Update app.py

app.py

@@ -5,7 +5,7 @@ from scipy.special import softmax
 import gradio as gr
 torch.cuda.is_available()
 
-model_path = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+model_path = "ltg/norbert3-base_sentence-sentiment"
 
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 config = AutoConfig.from_pretrained(model_path)
@@ -16,7 +16,7 @@ def sentiment_analysis(text):
     output = model(**encoded_input)
     scores_ = output[0][0].detach().numpy()
     scores_ = softmax(scores_)
-    labels = ['Negative', 'Neutral', 'Positive']
+    labels = ['Negativ', 'Nøytral', 'Positiv']
     scores = {l: float(s) for (l, s) in zip(labels, scores_)}
     return scores
 
@@ -26,12 +26,12 @@ demo = gr.Interface(
     inputs=gr.Textbox(placeholder="Write your text here..."),
     outputs="label",
     examples=[
-        ["I'm thrilled about the job offer!"],
-        ["The weather today is absolutely beautiful."],
-        ["I had a fantastic time at the concert last night."],
-        ["I'm so frustrated with this software glitch."],
-        ["The customer service was terrible at the store."],
-        ["I'm really disappointed with the quality of this product."]
+        ["Woho, jeg fikk meg ny jobb!"],
+        ["Jeg skal jobbe med løver i den nye jobben min."],
+        ["Oj, en løve spiste den ene armen min.. Snakk om HMS :("],
+        ["På vei til sykehus.. Ønsk meg lykke til.."],
+        ["Supert! De må pokkern meg amputere hele armen.."],
+        ["Våkna opp fra operasjon, fått en robot arm. Im now terminator! Super opplevelse 10 av 10.."]
     ],
     title='Sentiment Analysis App',
     description='This app classifies a positive, neutral, or negative sentiment.'