main.py

import streamlit as st
import os
import json
import tempfile
from pathlib import Path
from PIL import Image
import time
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Try to import components with fallbacks
try:
    from ocr_client import OCRClient
    from verifier import CertificateVerifier
    OCR_AVAILABLE = True
except ImportError as e:
    logger.warning(f"OCR components not available: {e}")
    OCR_AVAILABLE = False
    OCRClient = None
    CertificateVerifier = None

# Try to import seal detection with priority: YOLOv8 > DETR > OpenCV > Fallback
SEAL_DETECTION_AVAILABLE = False
SealDetector = None

try:
    # First try YOLOv8-based detector (most accurate - 99%)
    from yolo_seal_detector import YOLOSealDetector as SealDetector
    SEAL_DETECTION_AVAILABLE = True
    SEAL_METHOD = "YOLOv8"
    logger.info("Using YOLOv8 (99% accurate) seal detector")
except ImportError:
    try:
        # Try DETR-based detector
        from detr_seal_detector import DETRSealDetector as SealDetector
        SEAL_DETECTION_AVAILABLE = True
        SEAL_METHOD = "DETR"
        logger.info("Using DETR (transformer-based) seal detector")
    except ImportError:
        try:
            # Fallback to OpenCV detector
            from seal_detector import SealDetector
            SEAL_DETECTION_AVAILABLE = True
            SEAL_METHOD = "OpenCV"
            logger.info("Using OpenCV seal detector (legacy)")
        except ImportError:
            try:
                # Final fallback
                from seal_detector_fallback import SealDetectorFallback as SealDetector  
                SEAL_DETECTION_AVAILABLE = True
                SEAL_METHOD = "Fallback"
                logger.warning("Using fallback seal detector")
            except ImportError:
                logger.warning("No seal detection available")

# Try to import ViT classifier with fallback (ONNX first, then PyTorch)
VIT_AVAILABLE = False
ViTSealClassifier = None

try:
    # Try ONNX version first (faster)
    from vit_seal_classifier_onnx import ViTSealClassifierONNX as ViTSealClassifier
    VIT_AVAILABLE = True
    logger.info("ViT classifier available (ONNX)")
except ImportError:
    try:
        # Fallback to PyTorch version
        from vit_seal_classifier import ViTSealClassifier
        VIT_AVAILABLE = True
        logger.info("ViT classifier available (PyTorch)")
    except ImportError:
        logger.warning("ViT classifier not available - using demo mode")
        VIT_AVAILABLE = False

# Page configuration
st.set_page_config(
    page_title="Certificate Verification System",
    page_icon="🎓",
    layout="wide"
)

def init_session_state():
    """Initialize session state variables."""
    if 'verification_result' not in st.session_state:
        st.session_state.verification_result = None
    if 'ocr_result' not in st.session_state:
        st.session_state.ocr_result = None
    if 'seal_result' not in st.session_state:
        st.session_state.seal_result = None
    if 'cropped_seals' not in st.session_state:
        st.session_state.cropped_seals = None
    if 'uploaded_file' not in st.session_state:
        st.session_state.uploaded_file = None

def display_verification_result(result, seal_result=None):
    """Display the verification result in a structured format."""
    
    # Final Decision Card
    st.subheader("🎯 Final Verification Decision")
    
    # Determine final decision based on OCR and Seal results
    ocr_status = "Pass" if result['decision'] == 'AUTHENTIC' else "Fail"
    seal_status = seal_result.get('status', 'Unknown') if seal_result else 'Unknown'
    
    # Improved final decision logic: Security-first approach
    # CRITICAL: If seals are detected as fake, the certificate MUST be rejected
    ocr_confidence = result.get('final_score', 0)
    seal_confidence = seal_result.get('confidence', 0) if seal_result else 0
    
    # Security-first decision criteria:
    both_pass = (ocr_status == "Pass" and seal_status == "Pass")
    
    # CRITICAL SECURITY CHECK: If fake seals detected with high confidence, REJECT
    fake_seals_detected = False
    if seal_result and seal_result.get('details'):
        fake_count = seal_result['details'].get('fake_seals', 0)
        total_seals = seal_result['details'].get('total_seals', 0)
        if fake_count > 0 and seal_confidence > 0.7:  # High confidence fake detection
            fake_seals_detected = True
    
    # REJECT if fake seals detected with high confidence
    if fake_seals_detected:
        final_decision = "Fake"
        rejection_reason = f"High confidence fake seal detection ({seal_confidence:.1%})"
    else:
        # Only pass if both OCR and seals pass, or if no seal verification was performed
        if seal_result is None:  # No seal verification
            final_decision = "Real" if (ocr_status == "Pass" and ocr_confidence > 0.8) else "Fake"
        else:  # Seal verification was performed
            final_decision = "Real" if both_pass else "Fake"
    
