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from transformers import BertTokenizer, BertModel, AutoTokenizer, AutoModel
from sklearn.ensemble import IsolationForest
from tqdm import tqdm
import torch
import gradio as gr
import numpy as np
tokenizer = AutoTokenizer.from_pretrained("neuralmind/bert-base-portuguese-cased")
model = AutoModel.from_pretrained("neuralmind/bert-base-portuguese-cased")
model.eval()
data = np.load("X_test.npy")
iso_forest = IsolationForest(contamination=0.1, random_state=42)
iso_forest.fit(data)
def test_email(text):
with torch.no_grad():
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=256)
outputs = model(**inputs)
cls_embedding = outputs.last_hidden_state[:, 0, :].cpu().numpy()
pred = iso_forest.predict(cls_embedding)[0]
if pred == -1:
return "Anomaly detected"
else:
return "Normal"
demo = gr.Interface(fn=test_email, inputs="text", outputs="text")
demo.launch()
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