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da8ca36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import os
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from utils.preprocess import preprocess_text
FAKE_DIR = "data/fake_news/financeiros"
REAL_DIR = "data/real_news/financeiros"
MODEL_DIR = "app/model"
MAX_LEN = 256
BATCH_SIZE = 8
device = "cuda" if torch.cuda.is_available() else "cpu"
# ========= LOAD DATA =========
def load_texts(directory, label):
samples = []
for root, _, files in os.walk(directory):
for fname in files:
if fname.endswith(".txt"):
path = os.path.join(root, fname)
with open(path, "r", encoding="utf-8") as f:
text = preprocess_text(f.read())
samples.append((text, label))
return samples
def load_dataset():
fake = load_texts(FAKE_DIR, 0)
real = load_texts(REAL_DIR, 1)
data = fake + real
texts = [t for t, _ in data]
labels = [l for _, l in data]
return texts, labels
# ========= DATASET =========
class NewsDataset(Dataset):
def __init__(self, texts, labels, tokenizer):
self.texts = texts
self.labels = labels
self.tok = tokenizer
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
enc = self.tok(
self.texts[idx],
truncation=True,
padding="max_length",
max_length=MAX_LEN,
return_tensors="pt"
)
enc = {k: v.squeeze() for k, v in enc.items()}
enc["labels"] = torch.tensor(self.labels[idx], dtype=torch.long)
return enc
# ========= EVALUATION =========
def evaluate():
print("Carregando modelo...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR).to(device)
print("Carregando dataset...")
texts, labels = load_dataset()
dataset = NewsDataset(texts, labels, tokenizer)
loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False)
model.eval()
preds = []
true_labels = []
print("\nAvaliando...\n")
with torch.no_grad():
for batch in loader:
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
p = torch.argmax(outputs.logits, dim=1).cpu().numpy()
l = batch["labels"].cpu().numpy()
preds.extend(p)
true_labels.extend(l)
# === METRICS ===
acc = accuracy_score(true_labels, preds)
print(f"Accuracy: {acc:.4f}")
print("\nClassification Report:")
print(classification_report(true_labels, preds, target_names=["Fake", "Real"]))
print("\nConfusion Matrix:")
print(confusion_matrix(true_labels, preds))
if __name__ == "__main__":
evaluate()
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