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| import gradio as gr | |
| import pdfplumber, docx, sqlite3, os, random, tempfile, shutil | |
| from datetime import datetime | |
| import pandas as pd | |
| from sentence_transformers import SentenceTransformer | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import numpy as np | |
| from fpdf import FPDF | |
| import logging | |
| import hashlib | |
| from typing import List, Tuple, Optional | |
| import asyncio | |
| import aiohttp | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import re | |
| import time | |
| # ----------------------------- | |
| # ENHANCED CONFIG | |
| # ----------------------------- | |
| DB_NAME = "db.sqlite3" | |
| USERNAME = "aixbi" | |
| PASSWORD = "aixbi@123" | |
| MAX_SENTENCES_CHECK = 15 # Increased for better coverage | |
| LOGO_PATH = "aixbi.jpg" | |
| MIN_SENTENCE_LENGTH = 20 # Reduced for better detection | |
| SIMILARITY_THRESHOLD = 0.85 # For semantic similarity | |
| CHUNK_SIZE = 512 # For processing large documents | |
| LOG_FILE = "plagiarism_detector.log" | |
| # Setup logging | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(levelname)s - %(message)s', | |
| handlers=[ | |
| logging.FileHandler(LOG_FILE), | |
| logging.StreamHandler() | |
| ] | |
| ) | |
| logger = logging.getLogger(__name__) | |
| # ----------------------------- | |
| # ENHANCED DB INIT | |
| # ----------------------------- | |
| def init_db(): | |
| """Enhanced database with additional fields and indexes""" | |
| conn = sqlite3.connect(DB_NAME) | |
| c = conn.cursor() | |
| # Main results table with more fields | |
| c.execute("""CREATE TABLE IF NOT EXISTS results ( | |
| id INTEGER PRIMARY KEY AUTOINCREMENT, | |
| student_id TEXT NOT NULL, | |
| student_name TEXT NOT NULL, | |
| document_hash TEXT, | |
| ai_score REAL, | |
| plagiarism_score REAL, | |
| word_count INTEGER, | |
| sentence_count INTEGER, | |
| suspicious_sentences_count INTEGER, | |
| processing_time REAL, | |
| file_type TEXT, | |
| timestamp TEXT, | |
| status TEXT DEFAULT 'completed' | |
| )""") | |
| # Suspicious sentences table for detailed tracking | |
| c.execute("""CREATE TABLE IF NOT EXISTS suspicious_sentences ( | |
| id INTEGER PRIMARY KEY AUTOINCREMENT, | |
| result_id INTEGER, | |
| sentence TEXT, | |
| similarity_score REAL, | |
| source_found BOOLEAN, | |
| FOREIGN KEY (result_id) REFERENCES results (id) | |
| )""") | |
| # Create indexes for better performance | |
| c.execute("CREATE INDEX IF NOT EXISTS idx_student_id ON results (student_id)") | |
| c.execute("CREATE INDEX IF NOT EXISTS idx_timestamp ON results (timestamp)") | |
| c.execute("CREATE INDEX IF NOT EXISTS idx_document_hash ON results (document_hash)") | |
| conn.commit() | |
| conn.close() | |
| init_db() | |
| # ----------------------------- | |
| # ENHANCED MODEL LOADING WITH ERROR HANDLING | |
| # ----------------------------- | |
| try: | |
| embedder = SentenceTransformer('all-MiniLM-L6-v2') | |
| tokenizer = AutoTokenizer.from_pretrained("hello-simpleai/chatgpt-detector-roberta") | |
| model = AutoModelForSequenceClassification.from_pretrained("hello-simpleai/chatgpt-detector-roberta") | |
| logger.info("Models loaded successfully") | |
| except Exception as e: | |
| logger.error(f"Error loading models: {e}") | |
| raise | |
| # ----------------------------- | |
| # ENHANCED FILE HANDLING | |
| # ----------------------------- | |
| def calculate_file_hash(file_path: str) -> str: | |
| """Calculate SHA-256 hash of file for duplicate detection""" | |
| hash_sha256 = hashlib.sha256() | |
| with open(file_path, "rb") as f: | |
| for chunk in iter(lambda: f.read(4096), b""): | |
| hash_sha256.update(chunk) | |
| return hash_sha256.hexdigest() | |
| def extract_text(file_obj): | |
| """Extracts text safely from PDF/DOCX/TXT - Enhanced version of working code""" | |
| if file_obj is None: | |
| return None | |
| name = file_obj.name | |
| ext = os.path.splitext(name)[1].lower() | |
| # Copy to temp file preserving extension | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as tmp: | |
| shutil.copy(file_obj.name, tmp.name) | |
| tmp_path = tmp.name | |
| try: | |
| if ext == ".pdf": | |
| with pdfplumber.open(tmp_path) as pdf: | |
| text = " ".join(page.extract_text() or "" for page in pdf.pages) | |
| elif ext == ".docx": | |
| doc = docx.Document(tmp_path) | |
| text = " ".join(p.text for p in doc.paragraphs) | |
| elif ext == ".txt": | |
| with open(tmp_path, "r", encoding="utf-8", errors="ignore") as f: | |
| text = f.read() | |
| else: | |
| return None | |
| except: | |
| return None | |
| finally: | |
| # Clean up temp file | |
| try: | |
| os.unlink(tmp_path) | |
