import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
import spacy
from typing import List, Dict, Tuple
import logging
import os
import gradio as gr
from fastapi.middleware.cors import CORSMiddleware
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import time
import csv
import os
from datetime import datetime
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Constants
MAX_LENGTH = 512
MODEL_NAME = "microsoft/deberta-v3-small"
WINDOW_SIZE = 6
WINDOW_OVERLAP = 2
CONFIDENCE_THRESHOLD = 0.65
BATCH_SIZE = 8  # Reduced batch size for CPU
MAX_WORKERS = 4  # Number of worker threads for processing


def log_prediction_data(input_text, word_count, prediction, confidence, execution_time, mode):
    """Log prediction data to a CSV file in the /tmp directory."""
    # Define the CSV file path
    csv_path = "/tmp/prediction_logs.csv"
   
    # Check if file exists to determine if we need to write headers
    file_exists = os.path.isfile(csv_path)
   
    try:
        with open(csv_path, 'a', newline='', encoding='utf-8') as f:
            writer = csv.writer(f)
           
            # Write headers if the file is newly created
            if not file_exists:
                writer.writerow(["timestamp", "word_count", "prediction", "confidence", "execution_time_ms", "analysis_mode", "full_text"])
           
            # Clean up the input text for CSV storage (replace newlines with spaces)
            cleaned_text = input_text.replace("\n", " ")
           
            # Write the data row with the full text
            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
            writer.writerow([timestamp, word_count, prediction, f"{confidence:.2f}", f"{execution_time:.2f}", mode, cleaned_text])
           
        logger.info(f"Successfully logged prediction data to {csv_path}")
        return True
    except Exception as e:
        logger.error(f"Error logging prediction data: {str(e)}")
        return False


class TextWindowProcessor:
    def __init__(self):
        try:
            self.nlp = spacy.load("en_core_web_sm")
        except OSError:
            logger.info("Downloading spacy model...")
            spacy.cli.download("en_core_web_sm")
            self.nlp = spacy.load("en_core_web_sm")

        if 'sentencizer' not in self.nlp.pipe_names:
            self.nlp.add_pipe('sentencizer')

        disabled_pipes = [pipe for pipe in self.nlp.pipe_names if pipe != 'sentencizer']
        self.nlp.disable_pipes(*disabled_pipes)
        
        # Initialize thread pool for parallel processing
        self.executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)

    def split_into_sentences(self, text: str) -> List[str]:
        doc = self.nlp(text)
        return [str(sent).strip() for sent in doc.sents]

    def create_windows(self, sentences: List[str], window_size: int, overlap: int) -> List[str]:
        if len(sentences) < window_size:
            return [" ".join(sentences)]

        windows = []
        stride = window_size - overlap
        for i in range(0, len(sentences) - window_size + 1, stride):
            window = sentences[i:i + window_size]
            windows.append(" ".join(window))
        return windows

    def create_centered_windows(self, sentences: List[str], window_size: int) -> Tuple[List[str], List[List[int]]]:
        """Create windows with better boundary handling"""
        windows = []
        window_sentence_indices = []

        for i in range(len(sentences)):
            # Calculate window boundaries centered on current sentence
            half_window = window_size // 2
            start_idx = max(0, i - half_window)
            end_idx = min(len(sentences), i + half_window + 1)

            # Create the window
            window = sentences[start_idx:end_idx]
            windows.append(" ".join(window))
            window_sentence_indices.append(list(range(start_idx, end_idx)))

        return windows, window_sentence_indices

class TextClassifier:
    def __init__(self):
        # Set thread configuration before any model loading or parallel work
        if not torch.cuda.is_available():
            torch.set_num_threads(MAX_WORKERS)
            torch.set_num_interop_threads(MAX_WORKERS)
            
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model_name = MODEL_NAME
        self.tokenizer = None
        self.model = None
        self.processor = TextWindowProcessor()
        self.initialize_model()

    def initialize_model(self):
        """Initialize the model and tokenizer."""
        logger.info("Initializing model and tokenizer...")
        
