| labels={"value": "probability", | |
| "variable": "liking score"}) | |
| st.plotly_chart(fig, theme='streamlit') | |
| if __name__ == "__main__": | |
| main() | |