| import os |
| os.system('pip install transformers gradio torch') |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import gradio as gr |
| import torch.nn.functional as F |
|
|
| tokenizer = AutoTokenizer.from_pretrained("EXt1/mdeberta-v3-base-thai-fakenews") |
| model = AutoModelForSequenceClassification.from_pretrained("EXt1/mdeberta-v3-base-thai-fakenews") |
|
|
| def classify_fake_news(text): |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
| outputs = model(**inputs) |
| logits = outputs.logits |
| probs = F.softmax(logits, dim=1) |
| probs = probs.detach().cpu().numpy()[0] |
|
|
| labels = ["Real News", "Fake News"] |
| predicted_class = probs.argmax() |
|
|
| label = labels[predicted_class] |
| prob = float(probs[predicted_class]) * 100 |
|
|
| return label, f"{prob:.2f}%" |
|
|
| |
| gr.Interface( |
| fn=classify_fake_news, |
| inputs=gr.Textbox(lines=8, placeholder="Enter text here..."), |
| outputs="text", |
| title="Thai Fake News Classification using mdeberta-v3-base", |
| description="Classifies Thai News as Fake or Real with 91 percent accuracy using a fine-tuned BERT model", |
| theme="compact" |
| ).launch() |
