Update app.py

app.py

@@ -27,6 +27,8 @@ def user_query_recommend(query, min_p, max_p):
     recommendations = reviews.copy()
     recommendations['sim'] = sim_scores.T
     recommendations = recommendations.sort_values('sim', ascending=False)
+    if max_p < min_p:
+        min_p = 0
     recommendations = recommendations.loc[(recommendations.price >= min_p) &
                                           (recommendations.price <= max_p),
                                           ['name', 'price', 'description']].head(10)
@@ -36,14 +38,15 @@ def user_query_recommend(query, min_p, max_p):
 interface = gr.Interface(
     user_query_recommend, 
     inputs=[gr.inputs.Textbox(lines=5),
-            gr.inputs.Slider(minimum=1, maximum=100, default=30, label='Min Price'),
-            gr.inputs.Slider(minimum=1, maximum=1000, default=70, label='Max Price')],
+            gr.inputs.Slider(minimum=10, maximum=1000, default=30, label='Min Price'),
+            gr.inputs.Slider(minimum=30, maximum=1000, default=70, label='Max Price')],
     outputs=gr.outputs.Dataframe(max_rows=3, overflow_row_behaviour="paginate", type="pandas", label="Recommendations"),
     title = "Scotch Recommendation",
-    description = "Looking for scotch recommendations and have some flavours in mind? Get recommendations using semantic search through distilRoberta embeddings at a preferred price range :) ",
+    description = "Looking for scotch recommendations and have some flavours in mind? \nGet recommendations at a preferred price range using semantic search :) ",
     examples=[["very sweet with lemons and oranges and marmalades", 10,40], 
               ["smoky peaty earthy and spicy",50,100],
               ["salty and spicy with exotic fruits", 50,500], 
+              ["fragrant nose with chocolate, toffee, pudding and caramel", 100,500], 
               ],
     theme="grass",
 )