| except: | |
| pass | |
| return text.strip() if text else None | |
| def extract_text_with_metadata(file_obj) -> Optional[Tuple[str, dict]]: | |
| """Enhanced text extraction with metadata - calls the working extract_text function""" | |
| if file_obj is None: | |
| return None, None | |
| # Use the working extract_text function first | |
| text = extract_text(file_obj) | |
| if text is None: | |
| return None, None | |
| # Now gather metadata safely | |
| name = file_obj.name | |
| ext = os.path.splitext(name)[1].lower() | |
| # Create temporary file again for metadata extraction | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as tmp: | |
| shutil.copy(file_obj.name, tmp.name) | |
| tmp_path = tmp.name | |
| try: | |
| metadata = { | |
| 'file_type': ext, | |
| 'file_size': os.path.getsize(tmp_path), | |
| 'file_hash': calculate_file_hash(tmp_path), | |
| 'word_count': len(text.split()), | |
| 'char_count': len(text) | |
| } | |
| # Add specific metadata based on file type | |
| if ext == ".pdf": | |
| try: | |
| with pdfplumber.open(tmp_path) as pdf: | |
| metadata['page_count'] = len(pdf.pages) | |
| except: | |
| metadata['page_count'] = 'Unknown' | |
| elif ext == ".docx": | |
| try: | |
| doc = docx.Document(tmp_path) | |
| metadata['paragraph_count'] = len(doc.paragraphs) | |
| except: | |
| metadata['paragraph_count'] = 'Unknown' | |
| except Exception as e: | |
| logger.error(f"Error gathering metadata from {name}: {e}") | |
| # Return text with minimal metadata if metadata extraction fails | |
| metadata = { | |
| 'file_type': ext, | |
| 'file_size': 0, | |
| 'file_hash': '', | |
| 'word_count': len(text.split()), | |
| 'char_count': len(text) | |
| } | |
| finally: | |
| try: | |
| os.unlink(tmp_path) | |
| except: | |
| pass | |
| # Final validation | |
| if len(text.strip()) < 50: | |
| logger.warning("Extracted text is too short for meaningful analysis") | |
| return None, None | |
| return text, metadata | |
| # ----------------------------- | |
| # ENHANCED AI DETECTION WITH CHUNKING | |
| # ----------------------------- | |
| def detect_ai_text(text: str) -> Tuple[float, dict]: | |
| """Enhanced AI detection with confidence scores and chunking for large texts""" | |
| try: | |
| # Split into chunks for large texts | |
| chunks = [text[i:i+CHUNK_SIZE] for i in range(0, len(text), CHUNK_SIZE)] | |
| scores = [] | |
| details = {'chunk_scores': [], 'confidence': 'low'} | |
| for chunk in chunks[:5]: # Limit to first 5 chunks for performance | |
| if len(chunk.strip()) < 20: | |
| continue | |
| inputs = tokenizer(chunk, return_tensors="pt", truncation=True, max_length=512) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| probabilities = torch.softmax(outputs.logits, dim=1) | |
| score = probabilities[0][1].item() # AI probability | |
| scores.append(score) | |
| details['chunk_scores'].append(round(score * 100, 2)) | |
| if not scores: | |
| return 0.0, details | |
| avg_score = np.mean(scores) | |
| std_score = np.std(scores) if len(scores) > 1 else 0 | |
| # Determine confidence based on consistency | |
| if std_score < 0.1: | |
| details['confidence'] = 'high' | |
| elif std_score < 0.2: | |
| details['confidence'] = 'medium' | |
| else: | |
| details['confidence'] = 'low' | |
| details['std_deviation'] = round(std_score, 3) | |
| return avg_score, details | |
| except Exception as e: | |
| logger.error(f"Error in AI detection: {e}") | |
| return 0.0, {'error': str(e)} | |
| # ----------------------------- | |
| # ENHANCED PLAGIARISM DETECTION | |
| # ----------------------------- | |
| def preprocess_text(text: str) -> List[str]: | |
| """Extract meaningful sentences with better filtering""" | |
| # Split into sentences using multiple delimiters | |
| sentences = re.split(r'[.!?]+', text) | |
| # Clean and filter sentences | |
| cleaned_sentences = [] | |
| for sentence in sentences: | |
| sentence = sentence.strip() | |
| # Filter out short sentences, headers, page numbers, etc. | |
| if (len(sentence) >= MIN_SENTENCE_LENGTH and | |
| not sentence.isdigit() and | |
| len(sentence.split()) >= 5 and | |
| not re.match(r'^(page|chapter|\d+)[\s\d]*$', sentence.lower())): | |
| cleaned_sentences.append(sentence) | |
| return cleaned_sentences | |
| def semantic_similarity_check(sentences: List[str], suspicious_sentences: List[str]) -> List[Tuple[str, float]]: | |
| """Check for semantic similarity between sentences""" | |
| if not sentences or not suspicious_sentences: | |
| return [] | |
| try: | |
| # Encode sentences | |
| sentence_embeddings = embedder.encode(sentences) | |
| suspicious_embeddings = embedder.encode(suspicious_sentences) | |
| # Calculate similarities | |
| similarities = cosine_similarity(sentence_embeddings, suspicious_embeddings) | |
| high_similarity_pairs = [] | |
| for i, sentence in enumerate(sentences): | |
| max_similarity = np.max(similarities[i]) | |
| if max_similarity > SIMILARITY_THRESHOLD: | |
| high_similarity_pairs.append((sentence, max_similarity)) | |
| return high_similarity_pairs | |
| except Exception as e: | |
| logger.error(f"Error in semantic similarity check: {e}") | |
| return [] | |
| async def async_web_search(sentence: str, session: aiohttp.ClientSession) -> bool: | |
| """Async web search for better performance""" | |
| try: | |
| # Simple search simulation - replace with actual search API | |
| # This is a placeholder for actual web search implementation | |
| await asyncio.sleep(0.1) # Simulate network delay | |
| return random.choice([True, False]) # Placeholder result | |
| except Exception as e: | |
| logger.error(f"Error in web search: {e}") | |
| return False | |
| def enhanced_plagiarism_check(sentences: List[str]) -> Tuple[float, List[dict]]: | |
| """Enhanced plagiarism detection with multiple methods""" | |
| if not sentences: | |
| return 0.0, [] | |
| # Sample sentences strategically (beginning, middle, end) | |
| total_sentences = len(sentences) | |
| if total_sentences <= MAX_SENTENCES_CHECK: | |
| samples = sentences | |
| else: | |
| # Take samples from different parts of the document | |
| begin_samples = sentences[:MAX_SENTENCES_CHECK//3] | |
| middle_start = total_sentences // 2 - MAX_SENTENCES_CHECK//6 | |
| middle_samples = sentences[middle_start:middle_start + MAX_SENTENCES_CHECK//3] | |
| end_samples = sentences[-(MAX_SENTENCES_CHECK//3):] | |
| samples = begin_samples + middle_samples + end_samples | |
| suspicious_results = [] | |
| # Simulate plagiarism detection (replace with actual implementation) | |
| for sentence in samples: | |
| # Placeholder for actual plagiarism detection logic | |
| is_suspicious = len(sentence) > 100 and random.random() > 0.7 | |
| confidence = random.uniform(0.5, 1.0) if is_suspicious else random.uniform(0.0, 0.4) | |
| suspicious_results.append({ | |
| 'sentence': sentence, | |
| 'is_suspicious': is_suspicious, | |
| 'confidence': confidence, | |
| 'source_found': is_suspicious, | |
| 'similarity_score': confidence if is_suspicious else 0.0 | |
| }) | |
| # Calculate overall plagiarism score | |
| suspicious_count = sum(1 for r in suspicious_results if r['is_suspicious']) | |
| plagiarism_score = (suspicious_count / len(samples)) * 100 if samples else 0 | |
| return plagiarism_score, suspicious_results | |
| # ----------------------------- | |
| # ENHANCED DB OPERATIONS | |
| # ----------------------------- | |
| def save_result(student_id: str, student_name: str, ai_score: float, plagiarism_score: float, | |
| metadata: dict, suspicious_results: List[dict], processing_time: float) -> int: | |
| """Enhanced result saving with detailed information""" | |
| conn = sqlite3.connect(DB_NAME) | |
| c = conn.cursor() | |
| # Insert main result | |
| c.execute("""INSERT INTO results | |
| (student_id, student_name, document_hash, ai_score, plagiarism_score, | |
| word_count, sentence_count, suspicious_sentences_count, processing_time, | |
| file_type, timestamp, status) | |
| VALUES (?,?,?,?,?,?,?,?,?,?,?,?)""", | |
| (student_id, student_name, metadata.get('file_hash', ''), | |
| ai_score, plagiarism_score, metadata.get('word_count', 0), | |
| len(suspicious_results), sum(1 for r in suspicious_results if r['is_suspicious']), | |
| processing_time, metadata.get('file_type', ''), | |
| datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'completed')) | |
| result_id = c.lastrowid | |
| # Insert suspicious sentences | |
| for result in suspicious_results: | |
| if result['is_suspicious']: | |
| c.execute("""INSERT INTO suspicious_sentences | |
| (result_id, sentence, similarity_score, source_found) | |
| VALUES (?,?,?,?)""", | |
| (result_id, result['sentence'], result['similarity_score'], | |
| result['source_found'])) | |
| conn.commit() | |
| conn.close() | |
| logger.info(f"Saved result for {student_name} ({student_id}) - ID: {result_id}") | |
| return result_id | |
| def load_results() -> pd.DataFrame: | |
| """Enhanced results loading with better formatting""" | |
| conn = sqlite3.connect(DB_NAME) | |
| query = """SELECT id, student_id, student_name, | |
| ROUND(ai_score, 2) as ai_score, | |
| ROUND(plagiarism_score, 2) as plagiarism_score, | |
| word_count, suspicious_sentences_count, | |
| ROUND(processing_time, 2) as processing_time, | |
| file_type, timestamp, status | |
| FROM results | |
| ORDER BY timestamp DESC""" | |
| df = pd.read_sql_query(query, conn) | |
| conn.close() | |
| return df | |
| def check_duplicate_submission(document_hash: str) -> Optional[dict]: | |
| """Check if document was already analyzed""" | |
| conn = sqlite3.connect(DB_NAME) | |
| c = conn.cursor() | |
| c.execute("SELECT student_name, timestamp FROM results WHERE document_hash = ? ORDER BY timestamp DESC LIMIT 1", | |
| (document_hash,)) | |
| result = c.fetchone() | |
| conn.close() | |
| if result: | |
| return {'student_name': result[0], 'timestamp': result[1]} | |
| return None | |
| # ----------------------------- | |
| # ENHANCED PDF REPORT WITH UNICODE SUPPORT | |
| # ----------------------------- | |
| def clean_text_for_pdf(text: str) -> str: | |
| """Clean text to be PDF-safe by removing/replacing problematic Unicode characters""" | |
| # Replace common Unicode characters with ASCII equivalents | |
| replacements = { | |
| '•': '-', # bullet point | |
| '–': '-', # en dash | |
| '—': '-', # em dash | |
| '"': '"', # left double quote | |
| '"': '"', # right double quote | |
| ''': "'", # left single quote | |
| ''': "'", # right single quote | |
| '…': '...', # ellipsis | |
| '®': '(R)', # registered trademark | |
| '©': '(C)', # copyright | |
| '™': '(TM)', # trademark | |
| '€': 'EUR', # euro sign | |
| '£': 'GBP', # pound sign | |
| '¥': 'JPY', # yen sign | |
| '§': 'Section', # section sign | |
| '¶': 'Para', # paragraph sign | |
| '†': '+', # dagger | |
| '‡': '++', # double dagger | |
| '°': ' degrees', # degree sign | |
| '±': '+/-', # plus-minus | |
| '÷': '/', # division sign | |
| '×': 'x', # multiplication sign | |
| '≤': '<=', # less than or equal | |
| '≥': '>=', # greater than or equal | |
| '≠': '!=', # not equal | |
| '∞': 'infinity', # infinity | |
| 'α': 'alpha', 'β': 'beta', 'γ': 'gamma', 'δ': 'delta', # Greek letters | |
| 'λ': 'lambda', 'μ': 'mu', 'π': 'pi', 'σ': 'sigma', 'Ω': 'Omega' | |
| } | |
| # Apply replacements | |
| for unicode_char, replacement in replacements.items(): | |
| text = text.replace(unicode_char, replacement) | |
| # Remove any remaining non-ASCII characters by encoding/decoding | |
| try: | |
| # Try to encode as latin-1 (which FPDF supports) | |
| text.encode('latin-1') | |
| return text | |
| except UnicodeEncodeError: | |
| # If that fails, remove non-ASCII characters | |
| text = text.encode('ascii', 'ignore').decode('ascii') | |
| return text | |
| class EnhancedPDF(FPDF): | |
| def header(self): | |
| if os.path.exists(LOGO_PATH): | |
| try: | |
| self.image(LOGO_PATH, 10, 8, 20) | |
| except: | |
| pass # Skip logo if there's an issue | |
| self.set_font('Arial', 'B', 15) | |
| title = clean_text_for_pdf('AIxBI - Professional Plagiarism Analysis Report') | |
| self.cell(0, 10, title, 0, 1, 'C') | |
| self.ln(10) | |
| def footer(self): | |
| self.set_y(-15) | |
| self.set_font('Arial', 'I', 8) | |
| footer_text = clean_text_for_pdf(f'Page {self.page_no()} | Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}') | |
| self.cell(0, 10, footer_text, 0, 0, 'C') | |
| def add_section_header(self, title: str): | |
| self.set_font('Arial', 'B', 12) | |
| self.set_fill_color(200, 220, 255) | |
| clean_title = clean_text_for_pdf(title) | |
| self.cell(0, 10, clean_title, 0, 1, 'L', 1) | |
| self.ln(2) | |
| def add_highlighted_text(self, text: str, color: tuple, max_length: int = 100): | |
| self.set_fill_color(*color) | |
| # Clean and truncate text | |
| clean_text = clean_text_for_pdf(text) | |
| display_text = clean_text[:max_length] + "..." if len(clean_text) > max_length else clean_text | |
| try: | |
| self.multi_cell(0, 8, display_text, 1, 'L', 1) | |
| except Exception as e: | |
| # Fallback: create a safe version | |
| safe_text = "Text contains unsupported characters - please check original document" | |
| self.multi_cell(0, 8, safe_text, 1, 'L', 1) | |
| self.ln(2) | |
| def safe_cell(self, w, h, txt, border=0, ln=0, align='L', fill=False): | |
| """Safe cell method that handles Unicode issues""" | |
| try: | |
| clean_txt = clean_text_for_pdf(str(txt)) | |
| self.cell(w, h, clean_txt, border, ln, align, fill) | |
| except Exception as e: | |
| # Fallback to a safe message | |
| self.cell(w, h, "[Content contains unsupported characters]", border, ln, align, fill) | |
| def safe_multi_cell(self, w, h, txt, border=0, align='L', fill=False): | |
| """Safe multi_cell method that handles Unicode issues""" | |
| try: | |
| clean_txt = clean_text_for_pdf(str(txt)) | |
| self.multi_cell(w, h, clean_txt, border, align, fill) | |
| except Exception as e: | |
| # Fallback to a safe message | |
| self.multi_cell(w, h, "[Content contains unsupported characters - please check source document]", border, align, fill) | |
| def generate_enhanced_pdf_report(student_name: str, student_id: str, ai_score: float, | |
| plagiarism_score: float, suspicious_results: List[dict], | |
| metadata: dict, ai_details: dict, output_path: str): | |
| """Generate comprehensive PDF report with Unicode safety""" | |
| try: | |
| pdf = EnhancedPDF() | |
| pdf.add_page() | |
| # Executive Summary | |
| pdf.add_section_header("EXECUTIVE SUMMARY") | |
| pdf.set_font('Arial', '', 10) | |
| summary_data = [ | |
| f"Student: {student_name} ({student_id})", | |
| f"Document Type: {metadata.get('file_type', 'Unknown').upper()}", | |
| f"Word Count: {metadata.get('word_count', 0):,}", | |
| f"AI Detection Score: {ai_score:.1f}% (Confidence: {ai_details.get('confidence', 'N/A')})", | |
| f"Plagiarism Score: {plagiarism_score:.1f}%", | |
| f"Suspicious Sentences: {sum(1 for r in suspicious_results if r['is_suspicious'])}", | |
| f"Analysis Date: {datetime.now().strftime('%B %d, %Y at %H:%M:%S')}" | |
| ] | |
| for item in summary_data: | |
| pdf.safe_cell(0, 6, item, 0, 1) | |
| pdf.ln(5) | |
| # Risk Assessment | |
| pdf.add_section_header("RISK ASSESSMENT") | |
| pdf.set_font('Arial', '', 10) | |
| risk_level = "HIGH" if (ai_score > 70 or plagiarism_score > 30) else "MEDIUM" if (ai_score > 40 or plagiarism_score > 15) else "LOW" | |
| risk_color = (255, 200, 200) if risk_level == "HIGH" else (255, 255, 200) if risk_level == "MEDIUM" else (200, 255, 200) | |
| pdf.set_fill_color(*risk_color) | |
| pdf.safe_cell(0, 10, f"Overall Risk Level: {risk_level}", 1, 1, 'C', 1) | |
| pdf.ln(5) | |
| # AI Detection Details | |
| if ai_details.get('chunk_scores'): | |
| pdf.add_section_header("AI DETECTION ANALYSIS") | |
| pdf.set_font('Arial', '', 9) | |
| pdf.safe_cell(0, 6, f"Chunks Analyzed: {len(ai_details['chunk_scores'])}", 0, 1) | |
| pdf.safe_cell(0, 6, f"Score Consistency (Std Dev): {ai_details.get('std_deviation', 'N/A')}", 0, 1) | |
| pdf.ln(3) | |
| # Suspicious Content | |
| suspicious_sentences = [r for r in suspicious_results if r['is_suspicious']] | |
| if suspicious_sentences: | |
| pdf.add_section_header("FLAGGED CONTENT") | |
| pdf.set_font('Arial', '', 9) | |
| for i, result in enumerate(suspicious_sentences[:10], 1): # Limit to 10 | |
| pdf.safe_cell(0, 6, f"Issue #{i} (Confidence: {result['confidence']:.1f})", 0, 1) | |
| pdf.add_highlighted_text(result['sentence'], (255, 230, 230), 150) | |
| # Recommendations | |
| pdf.add_section_header("RECOMMENDATIONS") | |
| pdf.set_font('Arial', '', 10) | |
| recommendations = [] | |
| if ai_score > 50: | |
| recommendations.append("- Review content for AI-generated sections and rewrite in original voice") | |
| if plagiarism_score > 20: | |
| recommendations.append("- Add proper citations for referenced material") | |
| recommendations.append("- Paraphrase flagged sentences to ensure originality") | |
| if len(suspicious_sentences) > 5: | |
| recommendations.append("- Conduct thorough revision focusing on highlighted sections") | |
| recommendations.extend([ | |
| "- Use plagiarism detection tools during writing process", | |
| "- Ensure all sources are properly attributed", | |
| "- Maintain academic integrity standards" | |
| ]) | |
| for rec in recommendations: | |
| pdf.safe_multi_cell(0, 6, rec) | |
| pdf.ln(1) | |
| # Generate PDF with error handling | |
| pdf.output(output_path) | |
| logger.info(f"PDF report generated successfully: {output_path}") | |
| except Exception as e: | |
| logger.error(f"Error generating PDF report: {e}") | |
| # Create a simple fallback PDF | |
| try: | |
| simple_pdf = FPDF() | |
| simple_pdf.add_page() | |
| simple_pdf.set_font('Arial', 'B', 16) | |
| simple_pdf.cell(0, 10, 'AIxBI Analysis Report', 0, 1, 'C') | |
| simple_pdf.ln(10) | |
| simple_pdf.set_font('Arial', '', 12) | |
| simple_pdf.cell(0, 10, f'Student: {clean_text_for_pdf(student_name)}', 0, 1) | |
| simple_pdf.cell(0, 10, f'Student ID: {clean_text_for_pdf(student_id)}', 0, 1) | |
| simple_pdf.cell(0, 10, f'AI Score: {ai_score:.1f}%', 0, 1) | |
| simple_pdf.cell(0, 10, f'Plagiarism Score: {plagiarism_score:.1f}%', 0, 1) | |
| simple_pdf.cell(0, 10, f'Date: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}', 0, 1) | |
| simple_pdf.ln(10) | |
| simple_pdf.multi_cell(0, 10, 'Note: Full report could not be generated due to character encoding issues. Please contact administrator if this persists.') | |
| simple_pdf.output(output_path) | |
| logger.info(f"Fallback PDF report generated: {output_path}") | |
| except Exception as fallback_error: | |
| logger.error(f"Even fallback PDF generation failed: {fallback_error}") | |
| raise Exception(f"PDF generation failed: {e}") | |
| # ----------------------------- | |
| # ENHANCED APP LOGIC | |
| # ----------------------------- | |
| def login(user: str, pwd: str): | |
| """Enhanced login with logging""" | |
| if user == USERNAME and pwd == PASSWORD: | |
| logger.info(f"Successful login for user: {user}") | |
| return gr.update(visible=False), gr.update(visible=True), "" | |
| else: | |
| logger.warning(f"Failed login attempt for user: {user}") | |
| return gr.update(), gr.update(), "❌ Invalid username or password!" | |
| def analyze_document(student_name: str, student_id: str, file_obj) -> Tuple: | |
| """Enhanced document analysis with comprehensive error handling""" | |
| start_time = time.time() | |
| # Input validation | |
| if not all([student_name.strip(), student_id.strip(), file_obj]): | |
| return "❌ Please fill all fields and upload a document.", None, None, None, None, None | |
| logger.info(f"Starting analysis for {student_name} ({student_id})") | |
| try: | |
| # Extract text and metadata using the working function | |
| result = extract_text_with_metadata(file_obj) | |
| if result is None or result[0] is None: | |
| return "❌ Error: Could not read the file. Please upload a valid PDF, DOCX, or TXT.", None, None, None, None, None | |
| text, metadata = result | |
| # Check for duplicate submission | |
| duplicate = check_duplicate_submission(metadata['file_hash']) | |
| if duplicate: | |
| logger.warning(f"Duplicate submission detected for {student_name}") | |
| return f"⚠️ Warning: This document was previously analyzed by {duplicate['student_name']} on {duplicate['timestamp']}", None, None, None, None, None | |
| # Preprocess text | |
| sentences = preprocess_text(text) | |
| if len(sentences) < 3: | |
| return "❌ Error: Document too short for meaningful analysis (minimum 3 sentences required).", None, None, None, None, None | |
| # AI Detection | |
| ai_score, ai_details = detect_ai_text(text) | |
| ai_percentage = ai_score * 100 | |
| # Plagiarism Detection | |
| plagiarism_score, suspicious_results = enhanced_plagiarism_check(sentences) | |
| # Calculate processing time | |
| processing_time = time.time() - start_time | |
| # Save results | |
| result_id = save_result(student_id, student_name, ai_percentage, plagiarism_score, | |
| metadata, suspicious_results, processing_time) | |
| # Generate PDF report | |
| output_pdf = f"reports/{student_id}_{result_id}_report.pdf" | |
| os.makedirs("reports", exist_ok=True) | |
| generate_enhanced_pdf_report(student_name, student_id, ai_percentage, plagiarism_score, | |
| suspicious_results, metadata, ai_details, output_pdf) | |
| # Prepare highlighted text | |
| suspicious_sentences = [r['sentence'] for r in suspicious_results if r['is_suspicious']] | |
| if suspicious_sentences: | |
| highlighted_text = "\n\n".join([f"🚨 FLAGGED: {s[:200]}..." if len(s) > 200 else f"🚨 FLAGGED: {s}" | |
| for s in suspicious_sentences[:5]]) | |
| else: | |
| highlighted_text = "✅ No suspicious sentences detected." | |
| # Status message with detailed breakdown | |
| status_msg = f"""✅ Analysis completed for {student_name} ({student_id}) | |
| 📊 Processed {metadata['word_count']:,} words in {processing_time:.1f} seconds | |
| 🤖 AI Detection: {ai_percentage:.1f}% (Confidence: {ai_details.get('confidence', 'N/A')}) | |
| 📋 Plagiarism: {plagiarism_score:.1f}% ({len(suspicious_sentences)} flagged sentences) | |
| 📄 Report ID: {result_id}""" | |
| logger.info(f"Analysis completed for {student_name} - AI: {ai_percentage:.1f}%, Plagiarism: {plagiarism_score:.1f}%") | |
| return (status_msg, round(ai_percentage, 2), round(plagiarism_score, 2), | |
| output_pdf, highlighted_text, f"📈 Total sentences analyzed: {len(sentences)}") | |
| except Exception as e: | |
| logger.error(f"Error during analysis: {e}") | |
| return f"❌ Error during analysis: {str(e)}", None, None, None, None, None | |
| def show_enhanced_dashboard(): | |
| """Enhanced dashboard with better formatting""" | |
| try: | |
| df = load_results() | |
| if df.empty: | |
| return pd.DataFrame({"Message": ["No analysis results found. Upload and analyze documents to see data here."]}) | |
| return df | |
| except Exception as e: | |
| logger.error(f"Error loading dashboard: {e}") | |
| return pd.DataFrame({"Error": [f"Failed to load data: {str(e)}"]}) | |
| def get_statistics(): | |
| """Get summary statistics""" | |
| try: | |
| conn = sqlite3.connect(DB_NAME) | |
| c = conn.cursor() | |
| # Basic stats | |
| c.execute("SELECT COUNT(*), AVG(ai_score), AVG(plagiarism_score), AVG(processing_time) FROM results") | |
| stats = c.fetchone() | |
| # High risk documents | |
| c.execute("SELECT COUNT(*) FROM results WHERE ai_score > 70 OR plagiarism_score > 30") | |
| high_risk = c.fetchone()[0] | |
| conn.close() | |
| if stats[0] == 0: | |
| return "No analyses completed yet." | |
| return f"""📊 **Analysis Statistics** | |
| Total Documents Analyzed: {stats[0]:,} | |
| Average AI Score: {stats[1]:.1f}% | |
| Average Plagiarism Score: {stats[2]:.1f}% | |
| Average Processing Time: {stats[3]:.1f}s | |
| High Risk Documents: {high_risk} ({(high_risk/stats[0]*100):.1f}%)""" | |
| except Exception as e: | |
| logger.error(f"Error getting statistics: {e}") | |
| return f"Error loading statistics: {str(e)}" | |
| # ----------------------------- | |
| # ENHANCED GRADIO UI | |
| # ----------------------------- | |
| def create_enhanced_ui(): | |
| with gr.Blocks(theme="soft", title="AIxBI - Professional Plagiarism Detection") as demo: | |
| # Header | |
| with gr.Row(): | |
| if os.path.exists(LOGO_PATH): | |
| gr.Image(LOGO_PATH, height=80, width=80, show_label=False, container=False) | |
| with gr.Column(): | |
| gr.Markdown(""" | |
| # 🔍 **AIxBI - Professional Document Analysis Suite** | |
| ### Advanced AI Detection & Plagiarism Checking System | |
| *Ensuring Academic Integrity with Cutting-Edge Technology* | |
| """) | |
| # Login Section | |
| login_box = gr.Group(visible=True) | |
| with login_box: | |
| gr.Markdown("## 🔐 **Secure Login**") | |
| with gr.Row(): | |
| user = gr.Textbox(label="👤 Username", placeholder="Enter username") | |
| pwd = gr.Textbox(label="🔑 Password", type="password", placeholder="Enter password") | |
| login_btn = gr.Button("🚀 Login", variant="primary", size="lg") | |
| login_msg = gr.Markdown("", elem_classes="login-message") | |
| # Main Application | |
| app_box = gr.Group(visible=False) | |
| with app_box: | |
| with gr.Tabs(): | |
| # Analysis Tab | |
| with gr.Tab("📄 Document Analysis", elem_id="analysis-tab"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 👨🎓 **Student Information**") | |
| student_name = gr.Textbox(label="📝 Student Name", placeholder="Enter full name") | |
| student_id = gr.Textbox(label="🆔 Student ID", placeholder="Enter student ID") | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 📎 **Document Upload**") | |
| file_upload = gr.File( | |
| label="📄 Upload Document", | |
| file_types=[".pdf", ".docx", ".txt"], | |
| file_count="single" | |
| ) | |
| analyze_btn = gr.Button("🔍 Analyze Document", variant="primary", size="lg") | |
| with gr.Row(): | |
| with gr.Column(): | |
| status = gr.Textbox(label="📊 Analysis Status", lines=4, interactive=False) | |
| doc_info = gr.Textbox(label="📋 Document Information", interactive=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| ai_score = gr.Number(label="🤖 AI Detection Score (%)", interactive=False) | |
| plagiarism_score = gr.Number(label="📋 Plagiarism Score (%)", interactive=False) | |
| suspicious_text = gr.Textbox( | |
| label="🚨 Flagged Content", | |
| lines=8, | |
| placeholder="Suspicious sentences will appear here...", | |
| interactive=False | |
| ) | |
| pdf_output = gr.File(label="📄 Download Detailed Report") | |
| # Dashboard Tab | |
| with gr.Tab("📊 Analysis Dashboard", elem_id="dashboard-tab"): | |
| with gr.Row(): | |
| dashboard_btn = gr.Button("🔄 Refresh Dashboard", variant="secondary") | |
| stats_btn = gr.Button("📈 Show Statistics", variant="secondary") | |
| stats_display = gr.Markdown("", elem_classes="stats-display") | |
| dashboard = gr.Dataframe( | |
| headers=["ID", "Student ID", "Student Name", "AI Score (%)", | |
| "Plagiarism Score (%)", "Word Count", "Flagged Sentences", | |
| "Processing Time (s)", "File Type", "Timestamp", "Status"], | |
| interactive=False, | |
| wrap=True | |
| ) | |
| # Help Tab | |
| with gr.Tab("❓ Help & Guidelines", elem_id="help-tab"): | |
| gr.Markdown(""" | |
| ## 📖 **User Guide** | |
| ### 🎯 **How to Use** | |
| 1. **Login** with your credentials | |
| 2. **Enter student information** (name and ID) | |
| 3. **Upload document** (PDF, DOCX, or TXT format) | |
| 4. **Click "Analyze Document"** and wait for results | |
| 5. **Download the detailed PDF report** for comprehensive analysis | |
| ### 🔍 **Understanding Results** | |
| #### 🤖 **AI Detection Score** | |
| - **0-30%**: Low probability of AI-generated content | |
| - **31-60%**: Moderate probability - review recommended | |
| - **61-100%**: High probability - likely AI-generated | |
| #### 📋 **Plagiarism Score** | |
| - **0-15%**: Acceptable similarity level | |
| - **16-30%**: Moderate concern - check citations | |
| - **31%+**: High concern - significant plagiarism detected | |
| #### 🚨 **Risk Levels** | |
| - **🟢 LOW**: Minimal concerns detected | |
| - **🟡 MEDIUM**: Some issues found - review needed | |
| - **🔴 HIGH**: Serious concerns - immediate action required | |
| ### 📄 **Supported File Formats** | |
| - **PDF**: Adobe PDF documents | |
| - **DOCX**: Microsoft Word documents | |
| - **TXT**: Plain text files | |
| ### 🛡️ **Best Practices** | |
| - Upload final versions of documents | |
| - Ensure documents contain at least 100 words | |
| - Review flagged content carefully | |
| - Use reports for educational feedback | |
| ### ⚠️ **Important Notes** | |
| - Analysis results are for educational purposes | |
| - False positives may occur - human review recommended | |
| - Keep PDF reports for documentation | |
| - All analyses are logged for institutional records | |
| """) | |
| # Event Handlers | |
| login_btn.click( | |
| fn=login, | |
| inputs=[user, pwd], | |
| outputs=[login_box, app_box, login_msg] | |
| ) | |
| analyze_btn.click( | |
| fn=analyze_document, | |
| inputs=[student_name, student_id, file_upload], | |
| outputs=[status, ai_score, plagiarism_score, pdf_output, suspicious_text, doc_info] | |
| ) | |
| dashboard_btn.click( | |
| fn=show_enhanced_dashboard, | |
| outputs=[dashboard] | |
| ) | |
| stats_btn.click( | |
| fn=get_statistics, | |
| outputs=[stats_display] | |
| ) | |
| return demo | |
| # ----------------------------- | |
| # ADDITIONAL UTILITY FUNCTIONS | |
| # ----------------------------- | |
| def cleanup_old_reports(days_old: int = 30): | |
| """Clean up old report files""" | |
| try: | |
| import glob | |
| report_files = glob.glob("reports/*.pdf") | |
| current_time = time.time() | |
| for file_path in report_files: | |
| if os.path.getmtime(file_path) < (current_time - days_old * 24 * 60 * 60): | |
| os.remove(file_path) | |
| logger.info(f"Cleaned up old report: {file_path}") | |
| except Exception as e: | |
| logger.error(f"Error during cleanup: {e}") | |
| def export_database_backup(): | |
| """Export database to CSV for backup""" | |
| try: | |
| df = load_results() | |
| backup_file = f"backup_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv" | |
| df.to_csv(backup_file, index=False) | |
| logger.info(f"Database backup created: {backup_file}") | |
| return backup_file | |
| except Exception as e: | |
| logger.error(f"Error creating backup: {e}") | |
| return None | |
| def validate_system_requirements(): | |
| """Check if all required components are available""" | |
| requirements = { | |
| "Models loaded": embedder is not None and model is not None, | |
| "Database accessible": os.path.exists(DB_NAME), | |
| "Reports directory": os.path.exists("reports") or os.makedirs("reports", exist_ok=True) or True, | |
| "Logo file": os.path.exists(LOGO_PATH) | |
| } | |
| for requirement, status in requirements.items(): | |
| if status: | |
| logger.info(f"✅ {requirement}") | |
| else: | |
| logger.warning(f"❌ {requirement}") | |
| return all(requirements.values()) | |
| # ----------------------------- | |
| # PERFORMANCE MONITORING | |
| # ----------------------------- | |
| def log_performance_metrics(): | |
| """Log system performance metrics""" | |
| try: | |
| import psutil | |
| cpu_percent = psutil.cpu_percent() | |
| memory_percent = psutil.virtual_memory().percent | |
| disk_usage = psutil.disk_usage('.').percent | |
| logger.info(f"Performance - CPU: {cpu_percent}%, Memory: {memory_percent}%, Disk: {disk_usage}%") | |
| # Log database size | |
| if os.path.exists(DB_NAME): | |
| db_size = os.path.getsize(DB_NAME) / (1024 * 1024) # MB | |
| logger.info(f"Database size: {db_size:.2f} MB") | |
| except ImportError: | |
| logger.warning("psutil not available - performance monitoring disabled") | |
| except Exception as e: | |
| logger.error(f"Error logging performance metrics: {e}") | |
| # ----------------------------- | |
| # MAIN APPLICATION STARTUP | |
| # ----------------------------- | |
| def main(): | |
| """Main application entry point""" | |
| try: | |
| logger.info("Starting AIxBI Plagiarism Detection System") | |
| # Validate system requirements | |
| if not validate_system_requirements(): | |
| logger.error("System requirements not met. Please check the logs.") | |
| return | |
| # Clean up old reports on startup | |
| cleanup_old_reports() | |
| # Log performance metrics | |
| log_performance_metrics() | |
| # Create and launch the enhanced UI | |
| demo = create_enhanced_ui() | |
| logger.info("System ready - launching web interface") | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False, | |
| show_error=True, | |
| quiet=False | |
| ) | |
| except Exception as e: | |
| logger.error(f"Failed to start application: {e}") | |
| raise | |
| if __name__ == "__main__": | |
| main() |