app

app.py

@@ -0,0 +1,86 @@
+import streamlit as st
+import numpy as np
+
+# Custom CSS for styling
+st.markdown(
+    """
+    <style>
+        .header-text {
+            color: #333333;
+            text-align: center;
+            font-size: 24px;
+            font-weight: bold;
+            margin-bottom: 20px;
+        }
+        .result-text {
+            color: #333333;
+            font-size: 18px;
+            margin-bottom: 10px;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+def map_to_emotion(spo2, bp, temp):
+    # Spo2 mapping
+    if spo2 >= 96:
+        spo2_emotion = ["Joy", "Anticipation", "Trust"]
+    elif spo2 == 93 or spo2 == 94:
+        spo2_emotion = ["Fear"]
+    else:
+        spo2_emotion = ["Anger", "Disgust"]
+
+    # BP mapping
+    if bp == "110/70mmHg":
+        bp_emotion = ["Trust"]
+    elif bp == "122/74 mmHg":
+        bp_emotion = ["Joy"]
+    else:
+        bp_emotion = ["Surprise"]
+
+    # Temperature mapping
+    if temp >= 98.7 and temp <= 99.1:
+        temp_emotion = ["Joy", "Surprise", "Disgust", "Anticipation"]
+    elif temp < 98.7:
+        temp_emotion = ["Sadness"]
+    else:
+        temp_emotion = ["Fear", "Anger"]
+
+    # Combine all emotions
+    emotions = spo2_emotion + bp_emotion + temp_emotion
+    return emotions
+
+def predict_levels(emotions):
+    # Placeholder for machine learning models
+    # Here, we generate random predictions as placeholders
+    stress_percentage = np.random.randint(0, 100)
+    anxiety_percentage = np.random.randint(0, 100)
+    depression_percentage = np.random.randint(0, 100)
+    return stress_percentage, anxiety_percentage, depression_percentage
+
+def main():
+    st.title("Emotion Analysis and Mental Health Prediction")
+    st.markdown("### Enter Vital Parameters:")
+    
+    # User inputs
+    spo2 = st.selectbox("Select Spo2 Level", ["96% or more", "93-94%", "92% or less"])
+    bp = st.selectbox("Select Blood Pressure Level", ["110/70mmHg", "122/74 mmHg", "Others"])
+    temp = st.selectbox("Select Body Temperature", ["98.7F-99.1F", "Less than 98.7F", "Greater than 99.1F"])
+
+    # Map inputs to emotions
+    emotions = map_to_emotion(spo2, bp, temp)
+
+    st.markdown("### Emotion Analysis Results:")
+    for emotion in emotions:
+        st.write(f"- {emotion}")
+
+    # Predict levels using machine learning models
+    st.markdown("### Predicted Mental Health Levels:")
+    stress_percentage, anxiety_percentage, depression_percentage = predict_levels(emotions)
+    st.write(f"Stress Percentage (approx): {stress_percentage}%")
+    st.write(f"Anxiety Percentage (approx): {anxiety_percentage}%")
+    st.write(f"Depression Percentage (approx): {depression_percentage}%")
+
+if __name__ == "__main__":
+    main()