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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer_sentence_analysis = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
+model_sentence_analysis = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
+paragraph = """
+I woke up this morning feeling refreshed and excited for the day ahead. 
+I had a great night's sleep, and I was looking forward to spending time with my family and friends. 
+I went for a walk in the park, and I enjoyed the beautiful weather. I also stopped by my favorite coffee shop and got a delicious cup of coffee. 
+I felt so happy and content, and I knew that it was going to be a great day.
+
+"""
+def sentence_sentiment_model(text, tokenizer, model):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        result = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+        logits = result.logits.detach()
+        probs = torch.softmax(logits, dim=1)
+    pos_prob = probs[0][2].item()
+    neu_prob = probs[0][1].item()  
+    neg_prob = probs[0][0].item() 
+    return {'Positive': [round(float(pos_prob), 2)],"Neutural":[round(float(neu_prob), 2)], 'Negative': [round(float(neg_prob), 2)]}
+
+def sentence_sentiment(text):
+    result = sentence_sentiment_model(text,tokenizer_sentence_analysis,model_sentence_analysis)
+    return result
+
+with gr.Blocks(title="Sentence",css="footer {visibility: hidden}") as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Sentence sentiment")
+            with gr.Row():           
+                with gr.Column():
+                    inputs = gr.TextArea(label="sentence",value=paragraph,interactive=True)
+                    btn = gr.Button(value="RUN")
+                with gr.Column():
+                    output = gr.Label(label="output")
+                btn.click(fn=sentence_sentiment,inputs=[inputs],outputs=[output])
+demo.launch()

requirements.txt

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+gradio==3.32.0
+torch==2.0.0
+transformers==4.28.1