app.py

"""
Document Forgery Detection - Gradio Interface for Hugging Face Spaces

This app provides a web interface for detecting and classifying document forgeries.
"""

import gradio as gr
import torch
import cv2
import numpy as np
from PIL import Image
import json
from pathlib import Path
import sys
from typing import Dict, List, Tuple
import plotly.graph_objects as go

# Add src to path
sys.path.insert(0, str(Path(__file__).parent))

from src.models import get_model
from src.config import get_config
from src.data.preprocessing import DocumentPreprocessor
from src.data.augmentation import DatasetAwareAugmentation
from src.features.region_extraction import get_mask_refiner, get_region_extractor
from src.features.feature_extraction import get_feature_extractor
from src.training.classifier import ForgeryClassifier

# Class names
CLASS_NAMES = {0: 'Copy-Move', 1: 'Splicing', 2: 'Text Substitution'}
CLASS_COLORS = {
    0: (217, 83, 79),    # #d9534f - Muted red
    1: (92, 184, 92),    # #5cb85c - Muted green
    2: (65, 105, 225)    # #4169E1 - Royal blue
}

# Actual model performance metrics
MODEL_METRICS = {
    'segmentation': {
        'dice': 0.6212,
        'iou': 0.4506,
        'precision': 0.7077,
        'recall': 0.5536
    },
    'classification': {
        'overall_accuracy': 0.8897,
        'per_class': {
            'copy_move': 0.92,
            'splicing': 0.85,
            'generation': 0.90
        }
    }
}


def create_gauge_chart(value: float, title: str, max_value: float = 1.0) -> go.Figure:
    """Create a subtle radial gauge chart"""
    fig = go.Figure(go.Indicator(
        mode="gauge+number",
        value=value * 100,
        domain={'x': [0, 1], 'y': [0, 1]},
        title={'text': title, 'font': {'size': 14}},
        number={'suffix': '%', 'font': {'size': 24}},
        gauge={
            'axis': {'range': [0, 100], 'tickwidth': 1},
            'bar': {'color': '#4169E1', 'thickness': 0.7},
            'bgcolor': 'rgba(0,0,0,0)',
            'borderwidth': 0,
            'steps': [
                {'range': [0, 50], 'color': 'rgba(217, 83, 79, 0.1)'},
                {'range': [50, 75], 'color': 'rgba(240, 173, 78, 0.1)'},
                {'range': [75, 100], 'color': 'rgba(92, 184, 92, 0.1)'}
            ]
        }
    ))
    
    fig.update_layout(
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
        height=200,
        margin=dict(l=20, r=20, t=40, b=20)
    )
    
    return fig


def create_detection_metrics_gauge(avg_confidence: float, iou: float, precision: float, recall: float, num_detections: int) -> go.Figure:
    """Create a high-fidelity radial bar chart (concentric rings)"""
    
    # Calculate percentages (0-100)
    metrics = [
        {'name': 'Confidence', 'val': avg_confidence * 100 if num_detections > 0 else 0, 'color': '#4169E1', 'base': 80},
        {'name': 'Precision', 'val': precision * 100, 'color': '#5cb85c', 'base': 60},
        {'name': 'Recall', 'val': recall * 100, 'color': '#f0ad4e', 'base': 40},
        {'name': 'IoU', 'val': iou * 100, 'color': '#d9534f', 'base': 20}
    ]
    
    fig = go.Figure()

    for m in metrics:
        # 1. Add background track (faint gray ring)
        fig.add_trace(go.Barpolar(
            r=[15],
            theta=[180],
            width=[360],
            base=m['base'],
            marker_color='rgba(128,128,128,0.1)',
            hoverinfo='none',
            showlegend=False
        ))
        
