Fix app.py

.gitignore

@@ -0,0 +1 @@
+.idea

app.py

@@ -2,12 +2,10 @@ import os
 import numpy as np
 import tensorflow as tf
 import pandas as pd
+import streamlit as st
 import time
 
 from recommenders.models.sasrec.model import SASREC
-from tabulate import tabulate
-
-import streamlit as st
 
 
 class SASREC_Vessl(SASREC):
@@ -56,6 +54,16 @@ class SASREC_Vessl(SASREC):
         return predictions
 
 
+def elapsed_time(fn, *args):
+    start = time.time()
+    output = fn(*args)
+    end = time.time()
+
+    elapsed = f'{end - start:.2f}'
+
+    return elapsed, output
+
+
 def load_model():
     model_config = {
         "MAXLEN": 50,
@@ -81,19 +89,44 @@ def load_model():
         num_neg_test=model_config.get("NUM_NEG_TEST"),
     )
 
-    if os.path.isfile('best.index') and os.path.isfile('best.data-00000-of-00001'):
+    if os.path.isfile('best.index') and os.path.isfile(
+            'best.data-00000-of-00001'):
         model.load_weights('best').expect_partial()
 
     return model
 
 
-def main():
-    st.title('Self-Attentive Sequential Recommendation(SASRec)')
-    model = load_model()
-    st.write(model)
+def postprocess_data(data):
+    predictions = -1 * data
+    rec_items = predictions.argsort()[:5]
 
-    numbers = st.text_input
-    st.write(numbers)
+    dic_result = {
+        "Rank": [i for i in range(1, 6)],
+        "ItemID": list(rec_items + 1),
+        "Similarity Score": -1 * predictions[rec_items]
+    }
+    result = pd.DataFrame(dic_result)
+
+    time.sleep(0.5)
+
+    best_item = rec_items[0] + 1
+
+    return result, best_item
+
+
+def main():
+    st.title("Self-Attentive Sequential Recommendation(SASRec)")
+    elapsed, model = elapsed_time(load_model)
+    st.write(f"Model is loaded in {elapsed} seconds!")
+
+    numbers = st.text_input(
+        label="Please write input items separated by comma. (e.g. 80, 70, 100, 1)")
+    if numbers:
+        integer_numbers = np.array(list(map(int, numbers.split(","))))
+        result = model.predict_next(integer_numbers)
+        table, best_item = postprocess_data(result)
+        st.table(table)
+        st.write(f"Best item is {best_item}")
 
 
 if __name__ == '__main__':

best.data-00000-of-00001

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:214d4120e8eb750023ccec9ef22d615cd0d9ae6028b605c622a2d452eec491b7
-size 5389888
+oid sha256:faac3d239691b156d73bf8d6ae5fcfc7fdd9c1fe7952c43501c30109bc9a36d8
+size 5390136

best.index

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:90232741029421cb9a2bf31e3eff972467f8613017d1af46ac0f36225bb34f9c
+oid sha256:d507d7b032877b335818718a0381ef2028512e01a90570c47ab3f678a75e5076
 size 2055

requirements.txt

@@ -1,3 +1,2 @@
 tensorflow
-tabulate
 recommenders