| import gradio as gr | |
| from transformers import BertForSequenceClassification, BertTokenizer | |
| import torch | |
| model_name = "ArunNyp7/sentimentclassifier-finetuned-bert" | |
| model = BertForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = BertTokenizer.from_pretrained(model_name) | |
| def classify_sentiment(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| prediction = torch.argmax(logits, dim=-1).item() | |
| labels = {0: "Positive", 1: "Neutral", 2: "Negative"} | |
| return labels[prediction] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("### Sentiment Classification using Fine-Tuned BERT Model") | |
| text_input = gr.Textbox(label="Enter Text", placeholder="Type here...", lines=2) | |
| sentiment_output = gr.Label() | |
| submit_btn = gr.Button("Classify Sentiment") | |
| submit_btn.click(fn=classify_sentiment, inputs=text_input, outputs=sentiment_output) | |
| demo.launch(share=False) |