--- a
+++ b/server/model.py
@@ -0,0 +1,28 @@
+import pickle
+import numpy as np
+import os
+os.chdir(__file__.replace(os.path.basename(__file__), ''))
+os.chdir('../model')
+
+filename = 'trained_model.sav'
+model = pickle.load(open(filename, 'rb'))
+
+selected_feats =   [
+        'BVP_mean', 'BVP_std', 'EDA_phasic_mean', 'EDA_phasic_min', 'EDA_smna_min', 
+        'EDA_tonic_mean', 'Resp_mean', 'Resp_std', 'TEMP_mean', 'TEMP_std', 'TEMP_slope',
+        'BVP_peak_freq', 'age', 'height', 'weight'
+    ]
+
+labels = {
+    0: "Amused",
+    1: "Neutral",
+    2: "Stressed"
+}
+
+def predict(arr):
+    arr = np.array(arr)
+
+    global model
+    result = model.predict(arr.reshape(1,-1)).flatten()
+    prob = model.predict_proba(arr.reshape(1,-1)).flatten()
+    return result.tolist(), prob.tolist()
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