health_score.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Initialize session state variables if they don't exist
def init_session_state():
    if 'current_step' not in st.session_state:
        st.session_state.current_step = 0
    if 'assessment_data' not in st.session_state:
        st.session_state.assessment_data = {}
    if 'results_calculated' not in st.session_state:
        st.session_state.results_calculated = False
    if 'risk_data' not in st.session_state:
        st.session_state.risk_data = []
    if 'health_score' not in st.session_state:
        st.session_state.health_score = 0
    if 'recommendations' not in st.session_state:
        st.session_state.recommendations = []

# Define steps for the assessment
steps = [
    {"title": "Basic Information", "fields": ["age", "gender", "height", "weight"]},
    {"title": "Lifestyle", "fields": ["smoking_status", "alcohol_consumption", "physical_activity", "diet_type"]},
    {"title": "Medical History", "fields": ["family_history_diabetes", "family_history_heart_disease", 
                                           "family_history_hypertension", "previous_diagnoses"]},
    {"title": "Vital Signs", "fields": ["systolic_bp", "diastolic_bp", "resting_heart_rate"]},
    {"title": "Mental Health", "fields": ["stress_level", "sleep_quality", "sleep_duration"]},
    {"title": "Nutrition", "fields": ["daily_water_intake", "daily_fruit_veg_servings"]},
    {"title": "Additional Metrics", "fields": ["waist_circumference", "body_fat_percentage"]},
    {"title": "Additional Health Information", "fields": ["family_history_asthma", "family_history_obesity", 
                                                         "family_history_depression", "allergies", 
                                                         "chronic_pain", "mental_health_history"]}
]

# Function to calculate risk for different diseases
def calculate_risk(disease, data):
    risk = 0
    
    if disease == "Diabetes":
        if data.get("age", 0) > 45: risk += 10
        if float(data.get("bmi", 0)) > 30: risk += 15
        if data.get("family_history_diabetes", False): risk += 15
        if data.get("physical_activity", "") == "sedentary": risk += 10
    
    elif disease == "Heart Disease":
        if data.get("age", 0) > 55: risk += 10
        if data.get("systolic_bp", 0) > 140 or data.get("diastolic_bp", 0) > 90: risk += 15
        if data.get("family_history_heart_disease", False): risk += 15
        if data.get("smoking_status", "") == "current": risk += 15
    
    elif disease == "Hypertension":
        if data.get("systolic_bp", 0) > 140 or data.get("diastolic_bp", 0) > 90: risk += 20
        if data.get("family_history_hypertension", False): risk += 15
        if data.get("alcohol_consumption", "") == "heavy": risk += 10
    
    elif disease == "Obesity":
        if float(data.get("bmi", 0)) > 30: risk += 30
        if data.get("physical_activity", "") == "sedentary": risk += 15
        if data.get("family_history_obesity", False): risk += 10
    
    elif disease == "Asthma":
        if data.get("family_history_asthma", False): risk += 20
        if data.get("smoking_status", "") == "current": risk += 15
        allergies = data.get("allergies", "").lower()
        if "pollen" in allergies or "dust" in allergies: risk += 10
    
    elif disease == "Depression":
        if data.get("family_history_depression", False): risk += 15
        if data.get("stress_level", 0) > 7: risk += 15
        if data.get("sleep_quality", "") == "poor": risk += 10
        mental_health = data.get("mental_health_history", "").lower()
        if "depression" in mental_health or "anxiety" in mental_health: risk += 20
    
    return min(risk, 100)

# Function to generate recommendations
def generate_recommendations(data):
    recommendations = []
    
    if data.get("physical_activity", "") in ["sedentary", "light"]:
        recommendations.append("Increase your daily physical activity to at least 30 minutes of moderate exercise.")
    
    if data.get("daily_fruit_veg_servings", 0) < 5:
        recommendations.append("Increase your daily intake of fruits and vegetables to at least 5 servings.")
    
    if data.get("daily_water_intake", 0) < 2000:
        recommendations.append("Increase your daily water intake to at least 2 liters (2000ml).")
    
    if data.get("sleep_duration", 0) < 7 or data.get("sleep_quality", "") in ["poor", "fair"]:
        recommendations.append("Aim for 7-9 hours of quality sleep per night to improve overall health.")
    
