feat: add bert and isoforest

app.py

@@ -1,7 +1,29 @@
+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 greet(name):
-    return "Hello " + name + "!!"
+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=greet, inputs="text", outputs="text")
+demo = gr.Interface(fn=test_email, inputs="text", outputs="text")
 demo.launch()