app.py

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()