| import os |
| os.environ["USE_TF"] = "0" |
| import torch |
| import gradio as gr |
| from transformers import DebertaV2Tokenizer |
| from src.model import SentiNetTransformer |
| from src.config import HPARAMS |
| from src.ui import build_demo |
|
|
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| hp = HPARAMS() |
| backbone_config_path = "model/config.json" |
| checkpint_path = "model/SentiNet_Transformer_params.pt" |
| tokenizer_path = "model/" |
|
|
| |
| model = SentiNetTransformer(model_path=backbone_config_path, fc_dropout=hp.transformer_fc_dropout).to(device) |
| state_dict = torch.load(checkpint_path, map_location=device, weights_only=True) |
| model.load_state_dict(state_dict) |
| tokenizer = DebertaV2Tokenizer.from_pretrained(tokenizer_path) |
|
|
| |
| @torch.no_grad() |
| def sentiment_classifier(model, tokenizer, text, thresh=0.5, max_length_trun=256, device=None): |
| if device is None: |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| inputs = tokenizer(text, return_tensors="pt", |
| add_special_tokens=True, max_length=max_length_trun, |
| truncation=True, padding=True) |
| inputs = {k: v.to(device) for k, v in inputs.items()} |
| |
| model.eval() |
| logits = model(inputs) |
| prob = torch.sigmoid(logits).cpu().numpy()[0][0] |
|
|
| if prob >= thresh: |
| return "😀 Positive", round(float(prob), 3) |
| else: |
| return "😞 Negative", round(float(prob), 3) |
|
|
| |
| def generation_fn(text): |
| return sentiment_classifier(model, tokenizer, text, max_length_trun=256, device=None) |
| |
| demo = build_demo( |
| generation_fn, |
| english_title = "# SentiNet: Transformer‑Based Sentiment Classifier", |
| persian_title = "# سنتینت: تحلیل احساسات با ترنسفورمر", |
| assets_dir = "assets", |
| app_title = "SentiNet" |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
