bug fixess

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import numpy as np
 
-# Custom CSS for styling
+
 st.markdown(
     """
     <style>
@@ -23,10 +23,10 @@ st.markdown(
 )
 
 def map_to_emotion(spo2, bp, temp):
-    # Convert spo2 to numeric type
+
     spo2_numeric = float(spo2.split('%')[0])
 
-    # Spo2 mapping
+
     if spo2_numeric >= 96:
         spo2_emotion = ["Joy", "Anticipation", "Trust"]
     elif spo2_numeric == 93 or spo2_numeric == 94:
@@ -34,7 +34,7 @@ def map_to_emotion(spo2, bp, temp):
     else:
         spo2_emotion = ["Anger", "Disgust"]
 
-    # BP mapping
+
     if bp == "110/70mmHg":
         bp_emotion = ["Trust"]
     elif bp == "122/74 mmHg":
@@ -42,7 +42,6 @@ def map_to_emotion(spo2, bp, temp):
     else:
         bp_emotion = ["Surprise"]
 
-    # Temperature mapping
     if temp >= "98.7F" and temp <= "99.1F":
         temp_emotion = ["Joy", "Surprise", "Disgust", "Anticipation"]
     elif temp < "98.7F":
@@ -50,14 +49,13 @@ def map_to_emotion(spo2, bp, temp):
     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)
@@ -67,19 +65,19 @@ 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}%")