| 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() |