app.py

import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import torch
import torch.nn as nn
import torch.optim as optim

# Neural network definition (same as before)
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.sigmoid(self.fc3(x))
        return x

# Instantiate the model
input_size = 13  
hidden_size = 64
output_size = 1
deep_model = NeuralNet(input_size, hidden_size, output_size)
scaler = StandardScaler()

# Sidebar for navigation
st.sidebar.title("Navigation")
app_mode = st.sidebar.selectbox("Choose the app mode", ["Diabetes Predictor", "Chronotherapy Scheduler"])

# Diabetes Predictor Section
if app_mode == "Diabetes Predictor":
    st.title("BERTO AI: Personalized Diabetes Treatment Predictor")
    st.write("""
    Berto AI is a personalized diabetes prediction tool built to assist users in better understanding their health risks. 
    This tool uses a deep learning model powered by PyTorch to predict whether you may have diabetes based on various health indicators.
    """)

    # Input form for user health information
    st.subheader("Enter Your Health Information")
    
    AnyHealthcare = st.selectbox("Any Healthcare (1: Yes, 0: No)", [0, 1])
    Sex = st.selectbox("Sex (1: Male, 0: Female)", [0, 1])
    Smoker = st.selectbox("Smoker (1: Yes, 0: No)", [0, 1])
    MentHlth = st.slider("Mental Health (Bad days in last 30 days)", 0, 30, 0)
    CholCheck = st.selectbox("Cholesterol Check in Last 5 Years (1: Yes, 0: No)", [0, 1])
    Stroke = st.selectbox("History of Stroke (1: Yes, 0: No)", [0, 1])
    PhysHlth = st.slider("Physical Health (Bad days in last 30 days)", 0, 30, 0)
    HeartDiseaseorAttack = st.selectbox("History of Heart Disease or Attack (1: Yes, 0: No)", [0, 1])
    Age = st.slider("Age", 18, 100, 30)
    HighChol = st.selectbox("High Cholesterol (1: Yes, 0: No)", [0, 1])
    DiffWalk = st.selectbox("Difficulty Walking (1: Yes, 0: No)", [0, 1])
    HighBP = st.selectbox("High Blood Pressure (1: Yes, 0: No)", [0, 1])
    GenHlth = st.slider("General Health (1=Excellent, 5=Poor)", 1, 5, 3)

    # Create a feature array from the inputs
    user_input = np.array([[AnyHealthcare, Sex, Smoker, MentHlth, CholCheck, Stroke,
                            PhysHlth, HeartDiseaseorAttack, Age, HighChol, DiffWalk,
                            HighBP, GenHlth]])

    # Convert the NumPy array to a DataFrame
    user_input_df = pd.DataFrame(user_input, columns=[
        'AnyHealthcare', 'Sex', 'Smoker', 'MentHlth', 'CholCheck', 'Stroke', 'PhysHlth',
        'HeartDiseaseorAttack', 'Age', 'HighChol', 'DiffWalk', 'HighBP', 'GenHlth'])

    # Standardize the user input
    user_input_scaled = scaler.fit_transform(user_input_df)  # Use pre-trained scaler in practice
    user_input_tensor = torch.tensor(user_input_scaled, dtype=torch.float32)

    # Perform prediction
    if st.button("Predict"):
        with torch.no_grad():
            deep_model.eval()  # Set model to evaluation mode
            prediction = deep_model(user_input_tensor).round().numpy()

        if prediction == 1:
            st.success("The model predicts that you likely **have diabetes**.")
            st.warning("Tips to Manage Diabetes:\n\n- Manage blood sugar\n- Eat healthy meals\n- Exercise regularly\n- Monitor your blood pressure\n- Get enough sleep.")
        else:
            st.success("✅ Lower risk detected. Keep up the good work!")
            st.info("""
            Tips to Maintain Low Diabetes Risk:
            - Keep a healthy weight
            - Exercise often
            - Eat well-balanced meals
            - Cut down on sugar and unhealthy fats
            - Check your blood sugar
            - Manage stress
            - Get regular check-ups
            """)

# Chronotherapy Scheduler Section
elif app_mode == "Chronotherapy Scheduler":
    st.title("Chronotherapy Scheduler for Type 2 Diabetes")
    st.write("""
    This scheduler generates a personalized routine for Type 2 Diabetes management based on your inputs.
    Chronotherapy involves timing medical treatment and lifestyle interventions to better align with your body's natural rhythms.
    """)

    # Input fields
    city = st.text_input("Enter your city name:")
    a1c_level = st.slider("A1C level (%)", 4.0, 14.0, 7.0)
    
    weight_unit = st.radio("Weight unit", ["kg", "lbs"])
    if weight_unit == "kg":
        weight = st.number_input("Weight (kg)", min_value=30.0, max_value=200.0, value=70.0)
    else:
        weight = st.number_input("Weight (lbs)", min_value=66.0, max_value=440.0, value=154.0)

    height_unit = st.radio("Height unit", ["cm", "feet & inches"])
    if height_unit == "cm":
        height_cm = st.number_input("Height (cm)", min_value=100.0, max_value=250.0, value=170.0)
    else:
        height_ft = st.number_input("Height (feet)", min_value=3, max_value=8, value=5)
        height_in = st.number_input("Height (inches)", min_value=0, max_value=11, value=7)
        height_cm = height_ft * 30.48 + height_in * 2.54

    glucose_level = st.number_input("Glucose level (mg/dL)", min_value=50.0, max_value=600.0, value=70.0)
    age = st.slider("Age", min_value=18, max_value=100, value=45)
    sex = st.radio("Sex", ["M", "F"])
    smoking_status = st.radio("Do you smoke?", ["No", "Yes"])
    diabetes_status = st.radio("Diabetes Status", ["Prediabetic", "Type 2 Diabetic"])

    # Generate routine based on input
    if st.button("Generate Schedule"):
        st.subheader(f"Chronotherapy Routine for {city}")
        st.write(f"""
        Based on your inputs, here's a suggested routine for managing Type 2 Diabetes:
        
        - **Morning (7:00 AM)**: Wake up and take your morning medication (if prescribed).
        - **Breakfast (7:30 AM)**: A balanced meal with low sugar and carbs.
        - **Mid-morning (10:00 AM)**: Light physical activity or a short walk.
        - **Lunch (12:30 PM)**: A well-balanced meal with lean protein and fiber.
        - **Afternoon (3:00 PM)**: Check glucose levels if needed, and have a light snack.
        - **Evening (6:00 PM)**: Dinner with a focus on vegetables and healthy fats.
        - **Before bed (10:00 PM)**: Wind down and manage stress with relaxation techniques.
        
        Adjust this routine as needed based on medical advice.
        """)