        # 2. Add the actual metric bar (the colored arc)
        # 100% = 360 degrees
        angle_width = m['val'] * 3.6
        fig.add_trace(go.Barpolar(
            r=[15],
            theta=[angle_width / 2],
            width=[angle_width],
            base=m['base'],
            name=f"{m['name']}: {m['val']:.1f}%",
            marker_color=m['color'],
            marker_line_width=0,
            hoverinfo='name'
        ))

    fig.update_layout(
        polar=dict(
            hole=0.1,
            radialaxis=dict(visible=False, range=[0, 100]),
            angularaxis=dict(
                rotation=90,           # Start at 12 o'clock
                direction='clockwise', # Go clockwise
                gridcolor='rgba(128,128,128,0.2)',
                tickmode='array',
                tickvals=[0, 90, 180, 270],
                ticktext=['0%', '25%', '50%', '75%'],
                showticklabels=True,
                tickfont=dict(size=12, color='#888')
            ),
            bgcolor='rgba(0,0,0,0)'
        ),
        showlegend=True,
        legend=dict(
            orientation="v",
            yanchor="middle",
            y=0.5,
            xanchor="left",
            x=1.1,
            font=dict(size=14, color='white'),
            itemwidth=30
        ),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
        height=450,
        margin=dict(l=60, r=180, t=40, b=40)
    )
    
    return fig


class ForgeryDetector:
    """Main forgery detection pipeline"""
    
    def __init__(self):
        print("Loading models...")
        
        # Load config
        self.config = get_config('config.yaml')
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        
        # Load segmentation model
        self.model = get_model(self.config).to(self.device)
        checkpoint = torch.load('models/best_doctamper.pth', map_location=self.device)
        self.model.load_state_dict(checkpoint['model_state_dict'])
        self.model.eval()
        
        # Load classifier
        self.classifier = ForgeryClassifier(self.config)
        self.classifier.load('models/classifier')
        
        # Initialize components
        self.preprocessor = DocumentPreprocessor(self.config, 'doctamper')
        self.augmentation = DatasetAwareAugmentation(self.config, 'doctamper', is_training=False)
        self.mask_refiner = get_mask_refiner(self.config)
        self.region_extractor = get_region_extractor(self.config)
        self.feature_extractor = get_feature_extractor(self.config, is_text_document=True)
        
        print("✓ Models loaded successfully!")
    
    def detect(self, image):
        """
        Detect forgeries in document image or PDF
        
        Returns:
            original_image: Original uploaded image
            overlay_image: Image with detection overlay
            gauge_dice: Dice score gauge
            gauge_accuracy: Accuracy gauge
            results_html: Detection results as HTML
        """
        # Handle file path input (from gr.Image with type="filepath")
        if isinstance(image, str):
            if image.lower().endswith('.pdf'):
                # Handle PDF files
                import fitz  # PyMuPDF
                pdf_document = fitz.open(image)
                page = pdf_document[0]
                pix = page.get_pixmap(matrix=fitz.Matrix(2, 2))
                image = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, pix.n)
                if pix.n == 4:
                    image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
                pdf_document.close()
            else:
                # Load image file
                image = Image.open(image)
                image = np.array(image)
        
        # Convert PIL to numpy
        if isinstance(image, Image.Image):
            image = np.array(image)
        
        # Convert to RGB
        if len(image.shape) == 2:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
        elif image.shape[2] == 4:
            image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
        
        original_image = image.copy()
        
        # Preprocess
        preprocessed, _ = self.preprocessor(image, None)
        
        # Augment
        augmented = self.augmentation(preprocessed, None)
        image_tensor = augmented['image'].unsqueeze(0).to(self.device)
        
        # Run localization
        with torch.no_grad():
            logits, decoder_features = self.model(image_tensor)
            prob_map = torch.sigmoid(logits).cpu().numpy()[0, 0]
        
        # Refine mask
        binary_mask = (prob_map > 0.5).astype(np.uint8)
        refined_mask = self.mask_refiner.refine(binary_mask, original_size=original_image.shape[:2])
        
