add examples

app.py

@@ -5,7 +5,7 @@ os.system("pip3 install torch==1.10.1+cpu torchvision==0.11.2+cpu torchaudio==0.
           "https://download.pytorch.org/whl/cpu/torch_stable.html")
 
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+from transformers import pipeline
 
 import spacy
 from spacy import displacy
@@ -25,14 +25,12 @@ options = {"ents": ["OSOBA",
                       "ORGANIZÁCIA": "lightcoral",
                       "LOKALITA": "lightgreen"}}
 
-tokenizer = AutoTokenizer.from_pretrained("crabz/slovakbert-ner")
-model = AutoModelForTokenClassification.from_pretrained("crabz/slovakbert-ner")
-ner_pipeline = pipeline(task='ner', model=model, tokenizer=tokenizer)
-nlp = spacy.blank("en")
+ner_pipeline = pipeline(task='ner', model="crabz/slovakbert-ner")
+nlp = spacy.blank("sk")
 
 
-def apply_ner(text: str):
-    classifications = ner_pipeline(text)
+def apply_ner(sentence: str):
+    classifications = ner_pipeline(sentence)
 
     entities = []
     for i in range(len(classifications)):
@@ -43,7 +41,7 @@ def apply_ner(text: str):
                     j += 1
                 entities.append((ner_map[classifications[i]['entity']].split('-')[1], classifications[i]['start'],
                                  classifications[j - 1]['end']))
-    doc = nlp(text)
+    doc = nlp(sentence)
 
     ents = []
     for ee in entities:
@@ -55,5 +53,12 @@ def apply_ner(text: str):
 
 
 intf = gr.Interface(fn=apply_ner, inputs="text", outputs="html", title='Slovak Named Entity Recognition',
-                    allow_flagging=False)
+                    allow_flagging=False,
+                    examples=[["Laboratóriá Úradu verejného zdravotníctva sekvenovaním potvrdili výskyt ďalších "
+                               "štyroch prípadov variantu omikron na Slovensku."],
+                              ["Čaputová opakovane tvrdí, že \"spravodlivosť na Slovensku neplatí vždy pre všetkých "
+                               "rovnako\"."]],
+                    description="Named Entity Recognition (NER) labels persons, organizations and locations in the "
+                                "given sentence.",
+                    article="")
 intf.launch()