textPreprocess.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
import os

# ── 1) Configuration ────────────────────────────────────────────────────────────
BASE_DIR = "MAS-AI-0000/Authentica"
MODEL_DIR = os.path.join(BASE_DIR, "Lib/Models/Text")  # Update this path to your model location
MAX_LEN = 512

# ── 2) Load model & tokenizer ──────────────────────────────────────────────────
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Text prediction device: {device}")

# Global variables for model and tokenizer
tokenizer = None
model = None
ID2LABEL = {0: "human", 1: "ai"}

try:
    # Config carries id2label/label2id if you saved them
    config = AutoConfig.from_pretrained(MODEL_DIR)
    
    # Loads tokenizer.json + special_tokens_map.json automatically
    tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, use_fast=True)
    
    # Loads model.safetensors automatically (no extra flags needed)
    model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR, config=config)
    model.eval().to(device)
    
    # Update label mapping from config if available
    ID2LABEL = model.config.id2label if getattr(model.config, "id2label", None) else {0: "human", 1: "ai"}
    
    print(f"Text classification model loaded successfully")
    print("Labels:", ID2LABEL)
except Exception as e:
    print(f"Error loading text model: {e}")
    print("Text prediction will return fallback responses")

# ── 3) Inference function ──────────────────────────────────────────────────────
@torch.inference_mode()
def predict_text(text: str, max_length: int = None):
    """
    Predict whether the given text is human-written or AI-generated.
    
    Args:
        text (str): The text to classify
        max_length (int): Maximum sequence length for tokenization (defaults to MAX_LEN)
        
    Returns:
        dict: Contains predicted_class and confidence
    """
    if model is None or tokenizer is None:
        return {"predicted_class": "Human", "confidence": 0}
    
    if max_length is None:
        max_length = MAX_LEN
    
    try:
        # Tokenize input
        enc = tokenizer(
            text,
            return_tensors="pt",
            truncation=True,
            max_length=max_length,
        )
        enc = {k: v.to(device) for k, v in enc.items()}
        
        # Get predictions
        logits = model(**enc).logits
        probs = torch.softmax(logits, dim=-1).squeeze(0).detach().cpu().numpy()
        pred_id = int(probs.argmax(-1))
        
        # Get label (capitalize first letter for consistency)
        label = ID2LABEL.get(pred_id, str(pred_id))
        label = label.capitalize()  # "human" -> "Human", "ai" -> "Ai"
        
        return {
            "predicted_class": label,
            "confidence": float(probs[pred_id])
        }
    except Exception as e:
        print(f"Error during text prediction: {e}")
        return {"predicted_class": "Human", "confidence": 0}

# ── 4) Batch prediction (optional, for future use) ─────────────────────────────
@torch.inference_mode()
def predict_batch(texts, batch_size=16):
    """
    Predict multiple texts in batches.
    
    Args:
        texts (list): List of text strings to classify
        batch_size (int): Batch size for processing
        
    Returns:
        list: List of prediction dictionaries
    """
    if model is None or tokenizer is None:
        return [{"predicted_class": "Human", "confidence": 0} for _ in texts]
    
    results = []
    for i in range(0, len(texts), batch_size):
        chunk = texts[i:i+batch_size]
        enc = tokenizer(
            chunk,
            return_tensors="pt",
            truncation=True,
            max_length=MAX_LEN,
            padding=True,
        )
        enc = {k: v.to(device) for k, v in enc.items()}
        logits = model(**enc).logits
        probs = torch.softmax(logits, dim=-1).detach().cpu().numpy()
        ids = probs.argmax(-1)
        
        for t, pid, p in zip(chunk, ids, probs):
            label = ID2LABEL.get(int(pid), str(int(pid))).capitalize()
            results.append({
                "text": t,
                "predicted_class": label,
                "confidence": float(p[int(pid)])
            })
    return results