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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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import sys |
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from pathlib import Path |
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MODEL_PATH = Path("models/binary/final") |
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MAX_LENGTH = 512 |
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def load_model_and_tokenizer(): |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) |
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model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH) |
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model.eval() |
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return tokenizer, model |
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def predict(filepath: Path, tokenizer, model, debug: bool = False): |
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text = filepath.read_text(encoding="utf-8") |
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inputs = tokenizer( |
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text, |
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return_tensors="pt", |
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truncation=True, |
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padding="max_length", |
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max_length=MAX_LENGTH, |
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) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze() |
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pred = torch.argmax(probs).item() |
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label = "good" if pred == 0 else "bad" |
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print(f"\n🧪 Predykcja dla pliku: {filepath.name}") |
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print(f"📄 Długość: {len(text.splitlines())} linii") |
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print(f"📊 Wynik: {label.upper()}") |
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print(f"🔢 Prawdopodobieństwo: good={probs[0]:.4f}, bad={probs[1]:.4f}") |
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if debug: |
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print("\n🛠 DEBUG INFO:") |
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print(f"✏️ Surowy tekst (pierwsze 300 znaków):\n{text[:300]}") |
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print(f"🔢 Liczba tokenów: {len(inputs['input_ids'][0])}") |
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print(f"📈 Logity: {logits.tolist()[0]}") |
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print(f"📊 Softmax: good={probs[0].item():.5f}, bad={probs[1].item():.5f}") |
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def main(): |
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if len(sys.argv) < 2: |
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print("❌ Użycie: python3 12.1_predict_binary_file.py /ścieżka/do/Dockerfile [--debug]") |
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sys.exit(1) |
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filepath = Path(sys.argv[1]) |
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debug = "--debug" in sys.argv |
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if not filepath.exists(): |
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print(f"❌ Plik {filepath} nie istnieje.") |
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sys.exit(1) |
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tokenizer, model = load_model_and_tokenizer() |
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predict(filepath, tokenizer, model, debug=debug) |
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if __name__ == "__main__": |
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main() |
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