Update app.py

app.py

@@ -10,15 +10,31 @@ data = pd.DataFrame(
 
 adjs = set(data["Adj"])
 Nns  = set(list(data["Nn"]) + list(data["Hypr"]))
+all_set = set(list(adjs) + list(Nns))
 
+def test_input(words):
+    words = list(words)
+    word_dict = {}
+    for w in words:
+        if w in all_set:
+            word_dict[w] = "in-distribution"
+        else:
+            word_dict[w] = "out-of-distribution"
+    return word_dict
+    
 description = "Did you know that, logically speaking, a small cat is not a small animal, and that a fake smile is not a smile. \n Learn more testing our BERT model is tuned to perform phrase-level adjective-noun entailment, via the [PLANE](https://aclanthology.org/2022.coling-1.359/) dataset. \n The current model achieves an accuracy of 90% on out-of-distribution evaluation"
 title = "BERT on a PLANE"
 examples = [["A red car is a vehicle"], ["A fake smile is a smile"], ["A small cat is a small animal"]]
 
-interface = gr.Interface.load("huggingface/lorenzoscottb/bert-base-cased-PLANE-ood-2",
+interface_model = gr.Interface.load("huggingface/lorenzoscottb/bert-base-cased-PLANE-ood-2",
             description=description,
             examples=examples,
             title=title,
 )
 
-interface.launch()
+# interface.launch()
+
+
+Series(
+    interface_model  
+).launch()