Upload 4 files

gui.py

@@ -0,0 +1,45 @@
+from flask import Flask, render_template, request
+import pickle
+import numpy as np
+
+app = Flask(__name__)
+
+# Load model
+with open('random_forest_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+@app.route('/', methods=['GET', 'POST'])
+def predict():
+    if request.method == 'POST':
+        # Ambil input dari form
+        age = int(request.form['age'])
+        gender = int(request.form['gender'])
+        height = float(request.form['height'])
+        weight = float(request.form['weight'])
+        ap_hi = int(request.form['ap_hi'])
+        ap_lo = int(request.form['ap_lo'])
+        cholesterol = int(request.form['cholesterol'])
+        gluc = int(request.form['gluc'])
+        smoke = int(request.form['smoke'])
+        alco = int(request.form['alco'])
+        active = int(request.form['active'])
+
+
+        # Buat array input untuk model
+        input_features = np.array([[age, gender, height, weight, ap_hi, ap_lo,
+                                     cholesterol, gluc, smoke, alco, active]])
+
+        # Prediksi
+        prediction = model.predict(input_features)[0]  # 0 = Low Risk, 1 = High Risk
+        probability = model.predict_proba(input_features)[0][1]  # Probabilitas risiko tinggi
+
+        # Tentukan hasil berdasarkan prediksi
+        risk_level = "Tinggi" if prediction == 1 else "Rendah"
+        percentage = round(probability * 100, 2)
+
+        return render_template('index.html', risk_level=risk_level, percentage=percentage)
+
+    return render_template('index.html', risk_level=None)
+
+if __name__ == '__main__':
+    app.run(debug=True)

importsklearn.py

@@ -0,0 +1,2 @@
+import sklearn
+print(sklearn.__version__)

random_forest_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19cfebc3156ca0e30b28bdfaee417771746a272560b5466a4e71895479231b3d
+size 108745