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Update app.py
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app.py
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@@ -67,17 +67,15 @@ deep_model = NeuralNet(input_size, hidden_size, output_size)
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criterion = nn.BCELoss()
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optimizer = optim.Adam(deep_model.parameters(), lr=0.001)
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# Skip training loop here for real-time prediction
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# Title and description in Streamlit
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st.title("BERTO AI😊 : Personalized Diabetes Treatment Predictor")
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st.write("""
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BERTO
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of one of our
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This application uses a deep learning model powered by PyTorch to predict whether you may have diabetes based on various health indicators.
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We aim to provide insights and suggestions for personalizing diabetes treatment. The Application allows you to choose between two options
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which have been
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mission to help manage ,
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""")
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# Input form for user to enter health information with descriptions
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@@ -94,11 +92,18 @@ HeartDiseaseorAttack = st.selectbox("History of Heart Disease or Attack (1: Yes,
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Age = st.slider("Age", 18, 100, 30)
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HighChol = st.selectbox("High Cholesterol (1: Yes, 0: No)", [0, 1])
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DiffWalk = st.selectbox("Difficulty Walking (1: Yes, 0: No)", [0, 1])
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BMI = st.slider("BMI (Body Mass Index)", 10.0, 50.0, 25.0)
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HighBP = st.selectbox("High Blood Pressure (1: Yes, 0: No)", [0, 1])
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GenHlth = st.slider("General Health (1=Excellent, 5=Poor)", 1, 5, 3)
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#
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user_input = np.array([[AnyHealthcare, Sex, Smoker, MentHlth, CholCheck, Stroke,
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PhysHlth, HeartDiseaseorAttack, Age, HighChol, DiffWalk,
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BMI, HighBP, GenHlth]])
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@@ -120,6 +125,29 @@ if st.button("Predict"):
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# Display prediction result
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if prediction == 1:
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st.success("The model predicts that you likely **
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else:
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st.success("
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criterion = nn.BCELoss()
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optimizer = optim.Adam(deep_model.parameters(), lr=0.001)
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# Title and description in Streamlit
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st.title("BERTO AI😊 : Personalized Diabetes Treatment Predictor")
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st.write("""
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BERTO AI is a personalized diabetes prediction tool built to assist users in better understanding their health risks.
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Berto honors the legacy of one of our founder's dad, Roberto Ferrer, who unfortunately succumbed to Type 2 Diabetes.
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This application uses a deep learning model powered by PyTorch to predict whether you may have diabetes based on various health indicators.
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We aim to provide insights and suggestions for personalizing diabetes treatment. The Application allows you to choose between two options, Yes and No,
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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
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mission to help manage, prevent, and provide personalized treatment to Type 2 Diabetic Patients.
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""")
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# Input form for user to enter health information with descriptions
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Age = st.slider("Age", 18, 100, 30)
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HighChol = st.selectbox("High Cholesterol (1: Yes, 0: No)", [0, 1])
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DiffWalk = st.selectbox("Difficulty Walking (1: Yes, 0: No)", [0, 1])
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HighBP = st.selectbox("High Blood Pressure (1: Yes, 0: No)", [0, 1])
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GenHlth = st.slider("General Health (1=Excellent, 5=Poor)", 1, 5, 3)
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# Get height and weight input
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height_cm = st.slider("Height (in cm)", 100, 250, 170)
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weight_kg = st.slider("Weight (in kg)", 30, 200, 70)
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# Calculate BMI
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BMI = weight_kg / (height_cm / 100) ** 2
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st.write(f"Your BMI is: {BMI:.2f}")
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# Create a feature array from the inputs, including the calculated BMI
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user_input = np.array([[AnyHealthcare, Sex, Smoker, MentHlth, CholCheck, Stroke,
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PhysHlth, HeartDiseaseorAttack, Age, HighChol, DiffWalk,
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BMI, HighBP, GenHlth]])
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# Display prediction result
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if prediction == 1:
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st.success("The model predicts that you likely **have diabetes**.")
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# Provide tips for managing diabetes
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st.subheader("Tips for Managing Diabetes")
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st.markdown("""
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- **Maintain a balanced diet**: Focus on eating whole grains, vegetables, lean protein, and healthy fats.
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- **Exercise regularly**: Physical activity helps control your blood sugar.
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- **Monitor blood sugar levels**: Regular monitoring helps you keep track of your glucose levels.
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- **Take medications as prescribed**: Follow your doctor's instructions regarding medications.
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- **Manage stress**: Chronic stress can raise blood sugar levels, so practice relaxation techniques.
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- **Get regular check-ups**: Regular health check-ups are crucial for managing diabetes effectively.
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""")
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else:
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st.success("✅ Lower risk detected. Keep up the good work!")
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# Provide tips for maintaining low diabetes risk
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st.subheader("Tips to Maintain Low Diabetes Risk")
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st.markdown("""
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- **Keep a healthy weight**
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- **Exercise often**
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- **Eat well-balanced meals**
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- **Cut down on sugar and unhealthy fats**
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- **Check your blood sugar**
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- **Manage stress**
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- **Get regular check-ups**
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""")
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