Upload app.py

app.py

@@ -0,0 +1,114 @@
+import numpy as np
+from flask import Flask, request, jsonify, render_template
+import pickle
+from sklearn.preprocessing import normalize
+from werkzeug.utils import secure_filename
+import scipy.signal as signal
+import pandas as pd
+import os
+
+##########
+
+with open('model_a1.bin', 'rb') as f_in:
+    model_n = pickle.load(f_in)
+
+"""
+def intensityNormalisationFeatureScaling(da, dtype):
+    max = da.max()
+    min = da.min()
+
+    return ((da - min) / (max - min)).astype(dtype)
+"""
+
+def filter_data(da, fs):
+
+    """Filters the ECG data with a highpass at 0.1Hz and a bandstop around 50Hz (+/-2 Hz)"""
+
+    b_dc, a_dc = signal.butter(4, (0.1/fs*2), btype='highpass')
+    b_50, a_50 = signal.butter(4, [(48/fs*2),(52/fs*2)], btype='stop')
+
+    da = signal.lfilter(b_dc, a_dc, da)
+    da = signal.lfilter(b_50, a_50, da)
+
+    return da
+
+
+
+# Return difference array
+def return_diff_array_table(array, dur):
+    for idx in range(array.shape[1]-dur):
+        before_col = array[:,idx]
+        after_col = array[:,idx+dur]
+        new_col = ((after_col - before_col)+1)/2
+        new_col = new_col.reshape(-1,1)
+        if idx == 0:
+            new_table = new_col
+        else :
+            new_table = np.concatenate((new_table, new_col), axis=1)
+#For concat add zero padding
+    padding_array = np.zeros(shape=(array.shape[0],dur))
+    new_table = np.concatenate((padding_array, new_table), axis=1)
+    return new_table
+
+
+#Concat
+def return_merge_diff_table(df, diff_dur):
+    fin_table = df.reshape(-1,187,1,1)
+    for dur in diff_dur:
+        temp_table = return_diff_array_table(df, dur)
+        fin_table = np.concatenate((fin_table, temp_table.reshape(-1,187,1,1)), axis=2)
+    return fin_table
+
+
+def predict_endpoint(features):
+    #features = intensityNormalisationFeatureScaling(features, float)
+    features = filter_data(features, 300)
+    X = return_merge_diff_table(features, diff_dur=[1])
+    preds = model_n.predict(X)
+    return preds
+
+
+
+
+app = Flask('__name__')
+
+
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    
+    Age=float(request.form['Age'])
+    
+    Gender=float(request.form['Gender'])
+    
+    f = request.files['signal']
+    f.save(secure_filename(f.filename))
+
+    df = pd.read_csv(f.filename, header=None)
+
+    df_2 = df.iloc[3:4,0:187] 
+
+    features_t = np.array(df_2)
+
+    pred = predict_endpoint(features_t) 
+
+    print(pred[0])
+
+
+   
+
+    if float(pred[0][0]) > float(pred[0][1]):
+        os.remove(f.filename)
+        return render_template('Good.html', prediction=pred[0][0])
+    else:
+        return render_template('Bad.html', prediction2=pred[0][1])
+
+    
+
+if __name__ == "__main__":
+    #app.run(debug=True, host='0.0.0.0', port=9696)
+    app.run(debug=True)