EdgarDataScientist commited on
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39ab335
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1 Parent(s): 63e0290

Update app.py

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Files changed (1) hide show
  1. app.py +39 -11
app.py CHANGED
@@ -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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-
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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. Berto honors the legacy
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- of one of our founders 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 give binary values .Also there is a slider for various features which have ranges as indicated . Berto is part of DiabeTrek Health
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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
@@ -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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- # Create a feature array from the inputs
 
 
 
 
 
 
 
 
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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]])
@@ -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 ** have diabetes**.")
 
 
 
 
 
 
 
 
 
 
 
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  else:
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- st.success("The model predicts that you are ** unlikely to get diabetes**.")
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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+
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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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+
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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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+
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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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+ """)