Update app.py

app.py

@@ -29,7 +29,7 @@ loaded_scaler = loaded_components['scaler']
 
 # Data Fields
 data_fields = {
-    "PRG": "Number of pregnancies (applicable only to females)",
+    "PRG": "Represents the number of pregnancies, applicable only to females",
     "PL": "Plasma glucose concentration (mg/dL)",
     "PR": "Diastolic blood pressure (mm Hg)",
     "SK": "Triceps skinfold thickness (mm)",
@@ -37,7 +37,15 @@ data_fields = {
     "M11": "Body mass index (BMI) (weight in kg / {(height in m)}^2)",
     "BD2": "Diabetes pedigree function (mu U/ml)",
     "Age": "Age of the patient (years)"
-}
+
+"PRG": "Number of Pregnancies (applicable only to females)\n   - Description: The total number of pregnancies a female patient has experienced.",
+"PL": "Plasma Glucose Concentration (mg/dL)\n   - The concentration of glucose in the patient's blood). It provides insights into the patient's blood sugar levels.",
+"PR": "Diastolic Blood Pressure (mm Hg)\n   - The diastolic blood pressure, representing the pressure in the arteries when the heart is at rest between beats.",
+"SK": "Triceps Skinfold Thickness (mm)\n   - The thickness of the skinfold on the triceps, measured in millimeters (mm). This measurement is often used to assess body fat percentage.",
+"TS": "2-hour Serum Insulin (mu U/ml)\n   - The level of insulin in the patient's blood two hours after a meal, measured in micro international units per milliliter (mu U/ml).",
+"M11": "Body Mass Index (BMI) (weight in kg / {(height in m)}^2)\n   - It provides a standardized measure that helps assess the degree of body fat and categorizes individuals into different weight status categories, such as underweight, normal weight, overweight, and obesity."
+"BD2": "Diabetes Pedigree Function (mu U/ml)\n   - This function provides information about the patient's family history of diabetes.",
+"Age": "Age of the Patient (years)\n   - Age is an essential factor in medical assessments and can influence various health outcomes."
 
 # Organize input fields into two columns
 col1, col2 = st.columns(2)