app

app.py

@@ -1,41 +1,78 @@
 import gradio as gr
-import pickle
-import pandas as pd
+import joblib
+import numpy as np
 
 # Load the trained model
-model_path = "tuned_model.pkl"
+model = joblib.load("trained_model.pkl")
 
-def load_model():
-    """Load the model from the pickle file."""
-    with open(model_path, "rb") as file:
-        return pickle.load(file)
-
-# Prediction function
-def predict_with_model(*inputs):
+# 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()  # Load the model dynamically
-        # Create a DataFrame for prediction
-        input_data = pd.DataFrame([inputs], columns=features)
+        # 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
+
+        # 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,
+        ]])
+
         # Make prediction
-        prediction = model.predict(input_data)
-        return f"Prediction: {'Risk of Heart Failure' if prediction[0] == 1 else 'No Risk'}"
+        prediction = model.predict(features)
+        result = "Yes" if prediction[0] == 1 else "No"
+        return f"Heart Disease Prediction: {result}"
     except Exception as e:
-        return f"Error during prediction: {str(e)}"
+        return f"Error in prediction: {str(e)}"
 
-# Features derived from the CSV file
-features = ["Feature1", "Feature2", "Feature3"]  # Replace with actual feature names
+# 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"),
+]
 
-# Create input sliders
-input_sliders = [gr.Slider(0, 100, label=feature) for feature in features]
+outputs = gr.outputs.Textbox(label="Heart Disease Prediction")
 
-# Define Gradio interface
-iface = gr.Interface(
-    fn=predict_with_model,
-    inputs=input_sliders,
-    outputs="text",
-    title="Heart Failure Prediction App",
-    description="Adjust the sliders to simulate feature values and predict heart failure risk.",
+# 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."
 )
 
 # Launch the app
-iface.launch()
+if __name__ == "__main__":
+    app.launch()