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import streamlit as st
import numpy as np
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# Load the saved model
model = tf.keras.models.load_model('models/emotion_model')
# Function to preprocess user inputs and perform prediction
def predict_emotion(spO2, heart_rate, body_temp):
scaler = MinMaxScaler() # Initialize scaler
scaler.fit(pd.DataFrame(columns=['spO2', 'heart-rate', 'body-temperature'], data=[[70, 50, 95.0], [100, 120, 105.0]])) # Fit scaler to specified range
input_data = np.array([[spO2, heart_rate, body_temp]])
input_data_scaled = scaler.transform(input_data)
predicted_emotions = model.predict(input_data_scaled)
return predicted_emotions[0]
def main():
st.title('Emotion Prediction App')
st.sidebar.title('Options')
# User input fields
st.sidebar.header('User Inputs')
spO2 = st.sidebar.slider('Select spO2 level', min_value=70, max_value=100, value=98)
heart_rate = st.sidebar.slider('Select heart rate', min_value=50, max_value=120, value=80)
body_temp = st.sidebar.slider('Select body temperature', min_value=95.0, max_value=105.0, value=98.6)
# Button to trigger emotion prediction
if st.sidebar.button('Predict Emotion'):
# Perform prediction using the loaded model
predicted_emotions = predict_emotion(spO2, heart_rate, body_temp)
emotions = ['Anger', 'Fear', 'Sadness', 'Disgust', 'Surprise', 'Anticipation', 'Trust', 'Joy']
# Display predicted emotions
st.subheader('Predicted Emotions')
for emotion, score in zip(emotions, predicted_emotions):
st.write(f'{emotion}: {score:.2f}')
# Determine likely emotions and display them
likely_emotions = [emotions[i] for i, score in enumerate(predicted_emotions) if score > 0.5]
st.success(f'Most likely emotion(s): {", ".join(likely_emotions)}')
if __name__ == '__main__':
main()
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