app

app.py

@@ -14,12 +14,37 @@ def load_model(path):
         raise ValueError(f"Error loading model: {e}")
 
 # Define the prediction function
-def predict_with_model(State: float, Sex: float, GeneralHealth: float, PhysicalHealthDays: float, MentalHealthDays: float, LastCheckupTime: float, PhysicalActivities: float, SleepHours: float, HadStroke: float, HadArthritis: float, HadDiabetes: float, SmokerStatus: float, ECigaretteUsage: float, RaceEthnicityCategory: float, AgeCategory: float, HeightInMeters: float, WeightInKilograms: float, BMI: float, AlcoholDrinkers: float, HighRiskLastYear: float):
+def predict_heart_disease(
+    PhysicalHealthDays: float,
+    MentalHealthDays: float,
+    SleepHours: float,
+    BMI: float,
+    PhysicalActivities: str,
+    AlcoholDrinkers: str,
+    HIVTesting: str,
+    RemovedTeeth: str,
+    HighRiskLastYear: str,
+    CovidPos: str,
+):
     try:
         model = load_model(model_path)
-        # Combine inputs into a DataFrame for prediction
-        input_data = pd.DataFrame([[State, Sex, GeneralHealth, PhysicalHealthDays, MentalHealthDays, LastCheckupTime, PhysicalActivities, SleepHours, HadStroke, HadArthritis, HadDiabetes, SmokerStatus, ECigaretteUsage, RaceEthnicityCategory, AgeCategory, HeightInMeters, WeightInKilograms, BMI, AlcoholDrinkers, HighRiskLastYear]], columns=['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear'])
-        prediction = model.predict(input_data)
+        # Encode categorical inputs as integers
+        physical_activities = 1 if PhysicalActivities.lower() == "yes" else 0
+        alcohol_drinkers = 1 if AlcoholDrinkers.lower() == "yes" else 0
+        hiv_testing = 1 if HIVTesting.lower() == "yes" else 0
+        removed_teeth = 1 if RemovedTeeth.lower() == "yes" else 0
+        high_risk_last_year = 1 if HighRiskLastYear.lower() == "yes" else 0
+        covid_pos = 1 if CovidPos.lower() == "yes" else 0
+
+        # Combine inputs into a numpy array for prediction
+        features = np.array([
+            PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
+            physical_activities, alcohol_drinkers, hiv_testing,
+            removed_teeth, high_risk_last_year, covid_pos
+        ]).reshape(1, -1)
+
+        # Predict with the model
+        prediction = model.predict(features)
         return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
     except Exception as e:
         return f"Error during prediction: {e}"
@@ -27,19 +52,33 @@ def predict_with_model(State: float, Sex: float, GeneralHealth: float, PhysicalH
 # Define the Gradio interface
 with gr.Blocks() as app:
     gr.Markdown("# Heart Disease Prediction App")
-    gr.Markdown("### Provide input values for the features below and get a prediction.")
-
-    input_sliders = []
-    for feature in ['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear']:
-        input_sliders.append(gr.Slider(0, 100, step=1, label=feature))
+    gr.Markdown("### Provide input values and receive a prediction.")
+    
+    with gr.Row():
+        PhysicalHealthDays = gr.Slider(0, 30, label="Physical Health Days")
+        MentalHealthDays = gr.Slider(0, 30, label="Mental Health Days")
+        SleepHours = gr.Slider(0, 24, label="Average Sleep Hours")
+        BMI = gr.Slider(10, 50, label="Body Mass Index (BMI)")
+    
+    with gr.Row():
+        PhysicalActivities = gr.Radio(["Yes", "No"], label="Engaged in Physical Activities?")
+        AlcoholDrinkers = gr.Radio(["Yes", "No"], label="Consumes Alcohol?")
+        HIVTesting = gr.Radio(["Yes", "No"], label="Tested for HIV?")
+        RemovedTeeth = gr.Radio(["Yes", "No"], label="Has Removed Teeth?")
+        HighRiskLastYear = gr.Radio(["Yes", "No"], label="High Risk Last Year?")
+        CovidPos = gr.Radio(["Yes", "No"], label="Tested Positive for COVID-19?")
 
