Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pickle
+
+# Load the trained model
+with open('heart_disease_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Define the prediction function
+def predict_heart_disease(male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, BMI):
+    male = 1 if male == 'Male' else 0
+    currentSmoker = 1 if currentSmoker == "Yes" else 0
+    BPMeds = 1 if BPMeds == "Yes" else 0
+    prevalentStroke = 1 if prevalentStroke == "Yes" else 0
+    prevalentHyp = 1 if prevalentHyp == "Yes" else 0
+    diabetes = 1 if diabetes == "Yes" else 0
+    input_data = np.array([[male, age, currentSmoker, cigsPerDay, BPMeds, prevalentStroke, prevalentHyp, diabetes, BMI]])
+    prediction = model.predict(input_data)
+    return 'Heart Disease' if prediction[0] == 1 else 'No Heart Disease'
+
+# Define the input components
+inputs = [
+    gr.Radio(choices=["Male", "Female"], label="Sex"),
+    gr.Number(label="Age")
+    gr.Text(label="Do you smoke? (Yes/No)")
+    gr.Number(label="Number of ciggerates a day")
+    gr.Text(label="Do you take BP medicines?")
+    gr.text(label="Have you hade strokes previously?")
+    gr.text(label="Have you hade High BP previously?")
+    gr.text(label="Do you have Diabetes?")
+    gr.Number(value=float, label="BMI")
+
+]
+
+# Define the output component
+outputs = gr.Textbox(label="Prediction")
+
+# Create and launch the Gradio interface
+gr.Interface(fn=predict_heart_disease, inputs=inputs, outputs=outputs).launch()