app.py

from flask import Flask, render_template, request
import pandas as pd
import joblib

app = Flask(__name__)

# Load model pipeline
model = joblib.load("ckd_rf_model_pca.pkl")
scaler = joblib.load("ckd_scaler.pkl")
pca = joblib.load("ckd_pca.pkl")

# Feature list
features = [
    'age', 'blood_pressure', 'specific_gravity', 'albumin', 'sugar',
    'red_blood_cells', 'pus_cell', 'pus_cell_clumps', 'bacteria',
    'blood_glucose_random', 'blood_urea', 'serum_creatinine', 'sodium',
    'potassium', 'haemoglobin', 'packed_cell_volume', 'white_blood_cell_count',
    'red_blood_cell_count', 'hypertension', 'diabetes_mellitus',
    'coronary_artery_disease', 'appetite', 'peda_edema', 'aanemia'
]

@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        input_data = {feature: float(request.form[feature]) for feature in features}

        # Convert to DataFrame
        input_df = pd.DataFrame([input_data])

        # Preprocess
        scaled = scaler.transform(input_df)
        pca_input = pca.transform(scaled)

        # Predict
        prediction = model.predict(pca_input)[0]
        prob = model.predict_proba(pca_input)[0][1]

        if prediction == 1:
            result = f"🟢 CKD Detected with {prob:.2%} confidence"
        else:
            result = f"🟡 No CKD Detected with {(1 - prob):.2%} confidence"

        return render_template("index.html", result=result)

    return render_template("index.html", result=None)

if __name__ == '__main__':
    app.run(debug=True,port=5000)