Update app.py

app.py

@@ -1,23 +1,25 @@
 import gradio as gr
 
-from transformers import AutoModel, pipeline, AutoTokenizer
+from transformers import AutoModel, pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
-access_token = "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"
+access_token = "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"
 
-model = AutoModel.from_pretrained("EkhiAzur/RoBERTA_3", token=access_token)
+model = AutoModelForSequenceClassification.from_pretrained("EkhiAzur/RoBERTA_3", token=access_token)
 
 tokenizer = AutoTokenizer.from_pretrained(
-  "ixa-ehu/roberta-eus-euscrawl-large-cased",
+  "EkhiAzur/RoBERTA_3",
+  token = access_token,
   use_fast=True,
   add_prefix_space=True,
 )
 
-classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, max_length=512,
+classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, max_length=512,
                 padding=True, truncation=True, batch_size=1)
 
 def prozesatu(testua):
-    prediction = prozesatu.classifier([testua])
-    return f'C1:{prediction["label"]}. Probabilitatea:{prediction["score"]}'
+  
+    prediction = prozesatu.classifier(testua)[0]
+    return 'C1:{}. Probabilitatea:{:.2f}'.format(prediction["label"], round(prediction["score"], 2))
 
 prozesatu.classifier = classifier