    if data.get("stress_level", 0) > 7:
        recommendations.append("Practice stress-reduction techniques such as meditation or deep breathing exercises.")
    
    if data.get("smoking_status", "") == "current":
        recommendations.append("Consider quitting smoking to significantly reduce your risk of heart disease and other health problems.")
    
    if data.get("alcohol_consumption", "") == "heavy":
        recommendations.append("Reduce alcohol consumption to moderate levels or consider abstaining completely.")
    
    if float(data.get("bmi", 0)) > 25:
        recommendations.append("Work on maintaining a healthy weight through a balanced diet and regular exercise.")
    
    if data.get("chronic_pain", "") != "none":
        recommendations.append("Consult with a healthcare professional about managing your chronic pain and consider physical therapy or pain management techniques.")
    
    if data.get("mental_health_history", "") != "":
        recommendations.append("Continue to prioritize your mental health. Consider regular check-ins with a mental health professional.")
    
    return recommendations

# Function to calculate results
def calculate_results():
    # Calculate BMI if not already done
    if "bmi" not in st.session_state.assessment_data:
        height_m = st.session_state.assessment_data.get("height", 170) / 100
        weight = st.session_state.assessment_data.get("weight", 70)
        bmi = weight / (height_m * height_m)
        st.session_state.assessment_data["bmi"] = round(bmi, 1)
    
    # Calculate risk for each disease
    diseases = ["Diabetes", "Heart Disease", "Hypertension", "Obesity", "Asthma", "Depression"]
    risk_data = []
    
    for disease in diseases:
        risk = calculate_risk(disease, st.session_state.assessment_data)
        risk_data.append({"disease": disease, "risk": risk})
    
    st.session_state.risk_data = risk_data
    
    # Calculate overall health score (scaled to 0-100)
    total_risk = sum(item["risk"] for item in risk_data)
    health_score = round(100 * (1 - total_risk / (len(diseases) * 100)))
    st.session_state.health_score = health_score
    
    # Generate recommendations
    st.session_state.recommendations = generate_recommendations(st.session_state.assessment_data)
    
    st.session_state.results_calculated = True

# Function to handle form submission for each step
def process_step(step_index):
    # Save form data to session state
    for field in steps[step_index]["fields"]:
        if field in st.session_state:
            st.session_state.assessment_data[field] = st.session_state[field]
    
    # Move to next step or calculate results
    if step_index < len(steps) - 1:
        st.session_state.current_step += 1
    else:
        calculate_results()

# Function to go back to previous step
def go_back():
    if st.session_state.current_step > 0:
        st.session_state.current_step -= 1

# Function to restart assessment
def restart_assessment():
    st.session_state.current_step = 0
    st.session_state.assessment_data = {}
    st.session_state.results_calculated = False
    st.session_state.risk_data = []
    st.session_state.health_score = 0
    st.session_state.recommendations = []

# Main function for the Health Score module
def show_health_score():
    st.title("🏥 Health Score Assessment")
    
    # Initialize session state
    init_session_state()
    
    # Display results if calculated
    if st.session_state.results_calculated:
        st.header("Your Health Assessment Results")
        
        # Create columns for layout
        col1, col2 = st.columns(2)
        
        with col1:
            # Bar chart for disease risks
            risk_df = pd.DataFrame(st.session_state.risk_data)
            fig = px.bar(
                risk_df, 
                x='disease', 
                y='risk',
                title='Disease Risk Assessment',
                labels={'disease': 'Disease', 'risk': 'Risk Score'},
                color='risk',
                color_continuous_scale=[(0, 'green'), (0.5, 'yellow'), (1, 'red')]
            )
            st.plotly_chart(fig, use_container_width=True)
            
        with col2:
            # Radar chart for disease risks
            fig = go.Figure()
            
            fig.add_trace(go.Scatterpolar(
                r=[item["risk"] for item in st.session_state.risk_data],
                theta=[item["disease"] for item in st.session_state.risk_data],
                fill='toself',
                name='Risk Profile'
            ))
            
            fig.update_layout(
                polar=dict(
                    radialaxis=dict(
                        visible=True,
                        range=[0, 100]
                    )
                ),
                title="Health Risk Radar"
            )
            
            st.plotly_chart(fig, use_container_width=True)
        
