Update app.py

app.py

@@ -7,7 +7,7 @@ os.environ["WANDB_DISABLED"] = "true"
 from datasets import load_dataset, load_metric
 from transformers import (
     AutoConfig,
-    AutoModelForSequenceClassification,
+    # AutoModelForSequenceClassification,
     AutoTokenizer,
     TrainingArguments,
     logging,
@@ -32,15 +32,15 @@ pipeline
 
 
 # Question answering pipeline, specifying the checkpoint identifier
-model = AutoModelForSequenceClassification.from_pretrained(
-    pretrained_model_name_or_path= "thak123/Cro-Frida",
-    num_labels=3,
-)
+# model = AutoModelForSequenceClassification.from_pretrained(
+#     pretrained_model_name_or_path= "thak123/Cro-Frida",
+#     num_labels=3,
+# )
 
 
 analyzer = pipeline(
 
-    "sentiment-analysis", model=model, tokenizer="EMBEDDIA/crosloengual-bert"
+    "sentiment-analysis", model="thak123/Cro-Frida", tokenizer="EMBEDDIA/crosloengual-bert"
 
 )
 def predict_sentiment(x):
@@ -54,7 +54,7 @@ interface = gr.Interface(
     fn=predict_sentiment,
     inputs='text',
     outputs=['label'],
-    title='Latvian Twitter Sentiment Analysis',
+    title='Croatian Movie reviews Sentiment Analysis',
     examples= ["Es mīlu Tevi","Es ienīstu kafiju"],
     description='Get the positive/neutral/negative sentiment for the given input.'
 )