     predict_button = gr.Button("Predict")
     output = gr.Textbox(label="Prediction Result")
 
     # Connect prediction logic
     predict_button.click(
-        fn=predict_with_model,
-        inputs=input_sliders,
+        fn=predict_heart_disease,
+        inputs=[
+            PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
+            PhysicalActivities, AlcoholDrinkers, HIVTesting,
+            RemovedTeeth, HighRiskLastYear, CovidPos
+        ],
         outputs=output,
     )
 

app.py.bak

@@ -1,86 +0,0 @@
-
-import gradio as gr
-import joblib
-import numpy as np
-import pandas as pd
-
-# Load the trained model
-model_path = "trained_model.pkl"
-
-def load_model(path):
-    try:
-        return joblib.load(path)
-    except Exception as e:
-        raise ValueError(f"Error loading model: {e}")
-
-# Define the prediction function
-def predict_heart_disease(
-    PhysicalHealthDays: float,
-    MentalHealthDays: float,
-    SleepHours: float,
-    BMI: float,
-    PhysicalActivities: str,
-    AlcoholDrinkers: str,
-    HIVTesting: str,
-    RemovedTeeth: str,
-    HighRiskLastYear: str,
-    CovidPos: str,
-):
-    try:
-        model = load_model(model_path)
-        # Encode categorical inputs as integers
-        physical_activities = 1 if PhysicalActivities.lower() == "yes" else 0
-        alcohol_drinkers = 1 if AlcoholDrinkers.lower() == "yes" else 0
-        hiv_testing = 1 if HIVTesting.lower() == "yes" else 0
-        removed_teeth = 1 if RemovedTeeth.lower() == "yes" else 0
-        high_risk_last_year = 1 if HighRiskLastYear.lower() == "yes" else 0
-        covid_pos = 1 if CovidPos.lower() == "yes" else 0
-
-        # Combine inputs into a numpy array for prediction
-        features = np.array([
-            PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
-            physical_activities, alcohol_drinkers, hiv_testing,
-            removed_teeth, high_risk_last_year, covid_pos
-        ]).reshape(1, -1)
-
-        # Predict with the model
-        prediction = model.predict(features)
-        return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
-    except Exception as e:
-        return f"Error during prediction: {e}"
-
-# Define the Gradio interface
-with gr.Blocks() as app:
-    gr.Markdown("# Heart Disease Prediction App")
-    gr.Markdown("### Provide input values and receive a prediction.")
-    
-    with gr.Row():
-        PhysicalHealthDays = gr.Slider(0, 30, label="Physical Health Days")
-        MentalHealthDays = gr.Slider(0, 30, label="Mental Health Days")
-        SleepHours = gr.Slider(0, 24, label="Average Sleep Hours")
-        BMI = gr.Slider(10, 50, label="Body Mass Index (BMI)")
-    
-    with gr.Row():
-        PhysicalActivities = gr.Radio(["Yes", "No"], label="Engaged in Physical Activities?")
-        AlcoholDrinkers = gr.Radio(["Yes", "No"], label="Consumes Alcohol?")
-        HIVTesting = gr.Radio(["Yes", "No"], label="Tested for HIV?")
-        RemovedTeeth = gr.Radio(["Yes", "No"], label="Has Removed Teeth?")
-        HighRiskLastYear = gr.Radio(["Yes", "No"], label="High Risk Last Year?")
-        CovidPos = gr.Radio(["Yes", "No"], label="Tested Positive for COVID-19?")
-
-    predict_button = gr.Button("Predict")
-    output = gr.Textbox(label="Prediction Result")
-
-    # Connect prediction logic
-    predict_button.click(
-        fn=predict_heart_disease,
-        inputs=[
-            PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
-            PhysicalActivities, AlcoholDrinkers, HIVTesting,
-            RemovedTeeth, HighRiskLastYear, CovidPos
-        ],
-        outputs=output,
-    )
-
-# Launch the app
-app.launch()