        # Health score gauge
        fig = go.Figure(go.Indicator(
            mode="gauge+number",
            value=st.session_state.health_score,
            domain={'x': [0, 1], 'y': [0, 1]},
            title={'text': "Overall Health Score"},
            gauge={
                'axis': {'range': [0, 100]},
                'bar': {'color': "darkblue"},
                'steps': [
                    {'range': [0, 30], 'color': "red"},
                    {'range': [30, 70], 'color': "yellow"},
                    {'range': [70, 100], 'color': "green"}
                ]
            }
        ))
        
        st.plotly_chart(fig, use_container_width=True)
        
        # Recommendations
        st.subheader("Recommendations")
        for i, recommendation in enumerate(st.session_state.recommendations):
            st.markdown(f"- {recommendation}")
        
        # Button to restart assessment
        if st.button("Retake Assessment"):
            restart_assessment()
    
    # Display assessment form if results not calculated
    else:
        current_step = st.session_state.current_step
        step = steps[current_step]
        
        st.header(f"Comprehensive Health Assessment")
        st.subheader(f"{step['title']} (Step {current_step + 1} of {len(steps)})")
        
        # Create a centered container with smaller width
        col1, form_col, col3 = st.columns([1, 2, 1])
        
        with form_col:
            with st.form(f"step_{current_step}_form"):
                # Basic Information
                if "age" in step["fields"]:
                    st.session_state.age = st.number_input("Age", 0, 120, st.session_state.assessment_data.get("age", 30))
                
                if "gender" in step["fields"]:
                    st.session_state.gender = st.selectbox("Gender", 
                                                     ["male", "female", "other"], 
                                                     ["male", "female", "other"].index(st.session_state.assessment_data.get("gender", "male")))
                
                if "height" in step["fields"]:
                    st.session_state.height = st.number_input("Height (cm)", 100, 250, st.session_state.assessment_data.get("height", 170))
                
                if "weight" in step["fields"]:
                    st.session_state.weight = st.number_input("Weight (kg)", 30, 300, st.session_state.assessment_data.get("weight", 70))
                
                # Lifestyle
                if "smoking_status" in step["fields"]:
                    st.session_state.smoking_status = st.selectbox("Smoking Status", 
                                                                 ["never", "former", "current"], 
                                                                 ["never", "former", "current"].index(st.session_state.assessment_data.get("smoking_status", "never")))
                
                if "alcohol_consumption" in step["fields"]:
                    st.session_state.alcohol_consumption = st.selectbox("Alcohol Consumption", 
                                                                      ["none", "moderate", "heavy"], 
                                                                      ["none", "moderate", "heavy"].index(st.session_state.assessment_data.get("alcohol_consumption", "moderate")))
                
                if "physical_activity" in step["fields"]:
                    st.session_state.physical_activity = st.selectbox("Physical Activity Level", 
                                                                    ["sedentary", "light", "moderate", "vigorous"], 
                                                                    ["sedentary", "light", "moderate", "vigorous"].index(st.session_state.assessment_data.get("physical_activity", "moderate")))
                
                if "diet_type" in step["fields"]:
                    st.session_state.diet_type = st.selectbox("Diet Type", 
                                                            ["balanced", "high-carb", "high-protein", "vegetarian", "vegan"], 
                                                            ["balanced", "high-carb", "high-protein", "vegetarian", "vegan"].index(st.session_state.assessment_data.get("diet_type", "balanced")))
                
                # Medical History
                if "family_history_diabetes" in step["fields"]:
                    st.session_state.family_history_diabetes = st.checkbox("Family History of Diabetes", st.session_state.assessment_data.get("family_history_diabetes", False))
                
                if "family_history_heart_disease" in step["fields"]:
                    st.session_state.family_history_heart_disease = st.checkbox("Family History of Heart Disease", st.session_state.assessment_data.get("family_history_heart_disease", False))
                
                if "family_history_hypertension" in step["fields"]:
                    st.session_state.family_history_hypertension = st.checkbox("Family History of Hypertension", st.session_state.assessment_data.get("family_history_hypertension", False))
                
                if "previous_diagnoses" in step["fields"]:
                    st.session_state.previous_diagnoses = st.text_input("Previous Diagnoses (comma separated)", st.session_state.assessment_data.get("previous_diagnoses", ""))
                
