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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. | |
""") | |