Update app.py

app.py

@@ -16,13 +16,18 @@ tokenizer = AutoTokenizer.from_pretrained(
 classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, max_length=512,
                 padding=True, truncation=True, batch_size=1)
 
+adibideak = json.load(open("./Adibideak.json", "r"))
+
 def prozesatu(Testua, request: gr.Request):
+    if Testua[-3:-1]=="...":
+       Testua = prozesatu.adibideak[Testua]
     prediction = prozesatu.classifier(Testua)[0]
     if prediction["label"]=="GAI":
         return {"Gai":prediction["score"], "Ez gai": 1-prediction["score"]}
     else:
         return {"Gai":1-prediction["score"], "Ez gai": prediction["score"]}
 
+prozesatu.adibideak = adibideak
 prozesatu.classifier = classifier
 
 def ezabatu(Testua):
@@ -41,4 +46,6 @@ with gr.Blocks() as demo:
     
   bidali_btn.click(fn=prozesatu, inputs=input, outputs=label)
   ezabatu_btn.click(fn=ezabatu, inputs=input, outputs=input)
+  gr.Examples(['Azken urte hauetan, japonen telebista oso esaguna da...', 'Denok bizi izan genuen iaz ia hiru hilabetez jardun zen...', 'Pandemia egoera eta konfinamendua dela eta, asko izan dira...'], inputs=input, outputs=label, label="Adibideak:", fn=prozesatu, cache_examples=True)
+
 demo.launch()