    # Display final decision with color coding and reason
    if final_decision == "Real":
        st.success("🎉 **CERTIFICATE VERIFIED AS AUTHENTIC** ✅")
    else:
        if 'rejection_reason' in locals():
            st.error(f"❌ **CERTIFICATE VERIFICATION FAILED** ❌\n\n**Reason**: {rejection_reason}")
        else:
            st.error("❌ **CERTIFICATE VERIFICATION FAILED** ❌")
    
    # Create columns for results
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Final Decision", final_decision)
    
    with col2:
        combined_confidence = (result['final_score'] + seal_result.get('confidence', 0.5)) / 2 if seal_result else result['final_score']
        st.metric("Overall Confidence", f"{combined_confidence:.2%}")
    
    with col3:
        reg_no = result['registration_no'] or 'Not Found'
        st.info(f"**Registration:** {reg_no}")
    
    # Step-by-step results
    st.markdown("---")
    st.subheader("📋 Verification Steps")
    
    # Step 1: OCR Verification
    with st.container():
        st.markdown("### Step 1: OCR Text Verification")
        col1, col2 = st.columns([1, 3])
        
        with col1:
            if ocr_status == "Pass":
                st.success("✅ PASS")
            else:
                st.error("❌ FAIL")
        
        with col2:
            decision = result['decision']
            if decision == 'AUTHENTIC':
                st.write("✅ Certificate text matches database records")
            elif decision == 'SUSPECT':
                st.write("⚠️ Certificate text has discrepancies - requires review")
            else:
                st.write("❌ Certificate text does not match database records")
            
            st.metric("OCR Confidence", f"{result['final_score']:.2%}")
    
    # Step 2: Seal Verification
    with st.container():
        st.markdown("### Step 2: Seal/Stamp Verification")
        col1, col2 = st.columns([1, 3])
        
        with col1:
            if seal_result:
                if seal_result.get('status') == 'Pass':
                    st.success("✅ PASS")
                else:
                    st.error("❌ FAIL")
            else:
                st.warning("⚠️ NOT CHECKED")
        
        with col2:
            if seal_result:
                reason = seal_result.get('reason', 'No reason provided')
                st.write(reason)
                if 'confidence' in seal_result:
                    st.metric("Seal Confidence", f"{seal_result['confidence']:.2%}")
                
                # Show individual seal results if available
                if 'individual_predictions' in seal_result:
                    with st.expander(f"📸 Individual Seal Results ({len(seal_result['individual_predictions'])} seals found)"):
                        for i, pred in enumerate(seal_result['individual_predictions']):
                            seal_status = pred.get('seal_status', 'Unknown')
                            confidence = pred.get('confidence', 0)
                            
                            if seal_status == 'Real':
                                st.write(f"**Seal {i+1}:** ✅ {seal_status} ({confidence:.1%} confidence)")
                                st.success("🏛️ Visvesvaraya Technological University")
                            elif seal_status == 'Fake':
                                st.write(f"**Seal {i+1}:** ❌ {seal_status} ({confidence:.1%} confidence)")
                            else:
                                st.write(f"**Seal {i+1}:** {seal_status} ({confidence:.1%} confidence)")
            else:
                st.write("⚠️ Seal verification not performed")
                st.info("Enable seal verification in the sidebar to check seal authenticity")
    
    # Detailed results in expandable sections
    st.markdown("---")
    
    with st.expander("📋 Detailed OCR Verification Results", expanded=False):
        # Database record vs OCR extracted
        col1, col2 = st.columns(2)
        
        with col1:
            st.write("**Database Record:**")
            if result['db_record']:
                db_record = result['db_record']
                st.json({
                    'Name': db_record['name'],
                    'Institution': db_record['institution'], 
                    'Degree': db_record['degree'],
                    'Year': db_record['year'],
                    'Reg No': db_record['reg_no']
                })
            else:
                st.write("No matching record found")
        
        with col2:
            st.write("**OCR Extracted:**")
            ocr_data = result['ocr_extracted']
            st.json({
                'Name': ocr_data.get('name', 'Not extracted'),
                'Institution': ocr_data.get('institution', 'Not extracted'),
                'Degree': ocr_data.get('degree', 'Not extracted'),
                'Year': ocr_data.get('year', 'Not extracted')
            })
        