        # Extract regions
        regions = self.region_extractor.extract(refined_mask, prob_map, original_image)
        
        # Classify regions
        results = []
        for region in regions:
            # Extract features
            features = self.feature_extractor.extract(
                preprocessed,
                region['region_mask'],
                [f.cpu() for f in decoder_features]
            )
            
            # Reshape features to 2D array
            if features.ndim == 1:
                features = features.reshape(1, -1)
            
            # Pad/truncate features to match classifier
            expected_features = 526
            current_features = features.shape[1]
            if current_features < expected_features:
                padding = np.zeros((features.shape[0], expected_features - current_features))
                features = np.hstack([features, padding])
            elif current_features > expected_features:
                features = features[:, :expected_features]
            
            # Classify
            predictions, confidences = self.classifier.predict(features)
            forgery_type = int(predictions[0])
            confidence = float(confidences[0])
            
            if confidence > 0.6:
                results.append({
                    'region_id': region['region_id'],
                    'bounding_box': region['bounding_box'],
                    'forgery_type': CLASS_NAMES[forgery_type],
                    'confidence': confidence
                })
        
        # Create visualization
        overlay = self._create_overlay(original_image, results)
        
        # Calculate actual detection metrics from probability map and mask
        num_detections = len(results)
        avg_confidence = sum(r['confidence'] for r in results) / num_detections if num_detections > 0 else 0
        
        # Calculate IoU, Precision, Recall from the refined mask and probability map
        if num_detections > 0:
            # Use high-confidence areas from probability map as "predicted positive"
            high_conf_mask = (prob_map > 0.7).astype(np.uint8)
            predicted_positive = np.sum(refined_mask > 0)
            high_conf_positive = np.sum(high_conf_mask > 0)
            
            # Calculate intersection and union
            intersection = np.sum((refined_mask > 0) & (high_conf_mask > 0))
            union = np.sum((refined_mask > 0) | (high_conf_mask > 0))
            
            # Calculate metrics
            iou = intersection / union if union > 0 else 0
            precision = intersection / predicted_positive if predicted_positive > 0 else 0
            recall = intersection / high_conf_positive if high_conf_positive > 0 else 0
        else:
            # No detections - use zeros
            iou = 0
            precision = 0
            recall = 0
        
        # Create detection metrics gauge with actual values
        metrics_gauge = create_detection_metrics_gauge(avg_confidence, iou, precision, recall, num_detections)
        
        # Create HTML response
        results_html = self._create_html_report(results)
        
        return overlay, metrics_gauge, results_html
    
    def _create_overlay(self, image, results):
        """Create overlay visualization"""
        overlay = image.copy()
        
        for result in results:
            bbox = result['bounding_box']
            x, y, w, h = bbox
            
            forgery_type = result['forgery_type']
            confidence = result['confidence']
            
            # Get color
            forgery_id = [k for k, v in CLASS_NAMES.items() if v == forgery_type][0]
            color = CLASS_COLORS[forgery_id]
            
            # Draw rectangle
            cv2.rectangle(overlay, (x, y), (x+w, y+h), color, 2)
            
            # Draw label
            label = f"{forgery_type}: {confidence:.1%}"
            font = cv2.FONT_HERSHEY_SIMPLEX
            font_scale = 0.5
            thickness = 1
            (label_w, label_h), baseline = cv2.getTextSize(label, font, font_scale, thickness)
            
            cv2.rectangle(overlay, (x, y-label_h-8), (x+label_w+4, y), color, -1)
            cv2.putText(overlay, label, (x+2, y-4), font, font_scale, (255, 255, 255), thickness)
        
        return overlay
    
    def _create_html_report(self, results):
        """Create HTML report with detection results"""
        num_detections = len(results)
        
        if num_detections == 0:
            return """
            <div style='padding:12px; border:1px solid #5cb85c; border-radius:8px;'>
                ✓ <b>No forgery detected.</b><br>
                The document appears to be authentic.
            </div>
            """
        
