--fix

app.py

@@ -0,0 +1,45 @@
+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()

emo-final.csv

@@ -0,0 +1,501 @@
+spO2,heart-rate,body-temperature,anger,fear,sadness,disgust,surprise,anticipation,trust,joy,stress,anxiety,depression
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emo.csv

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+spO2,heart-rate,body-temperature,anger,fear,sadness,disgust,surprise,anticipation,trust,joy,stress,anxiety,depression
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emotion_model/fingerprint.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0804be7521d4a151453b30271b7d941f8c3623e69c3fe2913089777989a9cbb
+size 57

emotion_model/keras_metadata.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57faac85e3fa64bf2883be7640054bf4b4814ddfce63abc30dcd1575b57136ab
+size 8170

emotion_model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe72c568b8a44ae0b65f5085285762598e162b7fd7745d925a062b04a2c2f833
+size 92028

model.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+import tensorflow as tf
+# Load the dataset
+data = pd.read_csv('emo-final.csv')
+
+# Separate features (X) and target labels (y)
+X = data[['spO2', 'heart-rate', 'body-temperature']]
+y = data[['anger', 'fear', 'sadness', 'disgust', 'surprise', 'anticipation', 'trust', 'joy']]
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Normalize features using Min-Max scaling
+scaler = MinMaxScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(64, activation='relu', input_shape=(3,)),
+    tf.keras.layers.Dense(64, activation='relu'),
+    tf.keras.layers.Dense(8, activation='softmax')  # Output layer with 8 units for 8 emotions
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='mse')
+
+# Train the model
+model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_data=(X_test_scaled, y_test))
+
+# Save the trained model
+model.save('emotion_model')