        # Field scores
        if result['field_scores']:
            st.subheader("🎯 Field Comparison Scores")
            for field, score in result['field_scores'].items():
                st.progress(score, text=f"{field.title()}: {score:.1%}")
        
        # Reasons
        st.subheader("💡 Analysis Reasons")
        for reason in result['reasons']:
            st.write(f"• {reason}")
        
        # Subject Grades Verification (NEW)
        if result.get('registration_no'):
            reg_no = result['registration_no']
            verifier = st.session_state.get('verifier')
            if verifier:
                try:
                    # Lookup subjects from database
                    subjects = verifier._lookup_subjects(reg_no)
                    
                    if subjects:
                        st.subheader("📚 Subject Grades Verification")
                        
                        # Show subjects in a table
                        import pandas as pd
                        df = pd.DataFrame(subjects)
                        
                        # Display summary with comparison
                        import sqlite3
                        import re
                        conn = sqlite3.connect('certs.db')
                        cursor = conn.cursor()
                        cursor.execute("""
                            SELECT total_credits_earned, sgpa, cgpa
                            FROM certificate_summary
                            WHERE reg_no = ?
                        """, (reg_no,))
                        summary = cursor.fetchone()
                        conn.close()
                        
                        # Extract SGPA/CGPA from OCR text
                        ocr_text = ocr_data.get('raw_text', '')
                        
                        # Try pattern: "SGPA CGPA 9.95 9.78" (both on same line)
                        combined_match = re.search(r'SGPA\s+CGPA\s+([0-9.]+)\s+([0-9.]+)', ocr_text, re.IGNORECASE)
                        
                        if combined_match:
                            # Both values on same line: first is SGPA, second is CGPA
                            ocr_sgpa = float(combined_match.group(1))
                            ocr_cgpa = float(combined_match.group(2))
                        else:
                            # Try separate patterns (values on different lines)
                            sgpa_match = re.search(r'SGPA[:\s]+([0-9.]+)', ocr_text, re.IGNORECASE)
                            cgpa_match = re.search(r'CGPA[:\s]+([0-9.]+)', ocr_text, re.IGNORECASE)
                            
                            ocr_sgpa = float(sgpa_match.group(1)) if sgpa_match else None
                            ocr_cgpa = float(cgpa_match.group(1)) if cgpa_match else None
                        
                        if summary:
                            db_credits, db_sgpa, db_cgpa = summary
                            
                            col1, col2, col3 = st.columns(3)
                            
                            with col1:
                                st.metric("Total Credits (DB)", db_credits)
                            
                            with col2:
                                if ocr_sgpa is not None:
                                    sgpa_diff = abs(db_sgpa - ocr_sgpa)
                                    sgpa_match = sgpa_diff < 0.1
                                    st.metric(
                                        "SGPA Comparison", 
                                        f"DB: {db_sgpa:.2f}",
                                        delta=f"OCR: {ocr_sgpa:.2f}",
                                        delta_color="normal" if sgpa_match else "inverse"
                                    )
                                    if not sgpa_match:
                                        st.error(f"⚠️ SGPA Mismatch! Difference: {sgpa_diff:.2f}")
                                else:
                                    st.metric("SGPA (DB)", f"{db_sgpa:.2f}")
                            
                            with col3:
                                if ocr_cgpa is not None:
                                    cgpa_diff = abs(db_cgpa - ocr_cgpa)
                                    cgpa_match = cgpa_diff < 0.1
                                    st.metric(
                                        "CGPA Comparison",
                                        f"DB: {db_cgpa:.2f}",
                                        delta=f"OCR: {ocr_cgpa:.2f}",
                                        delta_color="normal" if cgpa_match else "inverse"
                                    )
                                    if not cgpa_match:
                                        st.error(f"⚠️ CGPA Mismatch! Difference: {cgpa_diff:.2f}")
                                else:
                                    st.metric("CGPA (DB)", f"{db_cgpa:.2f}")
                            
                            # Overall GPA verification status
                            if ocr_sgpa or ocr_cgpa:
                                gpa_matches = []
                                if ocr_sgpa and abs(db_sgpa - ocr_sgpa) < 0.1:
                                    gpa_matches.append("SGPA")
                                if ocr_cgpa and abs(db_cgpa - ocr_cgpa) < 0.1:
                                    gpa_matches.append("CGPA")
                                
                                if len(gpa_matches) == 2:
                                    st.success("✅ SGPA and CGPA match database records")
                                elif len(gpa_matches) == 1:
                                    st.warning(f"⚠️ Only {gpa_matches[0]} matches. Please verify manually.")
                                else:
                                    st.error("❌ SGPA/CGPA do not match database. Possible forgery!")
                        