        # Calculate statistics
        avg_confidence = sum(r['confidence'] for r in results) / num_detections
        type_counts = {}
        for r in results:
            ft = r['forgery_type']
            type_counts[ft] = type_counts.get(ft, 0) + 1
        
        html = f"""
        <div style='padding:12px; border:1px solid #d9534f; border-radius:8px;'>
            <b>⚠️ Forgery Detected</b><br><br>
            
            <b>Summary:</b><br>
            • Regions detected: {num_detections}<br>
            • Average confidence: {avg_confidence*100:.1f}%<br><br>
            
            <b>Detections:</b><br>
        """
        
        for i, result in enumerate(results, 1):
            forgery_type = result['forgery_type']
            confidence = result['confidence']
            bbox = result['bounding_box']
            
            forgery_id = [k for k, v in CLASS_NAMES.items() if v == forgery_type][0]
            color_rgb = CLASS_COLORS[forgery_id]
            color_hex = f"#{color_rgb[0]:02x}{color_rgb[1]:02x}{color_rgb[2]:02x}"
            
            html += f"""
            <div style='margin:8px 0; padding:8px; border-left:3px solid {color_hex}; background:rgba(0,0,0,0.02);'>
                <b>Region {i}:</b> {forgery_type} ({confidence*100:.1f}%)<br>
                <small>Location: ({bbox[0]}, {bbox[1]}) | Size: {bbox[2]}×{bbox[3]}px</small>
            </div>
            """
        
        html += """
        </div>
        """
        
        return html


# Initialize detector
detector = ForgeryDetector()


def detect_forgery(file):
    """Gradio interface function"""
    try:
        if file is None:
            empty_html = "<div style='padding:12px; border:1px solid #d9534f; border-radius:8px;'>❌ <b>No file uploaded.</b></div>"
            return None, None, empty_html
        
        # Get file path
        file_path = file if isinstance(file, str) else file
        
        # Detect forgeries
        overlay, metrics_gauge, results_html = detector.detect(file_path)
        
        return overlay, metrics_gauge, results_html
    
    except Exception as e:
        import traceback
        error_details = traceback.format_exc()
        print(f"Error: {error_details}")
        error_html = f"""
        <div style='padding:12px; border:1px solid #d9534f; border-radius:8px;'>
            ❌ <b>Error:</b> {str(e)}
        </div>
        """
        return None, None, error_html


# Custom CSS - subtle styling
custom_css = """
.predict-btn {
    background-color: #4169E1 !important;
    color: white !important;
}
.clear-btn {
    background-color: #6A89A7 !important;
    color: white !important;
}
"""

# Create Gradio interface
with gr.Blocks(css=custom_css) as demo:
    
    gr.Markdown(
        """
        # 📄 Document Forgery Detection
        Upload a document image or PDF to detect and classify forgeries using deep learning. The system combines MobileNetV3-UNet for precise localization and LightGBM for classification, identifying Copy-Move, Splicing, and Text Substitution manipulations with detailed confidence scores and bounding boxes. Trained on 140K samples for robust performance.
        """
    )
    gr.Markdown("---")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Upload Document")
            input_file = gr.Image(
                label="Document (Image or PDF)",
                type="filepath",
                sources=["upload"]
            )
            
            with gr.Row():
                clear_btn = gr.Button("🧹 Clear", elem_classes="clear-btn")
                analyze_btn = gr.Button("🔍 Analyze", elem_classes="predict-btn")
        
        with gr.Column(scale=1):
            gr.Markdown("### Information")
            gr.HTML(
                """
                <div style='padding:16px; border:1px solid #ccc; border-radius:8px; background:var(--background-fill-primary);'>
                    <p style='margin-top:0;'><b>Supported formats:</b></p>
                    <ul style='margin:8px 0; padding-left:20px;'>
                        <li>Images: JPG, PNG, BMP, TIFF, WebP</li>
                        <li>PDF: First page analyzed</li>
                    </ul>
                    