        from transformers import DebertaV2TokenizerFast
        
        self.tokenizer = DebertaV2TokenizerFast.from_pretrained(
            self.model_name,
            model_max_length=MAX_LENGTH,
            use_fast=True
        )
        
        self.model = AutoModelForSequenceClassification.from_pretrained(
            self.model_name,
            num_labels=2
        ).to(self.device)
            
        model_path = "model_20250209_184929_acc1.0000.pt"
        if os.path.exists(model_path):
            logger.info(f"Loading custom model from {model_path}")
            checkpoint = torch.load(model_path, map_location=self.device)
            self.model.load_state_dict(checkpoint['model_state_dict'])
        else:
            logger.warning("Custom model file not found. Using base model.")
            
        self.model.eval()

    def quick_scan(self, text: str) -> Dict:
        """Perform a quick scan using simple window analysis."""
        if not text.strip():
            return {
                'prediction': 'unknown',
                'confidence': 0.0,
                'num_windows': 0
            }

        sentences = self.processor.split_into_sentences(text)
        windows = self.processor.create_windows(sentences, WINDOW_SIZE, WINDOW_OVERLAP)

        predictions = []
        
        # Process windows in smaller batches for CPU efficiency
        for i in range(0, len(windows), BATCH_SIZE):
            batch_windows = windows[i:i + BATCH_SIZE]

            inputs = self.tokenizer(
                batch_windows,
                truncation=True,
                padding=True,
                max_length=MAX_LENGTH,
                return_tensors="pt"
            ).to(self.device)

            with torch.no_grad():
                outputs = self.model(**inputs)
                probs = F.softmax(outputs.logits, dim=-1)

                for idx, window in enumerate(batch_windows):
                    prediction = {
                        'window': window,
                        'human_prob': probs[idx][1].item(),
                        'ai_prob': probs[idx][0].item(),
                        'prediction': 'human' if probs[idx][1] > probs[idx][0] else 'ai'
                    }
                    predictions.append(prediction)

            # Clean up GPU memory if available
            del inputs, outputs, probs
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

        if not predictions:
            return {
                'prediction': 'unknown',
                'confidence': 0.0,
                'num_windows': 0
            }

        avg_human_prob = sum(p['human_prob'] for p in predictions) / len(predictions)
        avg_ai_prob = sum(p['ai_prob'] for p in predictions) / len(predictions)

        return {
            'prediction': 'human' if avg_human_prob > avg_ai_prob else 'ai',
            'confidence': max(avg_human_prob, avg_ai_prob),
            'num_windows': len(predictions)
        }

    # def detailed_scan(self, text: str) -> Dict:
    #     """Original prediction method with modified window handling"""
    #     if self.model is None or self.tokenizer is None:
    #         self.load_model()

    #     self.model.eval()
    #     sentences = self.processor.split_into_sentences(text)
    #     if not sentences:
    #         return {}

    #     # Create centered windows for each sentence
    #     windows, window_sentence_indices = self.processor.create_centered_windows(sentences, WINDOW_SIZE)

    #     # Track scores for each sentence
    #     sentence_appearances = {i: 0 for i in range(len(sentences))}
    #     sentence_scores = {i: {'human_prob': 0.0, 'ai_prob': 0.0} for i in range(len(sentences))}

    #     # Process windows in batches
    #     batch_size = 16
    #     for i in range(0, len(windows), batch_size):
    #         batch_windows = windows[i:i + batch_size]
    #         batch_indices = window_sentence_indices[i:i + batch_size]

    #         inputs = self.tokenizer(
    #             batch_windows,
    #             truncation=True,
    #             padding=True,
    #             max_length=MAX_LENGTH,
    #             return_tensors="pt"
    #         ).to(self.device)

    #         with torch.no_grad():
    #             outputs = self.model(**inputs)
    #             probs = F.softmax(outputs.logits, dim=-1)

    #             # Attribute predictions more carefully
    #             for window_idx, indices in enumerate(batch_indices):
    #                 center_idx = len(indices) // 2
    #                 center_weight = 0.7  # Higher weight for center sentence
    #                 edge_weight = 0.3 / (len(indices) - 1)  # Distribute remaining weight

