app

app.py

@@ -1,9 +1,17 @@
+
 import gradio as gr
 import joblib
 import numpy as np
+import pandas as pd
 
 # Load the trained model
-model = joblib.load("trained_model.pkl")
+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(
@@ -19,60 +27,60 @@ def predict_heart_disease(
     CovidPos: str,
 ):
     try:
+        model = load_model(model_path)
         # Encode categorical inputs as integers
-        physical_activities = 1 if PhysicalActivities == "Yes" else 0
-        alcohol_drinkers = 1 if AlcoholDrinkers == "Yes" else 0
-        hiv_testing = 1 if HIVTesting == "Yes" else 0
-        removed_teeth = 1 if RemovedTeeth == "Yes" else 0
-        high_risk_last_year = 1 if HighRiskLastYear == "Yes" else 0
-        covid_pos = 1 if CovidPos == "Yes" else 0
+        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
 
-        # Prepare features as a numpy array
-        features = np.array([[
-            PhysicalHealthDays,
-            MentalHealthDays,
-            SleepHours,
-            BMI,
-            physical_activities,
-            alcohol_drinkers,
-            hiv_testing,
-            removed_teeth,
-            high_risk_last_year,
-            covid_pos,
-        ]])
+        # 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)
 
-        # Make prediction
+        # Predict with the model
         prediction = model.predict(features)
-        result = "Yes" if prediction[0] == 1 else "No"
-        return f"Heart Disease Prediction: {result}"
+        return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
     except Exception as e:
-        return f"Error in prediction: {str(e)}"
+        return f"Error during prediction: {e}"
 
 # Define the Gradio interface
-inputs = [
-    gr.inputs.Number(label="Physical Health Days"),
-    gr.inputs.Number(label="Mental Health Days"),
-    gr.inputs.Number(label="Sleep Hours"),
-    gr.inputs.Number(label="BMI"),
-    gr.inputs.Radio(["Yes", "No"], label="Physical Activities"),
-    gr.inputs.Radio(["Yes", "No"], label="Alcohol Drinkers"),
-    gr.inputs.Radio(["Yes", "No"], label="HIV Testing"),
-    gr.inputs.Radio(["Yes", "No"], label="Removed Teeth"),
-    gr.inputs.Radio(["Yes", "No"], label="High Risk Last Year"),
-    gr.inputs.Radio(["Yes", "No"], label="Covid Positive"),
-]
+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?")
 
-outputs = gr.outputs.Textbox(label="Heart Disease Prediction")
+    predict_button = gr.Button("Predict")
+    output = gr.Textbox(label="Prediction Result")
 
-# Create the Gradio app
-app = gr.Interface(
-    fn=predict_heart_disease,
-    inputs=inputs,
-    outputs=outputs,
-    title="Heart Disease Prediction App",
-    description="Please enter health metrics to predict the risk of heart disease."
-)
+    # 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
-if __name__ == "__main__":
-    app.launch()
+app.launch()