                # Vital Signs
                if "systolic_bp" in step["fields"]:
                    st.session_state.systolic_bp = st.number_input("Systolic Blood Pressure", 70, 220, st.session_state.assessment_data.get("systolic_bp", 120))
                
                if "diastolic_bp" in step["fields"]:
                    st.session_state.diastolic_bp = st.number_input("Diastolic Blood Pressure", 40, 130, st.session_state.assessment_data.get("diastolic_bp", 80))
                
                if "resting_heart_rate" in step["fields"]:
                    st.session_state.resting_heart_rate = st.number_input("Resting Heart Rate", 40, 120, st.session_state.assessment_data.get("resting_heart_rate", 70))
                
                # Mental Health
                if "stress_level" in step["fields"]:
                    st.session_state.stress_level = st.slider("Stress Level (1-10)", 1, 10, st.session_state.assessment_data.get("stress_level", 5))
                
                if "sleep_quality" in step["fields"]:
                    st.session_state.sleep_quality = st.selectbox("Sleep Quality", 
                                                                ["poor", "fair", "good", "excellent"], 
                                                                ["poor", "fair", "good", "excellent"].index(st.session_state.assessment_data.get("sleep_quality", "good")))
                
                if "sleep_duration" in step["fields"]:
                    st.session_state.sleep_duration = st.number_input("Sleep Duration (hours)", 3.0, 12.0, float(st.session_state.assessment_data.get("sleep_duration", 7.0)), 0.5)
                
                # Nutrition
                if "daily_water_intake" in step["fields"]:
                    st.session_state.daily_water_intake = st.number_input("Daily Water Intake (ml)", 0, 5000, st.session_state.assessment_data.get("daily_water_intake", 2000), 100)
                
                if "daily_fruit_veg_servings" in step["fields"]:
                    st.session_state.daily_fruit_veg_servings = st.number_input("Daily Fruit & Vegetable Servings", 0, 10, st.session_state.assessment_data.get("daily_fruit_veg_servings", 3))
                
                # Additional Metrics
                if "waist_circumference" in step["fields"]:
                    st.session_state.waist_circumference = st.number_input("Waist Circumference (cm)", 50, 200, st.session_state.assessment_data.get("waist_circumference", 80))
                
                if "body_fat_percentage" in step["fields"]:
                    st.session_state.body_fat_percentage = st.number_input("Body Fat Percentage", 5.0, 50.0, float(st.session_state.assessment_data.get("body_fat_percentage", 20.0)), 0.5)
                
                # Additional Health Information
                if "family_history_asthma" in step["fields"]:
                    st.session_state.family_history_asthma = st.checkbox("Family History of Asthma", st.session_state.assessment_data.get("family_history_asthma", False))
                
                if "family_history_obesity" in step["fields"]:
                    st.session_state.family_history_obesity = st.checkbox("Family History of Obesity", st.session_state.assessment_data.get("family_history_obesity", False))
                
                if "family_history_depression" in step["fields"]:
                    st.session_state.family_history_depression = st.checkbox("Family History of Depression", st.session_state.assessment_data.get("family_history_depression", False))
                
                if "allergies" in step["fields"]:
                    st.session_state.allergies = st.text_input("Allergies (comma separated)", st.session_state.assessment_data.get("allergies", ""))
                
                if "chronic_pain" in step["fields"]:
                    st.session_state.chronic_pain = st.selectbox("Chronic Pain Level", 
                                                               ["none", "mild", "moderate", "severe"], 
                                                               ["none", "mild", "moderate", "severe"].index(st.session_state.assessment_data.get("chronic_pain", "none")))
                
                if "mental_health_history" in step["fields"]:
                    st.session_state.mental_health_history = st.text_area("Mental Health History", st.session_state.assessment_data.get("mental_health_history", ""))
                
                # Form buttons
                col1, col2 = st.columns(2)
                with col1:
                    if current_step > 0:
                        back_button = st.form_submit_button("Back")
                        if back_button:
                            go_back()
                
                with col2:
                    if current_step < len(steps) - 1:
                        next_button = st.form_submit_button("Next")
                        if next_button:
                            process_step(current_step)
                    else:
                        submit_button = st.form_submit_button("Submit Assessment")
                        if submit_button:
                            process_step(current_step)