app.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pickle
import gradio as gr
import numpy as np

# Load the CSV data
data = pd.read_csv('/content/Untitled spreadsheet - Heart desiese data.csv')  # Ensure the correct file path

# 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()

# Check the first few rows of the data (optional)
print(data.head())