backend/detector/views.py

from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from django.conf import settings
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import os
import math
# Model: roberta-base (Masked LM)
# Logic: Low Perplexity = High AI Probability (Predictable)
MODEL_NAME = "roberta-base"
print(f"Loading Model: {MODEL_NAME}...")

try:
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"Using Device: {device}")

    # Use MaskedLM to calculate loss/perplexity
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForMaskedLM.from_pretrained(MODEL_NAME)
    model.to(device)
    model.eval()
    print("Model Loaded Successfully!")

except Exception as e:
    print(f"Error loading model: {e}")
    model = None
    tokenizer = None

import time

def calculate_token_scores(text):
    """
    Analyzes text at token level to identify AI-generated regions.
    Returns: List of (start_char, end_char, score) tuples.
    """
    if not tokenizer or not model:
        return []

    encodings = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, return_offsets_mapping=True)
    input_ids = encodings.input_ids.to(device)
    offsets = encodings.offset_mapping[0].cpu().numpy() # [(0,0), (0,3), (3,4)...]
    seq_len = input_ids.shape[1]
    
    if seq_len < 4:
        return []

    # Calculate loss for every token (Stride=1 for max granularity)
    nlls = []
    
    # Reduced BATCH_SIZE to 4 to prevent OOM on 4GB GPU
    BATCH_SIZE = 4
    tensor_input_ids = input_ids.repeat(BATCH_SIZE, 1)
    
    # Iterate all tokens (excluding CLS/SEP)
    indices_to_mask = list(range(1, seq_len - 1))
    
    token_losses = [0.0] * seq_len
    
    loss_fct = torch.nn.CrossEntropyLoss(reduction='none')

    torch.cuda.empty_cache() # Clear memory before loop

    for i in range(0, len(indices_to_mask), BATCH_SIZE):
        batch_indices = indices_to_mask[i : i + BATCH_SIZE]
        current_curr_size = len(batch_indices)
        if current_curr_size == 0: break
        
        batch_input_ids = tensor_input_ids[:current_curr_size].clone()
        batch_labels = torch.full(batch_input_ids.shape, -100).to(device)
        
        for j, pos in enumerate(batch_indices):
            batch_labels[j, pos] = batch_input_ids[j, pos].item()
            batch_input_ids[j, pos] = tokenizer.mask_token_id
            
        with torch.no_grad():
            outputs = model(batch_input_ids)
            predictions = outputs.logits # [B, L, V]
            
        predictions = predictions.permute(0, 2, 1)
        loss = loss_fct(predictions, batch_labels) # [B, L]
        
        masked_losses = loss.sum(dim=1) # [B]
        
        for j, pos in enumerate(batch_indices):
            token_losses[pos] = masked_losses[j].item()

    # Identify ranges to highlight
    # Threshold: If loss is low (< approx 2.0 - 3.0), it's likely AI.
    # Let's say Threshold = 2.5 (similar to global threshold)
    # Adjusted to 1.0 (Strict) to avoid highlighting common human words.
    HIGHLIGHT_THRESHOLD = 1.0 
    
    highlights = []
    
    for i in range(1, seq_len - 1):
        loss = token_losses[i]
        # Lower loss = More likely AI
        if loss < HIGHLIGHT_THRESHOLD:
            start, end = offsets[i]
            # Filter out special tokens or empty offsets
            if start == end: continue
            highlights.append((start, end, loss))
            
    return highlights

def calculate_perplexity(text):
    """
    Calculates Pseudo-Perplexity (PPL) for Masked Language Models (like RoBERTa).
    Formula: PPL = exp( -1/N * sum( log(P(w_i | context)) ) )
    
    OPTIMIZATION: 
    - Strided Masking (Stride=3): Mask every 3rd token. 3x Speedup.
    - Batch Size: 16
    """
    t0 = time.time()
    
    # 1. Tokenize
    encodings = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    input_ids = encodings.input_ids.to(device) # Shape: [1, seq_len]
    seq_len = input_ids.shape[1]
    
    # If sequence is too short, return default
    if seq_len < 4:
        return 100.0

    nlls = [] # Negative Log Likelihoods
    
    # 2. Batched Masking for Efficiency
    BATCH_SIZE = 8
    
    # STRIDE: How many tokens to skip? 
    # Stride 1 = All tokens (Slowest, Most Accurate)
    # Stride 3 = Every 3rd token (3x Faster, Good Approx)
    STRIDE = 3
    
    tensor_input_ids = input_ids.repeat(BATCH_SIZE, 1) # [BATCH, seq_len]
    
    # Iterate through tokens [1...seq_len-2] to avoid CLS/SEP
    start_idx = 1
    end_idx = seq_len - 1
    
    loss_fct = torch.nn.CrossEntropyLoss(reduction='none')
    
    # Create list of indices to mask
    indices_to_mask = list(range(start_idx, end_idx, STRIDE))
    total_steps = len(indices_to_mask)
    
    for i in range(0, total_steps, BATCH_SIZE):
        # valid batch current step
        batch_indices = indices_to_mask[i : i + BATCH_SIZE]
        current_batch_size = len(batch_indices)
        
        if current_batch_size == 0: break
        
        # Prepare inputs: Clone the repeated text
        batch_input_ids = tensor_input_ids[:current_batch_size].clone()
        
        # Create labels: -100 means ignore index for loss calculation
        batch_labels = torch.full(batch_input_ids.shape, -100).to(device)
        
