update

app.py

@@ -7,7 +7,7 @@ from inference import Inference
 class QueryInputForm:
     def __init__(self):
         # Title of the Streamlit form
-        st.title("Query Form")
+        st.title("E-Commerce Recommendation Engine Demo")
 
         # Predefined options for channel and device type
         self.channel_options = [

as.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+import os
+from datetime import datetime
+from inference import Inference
+
+products_path = os.path.join("products")
+inference = Inference()
+
+raw_query = {
+    'user_id': "new_user", # any user will be considered as a new user
+    'channel': 'Organic',
+    'device_type': 'Desktop',
+    'query_text': 'pizza',
+    'time': datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), # querytime
+}
+
+inference.get_recommendations(raw_query)
+print(inference.recommendations)
+
+

inference.py

@@ -4,7 +4,6 @@ import os
 
 class Inference():
     def __init__(self):
-   
         index_path = os.path.join("recommendation_model", "index")
         model_path = os.path.join("recommendation_model", "model")
         self.index = tf.keras.models.load_model(index_path)
@@ -34,6 +33,7 @@ class Inference():
         filtered_recs = self.products.filter(self.filter_by_id)
         # Add query input
         query_added_recs = filtered_recs.map(lambda x: {**self.query_input, **x})
+        
         # Get score
         score_added_recs = query_added_recs.batch(8).map(self.get_score).unbatch()
 
@@ -43,7 +43,7 @@ class Inference():
         # Order by score
         ordered_recs = self.order_by_score(recs)
 
-        # Decode values
+        # Decode values and return
         self.recommendations = list(map(self.decode_values, ordered_recs))
 
     def filter_by_id(self, item):