Update app.py

app.py

@@ -1,47 +1,13 @@
-# Import required libraries
 import streamlit as st
 import pandas as pd
 import numpy as np
-from ucimlrepo import fetch_ucirepo
 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
 
-# Fetch the diabetes dataset (ID 891)
-cdc_diabetes_health_indicators = fetch_ucirepo(id=891)
-
-# Extract features and targets
-X_raw = cdc_diabetes_health_indicators.data.features
-y = cdc_diabetes_health_indicators.data.targets
-
-# Combine features and targets into a single DataFrame
-df = pd.concat([X_raw, y], axis=1)
-
-# Drop duplicates if there are any
-df.drop_duplicates(inplace=True)
-
-# Features and target selection
-X_raw = df[['AnyHealthcare', 'Sex', 'Smoker', 'MentHlth', 'CholCheck', 'Stroke',
-            'PhysHlth', 'HeartDiseaseorAttack', 'Age', 'HighChol', 'DiffWalk',
-            'BMI', 'HighBP', 'GenHlth']]
-y = df['Diabetes_binary']
-
-# Data Preprocessing
-scaler = StandardScaler()
-X = scaler.fit_transform(X_raw)
-
-# Train-test split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=123)
-
-# Convert to PyTorch tensors
-X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
-X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
-y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32).view(-1, 1)
-y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32).view(-1, 1)
-
-# Define the PyTorch model
+# Neural network definition (same as before)
 class NeuralNet(nn.Module):
     def __init__(self, input_size, hidden_size, output_size):
         super(NeuralNet, self).__init__()
@@ -57,97 +23,123 @@ class NeuralNet(nn.Module):
         x = self.sigmoid(self.fc3(x))
         return x
 
-# Instantiate PyTorch model
-input_size = X_train.shape[1]
+# Instantiate the model
+input_size = 13  
 hidden_size = 64
 output_size = 1
 deep_model = NeuralNet(input_size, hidden_size, output_size)
+scaler = StandardScaler()
 
-# Define loss and optimizer
-criterion = nn.BCELoss()
-optimizer = optim.Adam(deep_model.parameters(), lr=0.001)
-
-# Title and description in Streamlit
-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. 
-Berto honors the legacy of one of our founder's dad, Roberto Ferrer, who unfortunately succumbed to Type 2 Diabetes.
-This application uses a deep learning model powered by PyTorch to predict whether you may have diabetes based on various health indicators. 
-We aim to provide insights and suggestions for personalizing diabetes treatment. The Application allows you to choose between two options, Yes and No, 
-which have been given binary values. There is also a slider for various features, which have ranges as indicated. Berto is part of DiabeTrek Health's 
-mission to help manage, prevent, and provide personalized treatment to Type 2 Diabetic Patients.
-""")
-
-# Input form for user to enter health information with descriptions
-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)
-
-# Get height and weight input
-height_cm = st.slider("Height (in cm)", 100, 250, 170)
-weight_kg = st.slider("Weight (in kg)", 30, 200, 70)
-
-# Calculate BMI
-BMI = weight_kg / (height_cm / 100) ** 2
-st.write(f"Your BMI is: {BMI:.2f}")
-
-# Create a feature array from the inputs, including the calculated BMI
-user_input = np.array([[AnyHealthcare, Sex, Smoker, MentHlth, CholCheck, Stroke,
-                        PhysHlth, HeartDiseaseorAttack, Age, HighChol, DiffWalk,
-                        BMI, HighBP, GenHlth]])
-
-# Convert the NumPy array to a DataFrame with the same column names used in the scaler
-user_input_df = pd.DataFrame(user_input, columns=X_raw.columns)
-
-# Standardize the user input using the same scaler
-user_input_scaled = scaler.transform(user_input_df)
-
-# Convert user input to PyTorch tensor
-user_input_tensor = torch.tensor(user_input_scaled, dtype=torch.float32)
-
-# Perform prediction when the user clicks "Predict"
-if st.button("Predict"):
-    with torch.no_grad():
-        deep_model.eval()  # Set model to evaluation mode
-        prediction = deep_model(user_input_tensor).round().numpy()
-
-    # Display prediction result
-    if prediction == 1:
-        st.success("The model predicts that you likely **have diabetes**.")
-        
-        # Provide tips for managing diabetes
-        st.subheader("Tips for Managing Diabetes")
-        st.markdown("""
-        - **Maintain a balanced diet**: Focus on eating whole grains, vegetables, lean protein, and healthy fats.
-        - **Exercise regularly**: Physical activity helps control your blood sugar.
-        - **Monitor blood sugar levels**: Regular monitoring helps you keep track of your glucose levels.
-        - **Take medications as prescribed**: Follow your doctor's instructions regarding medications.
-        - **Manage stress**: Chronic stress can raise blood sugar levels, so practice relaxation techniques.
-        - **Get regular check-ups**: Regular health check-ups are crucial for managing diabetes effectively.
-        """)
+# 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:
-        st.success("✅ Lower risk detected. Keep up the good work!")
+        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.
         
-        # Provide tips for maintaining low diabetes risk
-        st.subheader("Tips to Maintain Low Diabetes Risk")
-        st.markdown("""
-        - **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**
+        Adjust this routine as needed based on medical advice.
         """)