    #                 for pos, sent_idx in enumerate(indices):
    #                     # Apply higher weight to center sentence
    #                     weight = center_weight if pos == center_idx else edge_weight
    #                     sentence_appearances[sent_idx] += weight
    #                     sentence_scores[sent_idx]['human_prob'] += weight * probs[window_idx][1].item()
    #                     sentence_scores[sent_idx]['ai_prob'] += weight * probs[window_idx][0].item()

    #         del inputs, outputs, probs
    #         if torch.cuda.is_available():
    #             torch.cuda.empty_cache()

    #     # Calculate final predictions
    #     sentence_predictions = []
    #     for i in range(len(sentences)):
    #         if sentence_appearances[i] > 0:
    #             human_prob = sentence_scores[i]['human_prob'] / sentence_appearances[i]
    #             ai_prob = sentence_scores[i]['ai_prob'] / sentence_appearances[i]

    #             # Only apply minimal smoothing at prediction boundaries
    #             if i > 0 and i < len(sentences) - 1:
    #                 prev_human = sentence_scores[i-1]['human_prob'] / sentence_appearances[i-1]
    #                 prev_ai = sentence_scores[i-1]['ai_prob'] / sentence_appearances[i-1]
    #                 next_human = sentence_scores[i+1]['human_prob'] / sentence_appearances[i+1]
    #                 next_ai = sentence_scores[i+1]['ai_prob'] / sentence_appearances[i+1]

    #                 # Check if we're at a prediction boundary
    #                 current_pred = 'human' if human_prob > ai_prob else 'ai'
    #                 prev_pred = 'human' if prev_human > prev_ai else 'ai'
    #                 next_pred = 'human' if next_human > next_ai else 'ai'

    #                 if current_pred != prev_pred or current_pred != next_pred:
    #                     # Small adjustment at boundaries
    #                     smooth_factor = 0.1
    #                     human_prob = (human_prob * (1 - smooth_factor) + 
    #                                 (prev_human + next_human) * smooth_factor / 2)
    #                     ai_prob = (ai_prob * (1 - smooth_factor) + 
    #                             (prev_ai + next_ai) * smooth_factor / 2)

    #             sentence_predictions.append({
    #                 'sentence': sentences[i],
    #                 'human_prob': human_prob,
    #                 'ai_prob': ai_prob,
    #                 'prediction': 'human' if human_prob > ai_prob else 'ai',
    #                 'confidence': max(human_prob, ai_prob)
    #             })

    #     return {
    #         'sentence_predictions': sentence_predictions,
    #         'highlighted_text': self.format_predictions_html(sentence_predictions),
    #         'full_text': text,
    #         'overall_prediction': self.aggregate_predictions(sentence_predictions)
    #     }

    def detailed_scan(self, text: str) -> Dict:
        """Perform a detailed scan with improved sentence-level analysis."""
        # Clean up trailing whitespace
        text = text.rstrip()
        
        if not text.strip():
            return {
                'sentence_predictions': [],
                'highlighted_text': '',
                'full_text': '',
                'overall_prediction': {
                    'prediction': 'unknown',
                    'confidence': 0.0,
                    'num_sentences': 0
                }
            }

        sentences = self.processor.split_into_sentences(text)
        if not sentences:
            return {}

        # Create centered windows for each sentence
        windows, window_sentence_indices = self.processor.create_centered_windows(sentences, WINDOW_SIZE)

        # Track scores for each sentence
        sentence_appearances = {i: 0 for i in range(len(sentences))}
        sentence_scores = {i: {'human_prob': 0.0, 'ai_prob': 0.0} for i in range(len(sentences))}

        # Process windows in batches
        for i in range(0, len(windows), BATCH_SIZE):
            batch_windows = windows[i:i + BATCH_SIZE]
            batch_indices = window_sentence_indices[i:i + BATCH_SIZE]

            inputs = self.tokenizer(
                batch_windows,
                truncation=True,
                padding=True,
                max_length=MAX_LENGTH,
                return_tensors="pt"
            ).to(self.device)

            with torch.no_grad():
                outputs = self.model(**inputs)
                probs = F.softmax(outputs.logits, dim=-1)