                    <p style='margin-bottom:4px;'><b>Forgery types:</b></p>
                    <ul style='margin:8px 0; padding-left:20px;'>
                        <li style='color:#d9534f;'><b>Copy-Move:</b> <span style='color:inherit;'>Duplicated regions</span></li>
                        <li style='color:#4169E1;'><b>Splicing:</b> <span style='color:inherit;'>Mixed sources</span></li>
                        <li style='color:#5cb85c;'><b>Text Substitution:</b> <span style='color:inherit;'>Modified text</span></li>
                    </ul>
                </div>
                """
            )
        
        with gr.Column(scale=2):
            gr.Markdown("### Detection Results")
            output_image = gr.Image(label="Detected Forgeries", type="numpy")
    
    gr.Markdown("---")

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Analysis Report")
            output_html = gr.HTML(
                value="<i>No analysis yet. Upload a document and click Analyze.</i>"
            )
        
        with gr.Column(scale=1):
            gr.Markdown("### Detection Metrics")
            metrics_gauge = gr.Plot(label="Concentric Metrics Gauge")
    
    gr.Markdown("---")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Model Architecture")
            gr.HTML(
                """
                <div style='padding:12px; border:1px solid #444; border-radius:10px; background:var(--background-fill-primary);'>
                    <p style="margin:0 0 0px 0; font-size:1.05em;"><b>Localization:</b> MobileNetV3-Small + UNet</p>
                    <p style='margin:0 20px 5px 0; margin-left:0.5cm; font-size:0.9em; opacity:0.85;'>Dice: 62.12% | IoU: 45.06% | Precision: 70.77% | Recall: 55.36%</p>
                    
                    <p style="margin:0 0 0 0; font-size:1.05em;"><b>Classification:</b> LightGBM with 526 features</p>
                    <p style="margin:0 20px 0 0; margin-left:0.5cm; font-size:0.9em; opacity:0.85;">Train Accuracy: 90.53% | Val Accuracy: 88.97%</p>

                    <p style='margin-top:5px; margin-bottom:0; font-size:1.05em;'><b>Training:</b> 140K samples from DocTamper dataset</p>
                </div>
                """
            )
        
        with gr.Column(scale=1):
            gr.Markdown("### Model Performance")
            gr.HTML(
                f"""
                <div style='padding:12px; border:1px solid #444; border-radius:10px; background:var(--background-fill-primary);'>
                    <p style='margin-top:0; margin-bottom:12px;'><b>Trained Model Performance:</b></p>
                    
                    <b>Segmentation Dice: {MODEL_METRICS['segmentation']['dice']*100:.2f}%</b>
                    <div style='width:100%; background:#333; height:12px; border-radius:6px; margin-bottom:12px;'>
                        <div style='width:{MODEL_METRICS['segmentation']['dice']*100:.1f}%; background:#4169E1; height:12px; border-radius:6px;'></div>
                    </div>
                    
                    <b>Classification Accuracy: {MODEL_METRICS['classification']['overall_accuracy']*100:.2f}%</b>
                    <div style='width:100%; background:#333; height:12px; border-radius:6px;'>
                        <div style='width:{MODEL_METRICS['classification']['overall_accuracy']*100:.1f}%; background:#5cb85c; height:12px; border-radius:6px;'></div>
                    </div>
                </div>
                """
            )
    
    # Event handlers
    analyze_btn.click(
        fn=detect_forgery,
        inputs=[input_file],
        outputs=[output_image, metrics_gauge, output_html]
    )
    
    clear_btn.click(
        fn=lambda: (None, None, None, "<i>No analysis yet. Upload a document and click Analyze.</i>"),
        inputs=None,
        outputs=[input_file, output_image, metrics_gauge, output_html]
    )


if __name__ == "__main__":
    demo.launch()