Upload app.py

app.py

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+import numpy as np
+from flask import Flask, request, jsonify, render_template
+import pickle
+from sklearn.preprocessing import normalize
+
+app = Flask(__name__)
+model = pickle.load(open('model.pkl', 'rb'))
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    '''
+    For rendering results on HTML GUI
+    '''
+    Age=float(request.form['Age'])
+    CigsPerDay=float(request.form['CigsPerDay'])
+    Cholestrol=float(request.form['Cholestrol'])
+    SysBP=float(request.form['SysBP'])
+    DIaBP=float(request.form['DIaBP'])
+    BMI=float(request.form['BMI'])
+    HeartRate=float(request.form['HeartRate'])
+    GlucoseLevel=float(request.form['GlucoseLevel'])
+    Gender=float(request.form['Gender'])
+    BpMedication=float(request.form['BpMedication'])
+    PrevalentStroke=float(request.form['PrevalentStroke'])
+    Smoker=float(request.form['Smoker'])
+    list_to_be_normalised=np.array([ Age,CigsPerDay, Cholestrol, SysBP,DIaBP, BMI,HeartRate,GlucoseLevel]).reshape(1,-1)
+    normalized = normalize(list_to_be_normalised)
+    boolean = [Gender,BpMedication,PrevalentStroke,Smoker]
+    final_features = np.append(normalized,boolean).reshape(1, -1)
+    print(final_features)
+    prediction = model.predict(final_features)
+
+    if prediction == 1:
+        return render_template('problem.html')
+    else:
+        return render_template('healthy.html')
+# @app.route('/predict_api',methods=['POST'])
+# def predict_api():
+#     '''
+#     For direct API calls trought request
+#     '''
+#     data = request.get_json(force=True)
+#     prediction = model.predict([np.array(list(data.values()))])
+
+#     output = prediction[0]
+#     return jsonify(output)
+
+if __name__ == "__main__":
+    app.run(debug=True)