                # Attribute predictions with weighted scoring
                for window_idx, indices in enumerate(batch_indices):
                    center_idx = len(indices) // 2
                    center_weight = 0.7  # Higher weight for center sentence
                    edge_weight = 0.3 / (len(indices) - 1)  # Distribute remaining weight

                    for pos, sent_idx in enumerate(indices):
                        # Apply higher weight to center sentence
                        weight = center_weight if pos == center_idx else edge_weight
                        sentence_appearances[sent_idx] += weight
                        sentence_scores[sent_idx]['human_prob'] += weight * probs[window_idx][1].item()
                        sentence_scores[sent_idx]['ai_prob'] += weight * probs[window_idx][0].item()

            # Clean up memory
            del inputs, outputs, probs
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

        # Calculate final predictions with boundary smoothing
        sentence_predictions = []
        for i in range(len(sentences)):
            if sentence_appearances[i] > 0:
                human_prob = sentence_scores[i]['human_prob'] / sentence_appearances[i]
                ai_prob = sentence_scores[i]['ai_prob'] / sentence_appearances[i]

                # Apply minimal smoothing at prediction boundaries
                if i > 0 and i < len(sentences) - 1:
                    prev_human = sentence_scores[i-1]['human_prob'] / sentence_appearances[i-1]
                    prev_ai = sentence_scores[i-1]['ai_prob'] / sentence_appearances[i-1]
                    next_human = sentence_scores[i+1]['human_prob'] / sentence_appearances[i+1]
                    next_ai = sentence_scores[i+1]['ai_prob'] / sentence_appearances[i+1]

                    # Check if we're at a prediction boundary
                    current_pred = 'human' if human_prob > ai_prob else 'ai'
                    prev_pred = 'human' if prev_human > prev_ai else 'ai'
                    next_pred = 'human' if next_human > next_ai else 'ai'

                    if current_pred != prev_pred or current_pred != next_pred:
                        # Small adjustment at boundaries
                        smooth_factor = 0.1
                        human_prob = (human_prob * (1 - smooth_factor) + 
                                    (prev_human + next_human) * smooth_factor / 2)
                        ai_prob = (ai_prob * (1 - smooth_factor) + 
                                (prev_ai + next_ai) * smooth_factor / 2)

                sentence_predictions.append({
                    'sentence': sentences[i],
                    'human_prob': human_prob,
                    'ai_prob': ai_prob,
                    'prediction': 'human' if human_prob > ai_prob else 'ai',
                    'confidence': max(human_prob, ai_prob)
                })

        return {
            'sentence_predictions': sentence_predictions,
            'highlighted_text': self.format_predictions_html(sentence_predictions),
            'full_text': text,
            'overall_prediction': self.aggregate_predictions(sentence_predictions)
        }

    def format_predictions_html(self, sentence_predictions: List[Dict]) -> str:
        """Format predictions as HTML with color-coding."""
        html_parts = []
        
        for pred in sentence_predictions:
            sentence = pred['sentence']
            confidence = pred['confidence']
            
            if confidence >= CONFIDENCE_THRESHOLD:
                if pred['prediction'] == 'human':
                    color = "#90EE90"  # Light green
                else:
                    color = "#FFB6C6"  # Light red
            else:
                if pred['prediction'] == 'human':
                    color = "#E8F5E9"  # Very light green
                else:
                    color = "#FFEBEE"  # Very light red
                    
            html_parts.append(f'<span style="background-color: {color};">{sentence}</span>')
            
        return " ".join(html_parts)

    def aggregate_predictions(self, predictions: List[Dict]) -> Dict:
        """Aggregate predictions from multiple sentences into a single prediction."""
        if not predictions:
            return {
                'prediction': 'unknown',
                'confidence': 0.0,
                'num_sentences': 0
            }

        total_human_prob = sum(p['human_prob'] for p in predictions)
        total_ai_prob = sum(p['ai_prob'] for p in predictions)
        num_sentences = len(predictions)

        avg_human_prob = total_human_prob / num_sentences
        avg_ai_prob = total_ai_prob / num_sentences

        return {
            'prediction': 'human' if avg_human_prob > avg_ai_prob else 'ai',
            'confidence': max(avg_human_prob, avg_ai_prob),
            'num_sentences': num_sentences
        }


    def analyze_text(text: str, mode: str, classifier: TextClassifier) -> tuple:
        """Analyze text using specified mode and return formatted results."""
        # Start timing
        start_time = time.time()
       
