Update app.py

app.py

@@ -5,4 +5,34 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer, Auto
 import numpy as np
 from scipy.special import softmax
 import gradio as gr
-torch.cuda.is_available()
+torch.cuda.is_available()
+model_path = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+def sentiment_analysis(text):
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores_ = output[0][0].detach().numpy()
+    scores_ = softmax(scores_)
+    labels = ['Negative', 'Neutral', 'Positive']
+    scores = {l: float(s) for (l, s) in zip(labels, scores_)}
+    return scores
+demo = gr.Interface(
+    theme=gr.themes.Base(),
+    fn=sentiment_analysis,
+    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."]
+    ],
+    title='Sentiment Analysis App',
+    description='This app classifies a positive, neutral, or negative sentiment.'
+)
+demo.launch()