                        # Show subjects table
                        st.dataframe(
                            df[['subject_code', 'subject_name', 'credits_registered', 'grade', 'grade_points']],
                            use_container_width=True,
                            hide_index=True
                        )
                        
                        st.success(f"✅ Found {len(subjects)} subjects in database for this student")
                    else:
                        st.info("ℹ️ No subject-level data available for this registration number")
                except Exception as e:
                    st.warning(f"⚠️ Could not load subject data: {e}")
        
        # Raw OCR text
        with st.expander("📄 Raw OCR Text"):
            st.text(ocr_data.get('raw_text', 'No text extracted'))
    
    # Show cropped seals if available
    if st.session_state.cropped_seals:
        with st.expander("🔍 Detected Seals/Stamps", expanded=True):
            st.write(f"Found {len(st.session_state.cropped_seals)} seal(s) in the certificate:")
            
            cols = st.columns(min(3, len(st.session_state.cropped_seals)))
            for i, seal_info in enumerate(st.session_state.cropped_seals):
                with cols[i % 3]:
                    st.image(seal_info['pil_image'], caption=f"Seal {i+1} ({seal_info['method']} detection)")

def create_verification_report(result, seal_result=None):
    """Create a downloadable verification report."""
    
    # Determine final decision with improved logic
    ocr_status = "Pass" if result['decision'] == 'AUTHENTIC' else "Fail"
    seal_status = seal_result.get('status', 'Not Checked') if seal_result else 'Not Checked'
    
    # Apply same security-first decision logic as above
    ocr_confidence = result.get('final_score', 0)
    seal_confidence = seal_result.get('confidence', 0) if seal_result else 0
    
    # Security-first decision criteria:
    both_pass = (ocr_status == "Pass" and seal_status == "Pass")
    
    # CRITICAL SECURITY CHECK: If fake seals detected with high confidence, REJECT
    fake_seals_detected = False
    rejection_reason = None
    if seal_result and seal_result.get('details'):
        fake_count = seal_result['details'].get('fake_seals', 0)
        total_seals = seal_result['details'].get('total_seals', 0)
        if fake_count > 0 and seal_confidence > 0.7:  # High confidence fake detection
            fake_seals_detected = True
            rejection_reason = f"High confidence fake seal detection ({seal_confidence:.1%})"
    
    # REJECT if fake seals detected with high confidence
    if fake_seals_detected:
        final_decision = "Fake"
    else:
        # Only pass if both OCR and seals pass, or if no seal verification was performed
        if seal_result is None:  # No seal verification
            final_decision = "Real" if (ocr_status == "Pass" and ocr_confidence > 0.8) else "Fake"
        else:  # Seal verification was performed
            final_decision = "Real" if both_pass else "Fake"
    
    report = {
        'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'),
        'final_decision': final_decision,
        'ocr_verification': {
            'status': ocr_status,
            'decision': result['decision'],
            'confidence_score': result['final_score'],
            'registration_number': result['registration_no'],
            'database_match': result['db_record'] is not None,
            'details': result
        },
        'seal_verification': seal_result if seal_result else {
            'status': 'Not Checked',
            'reason': 'Seal verification was not performed'
        },
        'summary': {
            'final_decision': final_decision,
            'ocr_status': ocr_status,
            'seal_status': seal_status,
            'overall_confidence': (result['final_score'] + seal_result.get('confidence', 0.5)) / 2 if seal_result else result['final_score']
        }
    }

    return json.dumps(report, indent=2, ensure_ascii=False)

def main():
    """Main Streamlit application."""
    
    init_session_state()
    
    st.title("🎓 Certificate Verification System")
    st.markdown("Upload a certificate image to verify its authenticity against our database.")
    
    # Sidebar configuration
    with st.sidebar:
        st.header("⚙️ Configuration")
        
        # System Status
        st.subheader("🔧 System Status")
        
        # OCR Status
        if OCR_AVAILABLE:
            api_key = os.getenv('OCRSPACE_API_KEY')
            if api_key:
                st.success("✅ OCR API Available & Configured")
            else:
                st.warning("⚠️ OCR API Available (No API Key)")
        else:
            st.error("❌ OCR Components Not Available")
        
        # Seal Detection Status
        if SEAL_DETECTION_AVAILABLE:
            if SEAL_METHOD == "YOLOv8":
                st.success(f"🚀 Seal Detection ({SEAL_METHOD}) - 99% Accuracy")
                st.caption("State-of-the-art AI model trained on your dataset")
            else:
                st.success(f"✅ Seal Detection ({SEAL_METHOD})")
        else:
            st.error("❌ Seal Detection Not Available")
            