        # Count words in the text
        word_count = len(text.split())
       
        # If text is less than 200 words and detailed mode is selected, switch to quick mode
        original_mode = mode
        if word_count < 200 and mode == "detailed":
            mode = "quick"
       
        if mode == "quick":
            result = classifier.quick_scan(text)
           
            quick_analysis = f"""
            PREDICTION: {result['prediction'].upper()}
            Confidence: {result['confidence']*100:.1f}%
            Windows analyzed: {result['num_windows']}
            """
           
            # Add note if mode was switched
            if original_mode == "detailed":
                quick_analysis += f"\n\nNote: Switched to quick mode because text contains only {word_count} words. Minimum 200 words required for detailed analysis."
           
            # Calculate execution time in milliseconds
            execution_time = (time.time() - start_time) * 1000
           
            # Log the prediction data
            log_prediction_data(
                input_text=text,
                word_count=word_count,
                prediction=result['prediction'],
                confidence=result['confidence'],
                execution_time=execution_time,
                mode=original_mode
            )
           
            return (
                text,  # No highlighting in quick mode
                "Quick scan mode - no sentence-level analysis available",
                quick_analysis
            )
        else:
            analysis = classifier.detailed_scan(text)
           
            detailed_analysis = []
            for pred in analysis['sentence_predictions']:
                confidence = pred['confidence'] * 100
                detailed_analysis.append(f"Sentence: {pred['sentence']}")
                detailed_analysis.append(f"Prediction: {pred['prediction'].upper()}")
                detailed_analysis.append(f"Confidence: {confidence:.1f}%")
                detailed_analysis.append("-" * 50)
           
            final_pred = analysis['overall_prediction']
            overall_result = f"""
            FINAL PREDICTION: {final_pred['prediction'].upper()}
            Overall confidence: {final_pred['confidence']*100:.1f}%
            Number of sentences analyzed: {final_pred['num_sentences']}
            """
           
            # Calculate execution time in milliseconds
            execution_time = (time.time() - start_time) * 1000
           
            # Log the prediction data
            log_prediction_data(
                input_text=text,
                word_count=word_count,
                prediction=final_pred['prediction'],
                confidence=final_pred['confidence'],
                execution_time=execution_time,
                mode=original_mode
            )
           
            return (
                analysis['highlighted_text'],
                "\n".join(detailed_analysis),
                overall_result
            )        


# Initialize the classifier globally
classifier = TextClassifier()

# Create Gradio interface
demo = gr.Interface(
    fn=lambda text, mode: analyze_text(text, mode, classifier),
    inputs=[
        gr.Textbox(
            lines=8,
            placeholder="Enter text to analyze...",
            label="Input Text"
        ),
        gr.Radio(
            choices=["quick", "detailed"],
            value="quick",
            label="Analysis Mode",
            info="Quick mode for faster analysis, Detailed mode for sentence-level analysis"
        )
    ],
    outputs=[
        gr.HTML(label="Highlighted Analysis"),
        gr.Textbox(label="Sentence-by-Sentence Analysis", lines=10),
        gr.Textbox(label="Overall Result", lines=4)
    ],
    title="AI Text Detector",
    description="Analyze text to detect if it was written by a human or AI. Choose between quick scan and detailed sentence-level analysis. 200+ words suggested for accurate predictions.",
    api_name="predict",
    flagging_mode="never"
)

app = demo.app
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # For development
    allow_credentials=True,
    allow_methods=["GET", "POST", "OPTIONS"],  # Explicitly list methods
    allow_headers=["*"],
)

# Ensure CORS is applied before launching
if __name__ == "__main__":
    demo.queue()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )