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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+
+tokenizer_review_feedback_sentiment = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
+model_review_feedback_sentiment = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
+
+
+def review_feedback_sentiment(text, tokenizer, model):
+    inputs = tokenizer.encode_plus(text, padding='max_length', max_length=512, 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).detach().numpy()[0]
+        categories = ['Terrible', 'Poor', 'Average', 'Good', 'Excellent']
+        output_dict = {}
+        for i in range(len(categories)):
+            output_dict[categories[i]] = [round(float(probs[i]), 2)]
+    return output_dict
+
+def review_feed_back(text):
+    result = review_feedback_sentiment(text,tokenizer_review_feedback_sentiment,model_review_feedback_sentiment)
+    return result
+
+with gr.Blocks(title="Feedback",css="footer {visibility: hidden}") as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Review Feedback sentiment")
+            with gr.Row():           
+                with gr.Column():
+                    inputs = gr.TextArea(label="sentence",value="I'm so impressed with your product! It's exactly what I needed and it's working great.",interactive=True)
+                    btn = gr.Button(value="RUN")
+                with gr.Column():
+                    output = gr.Label(label="output")
+                btn.click(fn=review_feed_back,inputs=[inputs],outputs=[output])
+demo.launch()

requirements.txt

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