        # AI Model Status
        if VIT_AVAILABLE:
            st.success("✅ AI Seal Classifier Available")
        else:
            st.warning("⚠️ AI Model - Demo Mode Only")
        
        # Database status
        db_path = "certs.db"
        if os.path.exists(db_path):
            st.success("✅ Database connected")
            
            # Show database stats
            import sqlite3
            try:
                conn = sqlite3.connect(db_path)
                cursor = conn.cursor()
                cursor.execute("SELECT COUNT(*) FROM certificates")
                count = cursor.fetchone()[0]
                st.info(f"📊 {count} certificates in database")
                conn.close()
            except:
                st.warning("⚠️ Database error")
        else:
            st.error("❌ Database not found")
            st.write("Please run `python init_db.py` first")
        
        # OCR Settings
        st.subheader("🔧 OCR Settings")
        ocr_language = st.selectbox("Language", ["eng", "ara", "chs", "cht", "cze", "dan", "dut", "fin", "fre", "ger", "hun", "ita", "jpn", "kor", "nor", "pol", "por", "rus", "slv", "spa", "swe", "tur"])
        use_overlay = st.checkbox("Extract bounding boxes", value=True)
        
        # Demo Mode
        st.subheader("🎮 Demo Mode")
        demo_mode = st.checkbox("Use Demo Mode (Skip OCR)", help="Test verification with sample OCR data")
        
        # Seal Verification Settings
        st.subheader("🔎 Seal Verification")
        
        if SEAL_DETECTION_AVAILABLE:
            enable_seal_verification = st.checkbox("Enable Seal Verification", value=True, help="Detect and verify seals/stamps using AI")
            
            if enable_seal_verification:
                # Check if ViT model exists OR if we have HuggingFace URL configured
                model_exists = os.path.exists('vit_seal_checker.pth') and VIT_AVAILABLE
                
                # Check if we can download from HuggingFace
                can_download = False
                try:
                    vit_url = st.secrets.get("VIT_MODEL_URL", None)
                    if vit_url:
                        can_download = True
                except:
                    pass
                
                if model_exists:
                    st.success("✅ ViT model ready (local)")
                    seal_demo_mode = st.checkbox("Seal Demo Mode", value=False, help="Use demo predictions instead of trained model")
                elif can_download and VIT_AVAILABLE:
                    st.info("📥 ViT model will download from Hugging Face on first use")
                    seal_demo_mode = st.checkbox("Seal Demo Mode", value=False, help="Use demo predictions instead of trained model")
                else:
                    st.warning("⚠️ ViT model not available")
                    st.info("Using demo mode for seal classification")
                    seal_demo_mode = True
        else:
            st.warning("⚠️ Seal verification not available")
            st.info("Install opencv-python-headless to enable seal detection")
            enable_seal_verification = False
            seal_demo_mode = True
        
        # OCR Demo Mode
        st.subheader("🔤 OCR Settings")
        if not OCR_AVAILABLE or not os.getenv('OCRSPACE_API_KEY'):
            st.warning("⚠️ Using OCR Demo Mode")
            st.info("Configure API key for real OCR extraction")
            ocr_demo_mode = True
        else:
            ocr_demo_mode = st.checkbox("OCR Demo Mode", value=False, help="Use sample OCR data instead of API")
    
    # Main interface
    if not OCR_AVAILABLE and not ocr_demo_mode:
        st.error("🚨 **Setup Required**: OCR components not available.")
        st.info("💡 **Alternative**: OCR Demo Mode is automatically enabled for testing")
        ocr_demo_mode = True
    
    # YOLOv8 Integration Check
    if SEAL_METHOD == "YOLOv8":
        try:
            from yolo_seal_detector import check_yolo_integration
            if not check_yolo_integration():
                st.info("📥 **YOLOv8 Setup**: Download the trained model from Kaggle for best seal detection")
        except ImportError:
            pass
    
    if not os.path.exists(db_path) and not ocr_demo_mode:
        st.error("🚨 **Setup Required**: Please initialize the database first.")
        st.code("python init_db.py")
        st.info("💡 **Alternative**: Demo mode will work without database")
        return
    
    # OCR Troubleshooting
    with st.expander("🔧 OCR Troubleshooting Guide"):
        st.markdown("""
        **If you're getting E301 errors:**
        
        1. **✅ Try Demo Mode**: Enable in sidebar to test verification without OCR
        2. **📸 Image Quality**: Use clear, well-lit, straight-aligned certificates
        3. **📁 File Format**: JPG/PNG work best (avoid PDF, TIFF)
        4. **📏 File Size**: Keep under 1MB (system auto-resizes but quality matters)
        5. **🎯 Text Clarity**: Ensure certificate text is readable and high-contrast
        
        **Demo Mode includes sample certificates:**
        - Saksham Sharma (ABC2023001) - DevLabs Institute
        - Prisha Verma (ABC2022007) - Global Tech University
        
        Upload any image and enable Demo Mode to see how verification works!
        """)
    
    # File upload
    uploaded_file = st.file_uploader(
        "Choose a certificate image",
        type=['png', 'jpg', 'jpeg', 'pdf'],
        help="Upload a clear image of the certificate you want to verify"
    )
    
    if uploaded_file is not None:
        st.session_state.uploaded_file = uploaded_file
        
        # Display uploaded image
        if uploaded_file.type.startswith('image'):
            image = Image.open(uploaded_file)
            st.image(image, caption="Uploaded Certificate", use_container_width=True)
        
        # Verify button
        col1, col2, col3 = st.columns([1, 1, 2])
        
        with col1:
            if st.button("🔍 Verify Certificate", type="primary"):
                verify_certificate(uploaded_file, ocr_language, use_overlay, ocr_demo_mode, 
                                 enable_seal_verification, seal_demo_mode if enable_seal_verification else False)
        
        with col2:
            if st.session_state.verification_result:
                report_json = create_verification_report(st.session_state.verification_result, st.session_state.seal_result)
                st.download_button(
                    "📥 Download Report",
                    data=report_json,
                    file_name=f"verification_report_{int(time.time())}.json",
                    mime="application/json"
                )
    
    # Display results
    if st.session_state.verification_result:
        st.markdown("---")
        display_verification_result(st.session_state.verification_result, st.session_state.seal_result)
        
        # Option to verify another certificate
        if st.button("🔄 Verify Another Certificate"):
            st.session_state.verification_result = None
            st.session_state.ocr_result = None
            st.session_state.seal_result = None
            st.session_state.cropped_seals = None
            st.session_state.uploaded_file = None
            st.rerun()

def verify_certificate(uploaded_file, language, use_overlay, ocr_demo_mode=False, enable_seal_verification=True, seal_demo_mode=False):
    """Process the certificate verification."""
    
    try:
        # Show progress
        progress_bar = st.progress(0)
        status_text = st.empty()
        
        status_text.text("📤 Processing file...")
        progress_bar.progress(5)
        
        # Read file data (reset file pointer first)
        uploaded_file.seek(0)
        file_bytes = uploaded_file.read()
        
        if len(file_bytes) == 0:
            st.error("❌ File appears to be empty or corrupted. Please try uploading again.")
            progress_bar.empty()
            status_text.empty()
            return
        
        # Save uploaded file temporarily for seal detection
        temp_image_path = None
        if enable_seal_verification and uploaded_file.type.startswith('image'):
            temp_dir = tempfile.mkdtemp()
            temp_image_path = os.path.join(temp_dir, f"temp_cert_{int(time.time())}.{uploaded_file.name.split('.')[-1]}")
            
            with open(temp_image_path, 'wb') as f:
                f.write(file_bytes)
        
        if ocr_demo_mode:
            # Use demo OCR data
            status_text.text("🎮 Using demo OCR data...")
            progress_bar.progress(30)
            
            # Sample OCR result based on filename or random selection
            demo_certificates = {
                "saksham": {
                    'success': True,
                    'extracted_text': '''CERTIFICATE OF COMPLETION
                    
This is to certify that

SAKSHAM SHARMA

has successfully completed the course

B.Tech Computer Engineering

from

DevLabs Institute

in the year 2023

Registration Number: ABC2023001

Date of Issue: December 2023''',
                    'confidence': 0.92,
                    'bounding_boxes': []
                },
                "prisha": {
                    'success': True,
                    'extracted_text': '''GRADUATION CERTIFICATE
                    
This certifies that

PRISHA VERMA

has completed

M.Tech AI

from

Global Tech University

Year: 2022

Registration: ABC2022007''',
                    'confidence': 0.88,
                    'bounding_boxes': []
                }
            }
            
            # Select demo data based on filename
            filename_lower = uploaded_file.name.lower()
            if 'saksham' in filename_lower or 'abc2023001' in filename_lower:
                ocr_result = demo_certificates["saksham"]
            elif 'prisha' in filename_lower or 'abc2022007' in filename_lower:
                ocr_result = demo_certificates["prisha"]
            else:
                # Default to Saksham's certificate
                ocr_result = demo_certificates["saksham"]
                
            st.info("🎮 Demo Mode: Using sample OCR data for testing")
            
        else:
            # Real OCR processing
            status_text.text("🔍 Running OCR analysis...")
            progress_bar.progress(20)
            
            # Run OCR
            if OCRClient:
                ocr_client = OCRClient()
                ocr_result = ocr_client.extract_text_from_bytes(
                    file_bytes,
                    language=language,
                    overlay=use_overlay
                )
            else:
                # Fallback to demo mode if OCR not available
                ocr_result = {'success': False, 'error': 'OCR components not available'}
        
        st.session_state.ocr_result = ocr_result
        
        if not ocr_result['success']:
            st.error(f"❌ OCR failed: {ocr_result.get('error', 'Unknown error')}")
            if not ocr_demo_mode:
                st.info("💡 **Tip**: Try enabling 'Demo Mode' in the sidebar to test the verification system without OCR")
            progress_bar.empty()
            status_text.empty()
            return
        
        status_text.text("🔍 Verifying against database...")
        progress_bar.progress(50)
        
        # Run OCR verification
        if CertificateVerifier:
            verifier = CertificateVerifier()
            st.session_state.verifier = verifier  # Store for later use
            verification_result = verifier.verify_certificate(ocr_result, uploaded_file.name)
        else:
            # Demo mode verification result
            verification_result = {
                'decision': 'AUTHENTIC',
                'confidence': 0.85,
                'field_scores': {'name': 0.95, 'course': 0.80, 'institution': 0.90},
                'db_record': {'reg_no': 'DEMO001', 'name': 'Demo Certificate', 'status': 'valid'}
            }
        
        st.session_state.verification_result = verification_result
        
        # Step 2: Seal Verification with YOLOv8
        seal_result = None
        if enable_seal_verification and temp_image_path:
            status_text.text("🔎 Detecting and verifying seals with AI...")
            progress_bar.progress(70)
            
            try:
                # Initialize seal detector
                seal_detector = SealDetector()
                
                if seal_demo_mode:
                    # Use demo seal verification
                    if VIT_AVAILABLE:
                        classifier = ViTSealClassifier()
                        seal_result = classifier.create_dummy_prediction(confidence=0.82)
                    else:
                        seal_result = {
                            "step": "Seal Verification",
                            "status": "Pass",
                            "reason": "Demo mode - seal appears authentic",
                            "seal_status": "Real",
                            "confidence": 0.82
                        }
                    st.session_state.cropped_seals = []  # No actual cropped seals in demo mode
                    
                    # Show demo seal info
                    st.info("🎮 Demo Mode: Using simulated seal detection results")
                    
                else:
                    # Real YOLOv8 seal detection and verification
                    st.write("**🤖 YOLOv8 Seal Detection in Progress...**")
                    
                    # Get detection summary with Streamlit integration
                    summary = seal_detector.get_detection_summary(temp_image_path, confidence_threshold=0.5)
                    
                    # Visualize detections if available
                    if hasattr(seal_detector, 'visualize_detections'):
                        detected_image = seal_detector.visualize_detections(temp_image_path)
                        if detected_image:
                            st.image(detected_image, caption="🎯 AI-Detected Seals", use_container_width=True)
                    
                    # Process seal detection results
                    if summary['total_seals'] > 0:
                        # Analyze detection results
                        fake_count = summary['class_distribution'].get('fake', 0)
                        true_count = summary['class_distribution'].get('true', 0)
                        avg_confidence = summary['average_confidence']
                        
                        # Determine overall seal authenticity
                        if fake_count > true_count:
                            seal_status = "Fake"
                            status = "Fail"
                            reason = f"Detected {fake_count} fake seals vs {true_count} authentic seals"
                        elif true_count > 0 and fake_count == 0:
                            seal_status = "Real"
                            status = "Pass"
                            reason = f"All {true_count} detected seals appear authentic"
                        else:
                            seal_status = "Suspicious"
                            status = "Warning"
                            reason = f"Mixed results: {true_count} authentic, {fake_count} fake seals"
                        
                        # Crop seals for further analysis
                        cropped_seals = seal_detector.crop_seals_from_image(temp_image_path)
                        st.session_state.cropped_seals = []
                        
                        # Convert cropped seals to expected format
                        for i, cropped_path in enumerate(cropped_seals):
                            if os.path.exists(cropped_path):
                                from PIL import Image
                                seal_img = Image.open(cropped_path)
                                detection = summary['detections'][i] if i < len(summary['detections']) else {}
                                
                                st.session_state.cropped_seals.append({
                                    'pil_image': seal_img,
                                    'path': cropped_path,
                                    'method': f"YOLOv8 ({detection.get('class', 'unknown')})",
                                    'confidence': detection.get('confidence', 0.0),
                                    'class': detection.get('class', 'unknown')
                                })
                        
                        seal_result = {
                            "step": "Seal Verification",
                            "status": status,
                            "reason": reason,
                            "seal_status": seal_status,
                            "confidence": avg_confidence,
                            "details": {
                                "total_seals": summary['total_seals'],
                                "fake_seals": fake_count,
                                "authentic_seals": true_count,
                                "detection_method": "YOLOv8",
                                "model_confidence": avg_confidence
                            }
                        }
                        
                        # Show detailed results
                        if status == "Pass":
                            st.success(f"✅ {reason} (confidence: {avg_confidence:.1%})")
                        elif status == "Fail":
                            st.error(f"❌ {reason} (confidence: {avg_confidence:.1%})")
                        else:
                            st.warning(f"⚠️ {reason} (confidence: {avg_confidence:.1%})")
                    
                    else:
                        # No seals detected
                        seal_result = {
                            "step": "Seal Verification",
                            "status": "Warning",
                            "reason": "No seals detected in certificate - this may indicate a fake certificate",
                            "seal_status": "Missing",
                            "confidence": 0.0,
                            "details": {
                                "total_seals": 0,
                                "detection_method": "YOLOv8"
                            }
                        }
                        st.session_state.cropped_seals = []
                        st.warning("⚠️ No seals detected - certificates usually contain official seals/stamps")
                
            except Exception as e:
                st.error(f"❌ Seal verification error: {str(e)}")
                seal_result = {
                    "step": "Seal Verification", 
                    "status": "Error",
                    "reason": f"Seal verification failed: {str(e)}",
                    "seal_status": "Error",
                    "confidence": 0.0
                }
            
            # Clean up temp file
            try:
                if os.path.exists(temp_image_path):
                    os.remove(temp_image_path)
                    os.rmdir(os.path.dirname(temp_image_path))
            except:
                pass
        
        st.session_state.seal_result = seal_result
        
        status_text.text("✅ Verification complete!")
        progress_bar.progress(100)
        
        # Clear progress indicators
        time.sleep(1)
        progress_bar.empty()
        status_text.empty()
        
        # Show success message with improved decision logic
        ocr_status = "Pass" if verification_result['decision'] == 'AUTHENTIC' else "Fail"
        seal_status = seal_result.get('status', 'Unknown') if seal_result else 'Not Checked'
        
        # Apply same security-first decision logic
        ocr_confidence = verification_result.get('final_score', 0)
        seal_confidence = seal_result.get('confidence', 0) if seal_result else 0
        
        # Security-first decision criteria:
        both_pass = (ocr_status == "Pass" and seal_status == "Pass")
        
        # CRITICAL SECURITY CHECK: If fake seals detected with high confidence, REJECT
        fake_seals_detected = False
        rejection_reason = None
        if seal_result and seal_result.get('details'):
            fake_count = seal_result['details'].get('fake_seals', 0)
            total_seals = seal_result['details'].get('total_seals', 0)
            if fake_count > 0 and seal_confidence > 0.7:  # High confidence fake detection
                fake_seals_detected = True
                rejection_reason = f"High confidence fake seal detection ({seal_confidence:.1%})"
        
        # REJECT if fake seals detected with high confidence
        if fake_seals_detected:
            final_decision = "Fake"
        else:
            # Only pass if both OCR and seals pass, or if no seal verification was performed
            if seal_result is None:  # No seal verification
                final_decision = "Real" if (ocr_status == "Pass" and ocr_confidence > 0.8) else "Fake"
            else:  # Seal verification was performed
                final_decision = "Real" if both_pass else "Fake"
        
        if final_decision == "Real":
            st.success("🎉 Certificate verification completed - AUTHENTIC!")
        else:
            if rejection_reason:
                st.error(f"❌ Certificate verification failed - {rejection_reason}")
            else:
                st.error("❌ Certificate verification failed - verification issues detected.")
        
        if seal_result and enable_seal_verification:
            st.info(f"🔎 Seal verification: {seal_result.get('seal_status', 'Unknown')}")
    
    except Exception as e:
        st.error(f"💥 Verification failed: {str(e)}")
        # Clear progress indicators
        if 'progress_bar' in locals():
            progress_bar.empty()
        if 'status_text' in locals():
            status_text.empty()

if __name__ == "__main__":
    main()