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. """)