        # Mask the target tokens
        for j, token_pos in enumerate(batch_indices):
            # Save the original token ID as the label
            batch_labels[j, token_pos] = batch_input_ids[j, token_pos].item()
            # Replace input with [MASK]
            batch_input_ids[j, token_pos] = tokenizer.mask_token_id
            
        # Forward pass
        with torch.no_grad():
            outputs = model(batch_input_ids)
            predictions = outputs.logits # [batch, seq_len, vocab]
            
        # Calculate loss only for the masked positions
        predictions = predictions.permute(0, 2, 1)
        loss = loss_fct(predictions, batch_labels)
        
        # Get scalar loss for the masked token
        masked_losses = loss.sum(dim=1) # [current_batch_size]
        nlls.append(masked_losses)
        
    if not nlls:
        return 0.0
        
    # Stack all NLLs
    all_nlls = torch.cat(nlls)
    
    # Mean NLL
    mean_nll = all_nlls.mean()
    
    # Perplexity = exp(mean_nll)
    ppl = torch.exp(mean_nll)
    
    print(f"Inference Time: {time.time() - t0:.2f}s")
    return ppl.item()

class AnalyzeView(APIView):
    def post(self, request):
        if not model or not tokenizer:
            return Response({"error": "Model not loaded"}, status=status.HTTP_503_SERVICE_UNAVAILABLE)

        data = request.data
        text = data.get('text', '')
        if not text and 'file' in request.FILES:
             text = "File content placeholder" 

        if not text:
            return Response({"error": "No text provided"}, status=status.HTTP_400_BAD_REQUEST)

        try:
            ppl = calculate_perplexity(text)
            
            # PPL < 2.5 -> AI
            # PPL > 2.5 -> Human
            # Sigmoid Curve calibrated at 2.5
            # Formula: 100 / (1 + exp(3 * (ppl - 2.5)))
            
            ai_score = 100 / (1 + math.exp(3 * (ppl - 2.5)))
            
            # Clamp between 2 and 99
            ai_score = max(2.5, min(99.5, ai_score))
            
            label = "AI Generated" if ai_score > 50 else "Human Written"
            
            return Response({
                "score": round(ai_score, 1),
                "label": label,
                "perplexity": round(ppl, 2),
                "device": str(device)
            })

        except Exception as e:
            return Response({"error": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)

from django.http import FileResponse
from reportlab.lib.pagesizes import letter
from reportlab.lib import colors
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.enums import TA_JUSTIFY
import io

class ReportView(APIView):
    def post(self, request):
        try:
            data = request.data
            text = data.get('text', '')
            score = data.get('score', 0)
            label = data.get('label', 'Unknown')
            
            # Granular Highlighting Logic
            # 1. Get highlight ranges
            highlights = calculate_token_scores(text)
            
            # 2. Sort highlights by start index
            highlights.sort(key=lambda x: x[0])
            
            # 3. Construct XML tagged string
            # IMPORTANT: Escape text to prevent XML errors in ReportLab
            import html
            
            formatted_text = ""
            current_idx = 0
            
            # Simple greedy tagging
            print(f"Generating PDF for text length: {len(text)} with {len(highlights)} highlights.")
            
            for start, end, loss in highlights:
                if start < current_idx: continue # Skip overlaps
                
                # Append non-highlighted text
                segment = text[current_idx:start]
                formatted_text += html.escape(segment)
                
                # Append highlighted text
                segment = text[start:end]
                # XML tag for yellow background
                formatted_text += f'<font backColor="yellow">{html.escape(segment)}</font>'
                
                current_idx = end
                
            # Append remaining text
            formatted_text += html.escape(text[current_idx:])
            
            # Handle newlines for HTML
            formatted_text = formatted_text.replace('\n', '<br/>')
            
            # Create PDF
            buffer = io.BytesIO()
            doc = SimpleDocTemplate(buffer, pagesize=letter,
                                  rightMargin=72, leftMargin=72,
                                  topMargin=72, bottomMargin=18)
            
            Story = []
            
            styles = getSampleStyleSheet()
            styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY))
            
            # Header
            Story.append(Paragraph("DetectAI Analysis Report", styles["Heading1"]))
            Story.append(Paragraph(f"Date: {time.strftime('%Y-%m-%d %H:%M:%S')}", styles["Normal"]))
            Story.append(Spacer(1, 12))
            
            # Result
            # Calculate Highlighted Percentage
            total_chars = len(text)
            highlighted_chars = sum((h[1] - h[0]) for h in highlights)
            highlight_ratio = (highlighted_chars / total_chars * 100) if total_chars > 0 else 0
            
            # Display metrics
            res_color = "red" if score > 50 else "green"
            
            # Primary Metric: AI Probability
            Story.append(Paragraph(f'AI Probability: <font color="{res_color}"><b>{score}%</b></font>', styles["Heading2"]))
            
            # Secondary Metric: Highlighted Content
            Story.append(Paragraph(f'Highlighted Content: <b>{highlight_ratio:.1f}%</b>', styles["Normal"]))
            
            Story.append(Spacer(1, 12))
            
            # Content Header
            Story.append(Paragraph("Analyzed Content:", styles["Heading3"]))
            Story.append(Spacer(1, 6))
            
            # The Content (Highlighted)
            Story.append(Paragraph(formatted_text, styles["Justify"]))
            
            doc.build(Story)

            buffer.seek(0)
            return FileResponse(buffer, as_attachment=True, filename='detectAI_report.pdf')

        except Exception as e:
            print(f"Report Generation Error: {e}")
            return Response({"error": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)