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| from transformers import ( | |
| AlbertTokenizerFast, DistilBertTokenizerFast, RobertaTokenizerFast, | |
| AlbertForSequenceClassification, DistilBertForSequenceClassification, RobertaForSequenceClassification | |
| ) | |
| import torch.nn.functional as F | |
| import torch | |
| import gradio as gr | |
| from gradio.components import Textbox | |
| albert_tokenizer = AlbertTokenizerFast.from_pretrained('albert-base-v2') | |
| bert_tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased') | |
| roberta_tokenizer = RobertaTokenizerFast.from_pretrained('distilroberta-base') | |
| albert_model = AlbertForSequenceClassification.from_pretrained("./model_albert") | |
| bert_model = DistilBertForSequenceClassification.from_pretrained("./model_bert") | |
| roberta_model = RobertaForSequenceClassification.from_pretrained("./model_roberta") | |
| def majority_voting(votes): | |
| vote_result = max(set(votes), key=votes.count) | |
| return vote_result | |
| def predict_news(news_title, news_text): | |
| combined_text = f"{news_title} - {news_text}" | |
| albert_input = albert_tokenizer(combined_text, return_tensors='pt', truncation=True, padding=True) | |
| bert_input = bert_tokenizer(combined_text, return_tensors='pt', truncation=True, padding=True) | |
| roberta_input = roberta_tokenizer(combined_text, return_tensors='pt', truncation=True, padding=True) | |
| with torch.no_grad(): | |
| albert_logits = albert_model(**albert_input).logits | |
| bert_logits = bert_model(**bert_input).logits | |
| roberta_logits = roberta_model(**roberta_input).logits | |
| albert_probs = F.softmax(albert_logits, dim=-1) | |
| bert_probs = F.softmax(bert_logits, dim=-1) | |
| roberta_probs = F.softmax(roberta_logits, dim=-1) | |
| albert_pred_class = albert_probs.argmax(-1).item() | |
| bert_pred_class = bert_probs.argmax(-1).item() | |
| roberta_pred_class = roberta_probs.argmax(-1).item() | |
| albert_pred_prob = albert_probs[0, albert_pred_class].item() | |
| bert_pred_prob = bert_probs[0, bert_pred_class].item() | |
| roberta_pred_prob = roberta_probs[0, roberta_pred_class].item() | |
| average_confidence = (albert_pred_prob + bert_pred_prob + roberta_pred_prob) / 3 | |
| final_pred_class = majority_voting([albert_pred_class, bert_pred_class, roberta_pred_class]) | |
| prediction = 'REAL' if final_pred_class == 1 else 'FAKE' | |
| return prediction, f"{average_confidence:.2f}", f"{albert_pred_prob:.2f}", f"{bert_pred_prob:.2f}", f"{roberta_pred_prob:.2f}" | |
| iface = gr.Interface( | |
| fn=predict_news, | |
| inputs=[ | |
| Textbox(lines=2, label="News Title", placeholder="Enter News Title Here..."), | |
| Textbox(lines=7, label="News Text", placeholder="Enter News Text Here...") | |
| ], | |
| outputs=[ | |
| Textbox(label="Prediction"), | |
| Textbox(label="Average Confidence"), | |
| Textbox(label="ALBERT Confidence"), | |
| Textbox(label="BERT Confidence"), | |
| Textbox(label="RoBERTa Confidence") | |
| ], | |
| title="Fake News Classification with Ensemble Learning Transformer Models", | |
| description="UAS Deep Learning - 2501985836 - Arya Adyatma" | |
| ) | |
| iface.launch() | |