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import gradio as gr
import torch
import re
from underthesea import word_tokenize
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("VietTung04/videberta-base-topic-classification")
model = AutoModelForSequenceClassification.from_pretrained("VietTung04/videberta-base-topic-classification")
# Check if GPU is available and set the device accordingly
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
def preprocess_fn(text):
stopword_path = 'vietnamese-stopwords.txt'
with open(stopword_path, 'r', encoding='utf-8') as file:
stopwords = file.read().splitlines()
def remove_stopwords(tokens):
return [word for word in tokens if word not in stopwords]
text = re.sub(r'http\S+', ' ', text) # Remove URLs
text = re.sub(r'#\w+', ' ', text) # Remove hashtags
text = re.sub(r'@\w+', ' ', text) # Remove mentions
text = re.sub(r'\d+', ' ', text) # Remove numbers
text = re.sub(r'[^\w\sđĐàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆìÌỉỈĩĨíÍịỊòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰỳỲỷỶỹỸýÝỵỴ]', ' ', text) # Remove special characters
# Tokenize Vietnamese text
tokens = word_tokenize(' '.join(text.split()).lower())
# Remove stop words
tokens = remove_stopwords(tokens)
return ' '.join(tokens)
def predict_topic(text):
inputs = tokenizer(
preprocess_fn(text),
truncation=True,
padding='max_length',
max_length=512,
add_special_tokens=True,
return_tensors='pt'
)
inputs = {key: value.to(device) for key, value in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1).cpu().numpy()[0]
# Get the top 3 classes
top3_indices = probabilities.argsort()[-3:][::-1]
top3_probabilities = probabilities[top3_indices]
top3_classes = [model.config.id2label[idx] for idx in top3_indices] # Assuming your model has this attribute
return {top3_classes[i]: float(top3_probabilities[i]) for i in range(3)}
# Define the Gradio interface
iface = gr.Interface(
fn=predict_topic,
inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
outputs=gr.Label(num_top_classes=3),
title="Text Classification",
description="Enter text to classify it into different categories and get the probability for each class."
)
# Launch the interface
iface.launch() |