Update app.py

app.py

@@ -0,0 +1,45 @@
+import pickle
+import gradio as gr
+import numpy as np
+
+# Load the trained model
+model = pickle.load(open('/content/heart_disease_model.sav', 'rb'))
+
+# Prediction function
+def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
+    input_data = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]])
+    prediction = model.predict(input_data)
+    
+    if prediction[0] == 1:
+        return "The person has heart disease."
+    else:
+        return "The person does not have heart disease."
+
+# Gradio Interface
+def heart_disease_interface():
+    with gr.Blocks() as app:
+        gr.Markdown("### Heart Disease Prediction System")
+        age = gr.Number(label="Age")
+        sex = gr.Number(label="Sex")
+        cp = gr.Number(label="Chest Pain Type")
+        trestbps = gr.Number(label="Resting Blood Pressure")
+        chol = gr.Number(label="Serum Cholesterol")
+        fbs = gr.Number(label="Fasting Blood Sugar > 120 mg/dl")
+        restecg = gr.Number(label="Resting ECG Results")
+        thalach = gr.Number(label="Max Heart Rate Achieved")
+        exang = gr.Number(label="Exercise Induced Angina")
+        oldpeak = gr.Number(label="ST Depression by Exercise")
+        slope = gr.Number(label="Slope of Peak Exercise ST Segment")
+        ca = gr.Number(label="Major Vessels Colored by Fluoroscopy")
+        thal = gr.Number(label="Thalassemia Type")
+        submit_btn = gr.Button("Predict")
+        output = gr.Textbox()
+        submit_btn.click(
+            predict_heart_disease, 
+            inputs=[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal], 
+            outputs=output
+        )
+    return app
+
+# Launch the Gradio Interface
+heart_disease_interface().launch()