Update app.py

app.py

@@ -1,566 +1,656 @@
-# app.py - Gradio Interface for Advanced Multi-Language OCR System
-# Hugging Face Spaces compatible application
-
-import os
-import json
-import shutil
-from datetime import datetime
-from pathlib import Path
-from typing import Tuple
-import gradio as gr
-
-# Import our OCR functionality
-from main6_pix2text import extract_all_text_advanced_pix2text, initialize_pix2text
-from eval import evaluate_ocr_accuracy, clean_control_characters
-
-# Set up logging
-import logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-# Create necessary directories
-def create_directories():
-    """Create necessary directories for file storage."""
-    directories = ["documents", "extracted", "temp"]
-    for directory in directories:
-        Path(directory).mkdir(exist_ok=True)
-        logger.info(f"✅ Created/verified directory: {directory}")
-
-
-# Initialize directories
-create_directories()
-
-# Initialize Pix2Text model at startup
-logger.info("🚀 Initializing Pix2Text model...")
-PIX2TEXT_MODEL = initialize_pix2text()
-if PIX2TEXT_MODEL:
-    logger.info("✅ Pix2Text model loaded successfully")
-else:
-    logger.warning("⚠️ Pix2Text model not available, using fallback OCR")
-
-
-def get_safe_filename(filename: str) -> str:
-    """Generate a safe filename with timestamp."""
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    name, ext = os.path.splitext(filename)
-    # Remove special characters and replace spaces
-    safe_name = "".join(c for c in name if c.isalnum() or c in ("-", "_")).rstrip()
-    return f"{safe_name}_{timestamp}{ext}"
-
-
-def get_extraction_filename(pdf_filename: str, file_type: str) -> str:
-    """Generate extraction filename with convention: [pdf_filename]_extract.[extension]"""
-    base_name = os.path.splitext(pdf_filename)[0]
-    extensions = {"txt": "txt", "json": "json", "analysis": "json"}
-    return f"{base_name}_extract.{extensions.get(file_type, 'txt')}"
-
-
-def extract_text_from_pdf(pdf_file) -> Tuple[str, str, str, str]:
-    """
-    Extract text from uploaded PDF file using advanced OCR.
-
-    Returns:
-        - extracted_text: The full extracted text
-        - summary_text: A summary of the extraction process
-        - text_file_path: Path to the text file (for download)
-        - json_file_path: Path to the JSON file (for download)
-    """
-    if pdf_file is None:
-        return "❌ No file uploaded", "Please upload a PDF file", "", ""
-
-    try:
-        start_time = datetime.now()
-
-        # Get the uploaded file path
-        pdf_path = pdf_file.name
-        filename = os.path.basename(pdf_path)
-
-        logger.info(f"📄 Processing uploaded file: {filename}")
-
-        # Generate safe filename
-        safe_filename = get_safe_filename(filename)
-
-        # Copy uploaded file to documents directory
-        documents_path = Path("documents") / safe_filename
-        shutil.copy2(pdf_path, documents_path)
-
-        # Generate output filenames
-        text_filename = get_extraction_filename(safe_filename, "txt")
-        json_filename = get_extraction_filename(safe_filename, "json")
-        analysis_filename = get_extraction_filename(safe_filename, "analysis")
-
-        # Create full paths for extracted files
-        text_path = Path("extracted") / text_filename
-        json_path = Path("extracted") / json_filename
-        analysis_path = Path("extracted") / analysis_filename
-
-        logger.info("🔄 Starting OCR processing...")
-
-        # Process the PDF using our advanced OCR system
-        extract_all_text_advanced_pix2text(
-            pdf_path=str(documents_path),
-            output_text_file=str(text_path),
-            output_json_file=str(json_path),
-            output_analysis_file=str(analysis_path),
-        )
-
-        # Read the extracted text
-        with open(text_path, "r", encoding="utf-8") as f:
-            extracted_text = f.read()
-
-        # Read the analysis for summary
-        with open(analysis_path, "r", encoding="utf-8") as f:
-            analysis_data = json.load(f)
-
-        # Calculate processing time
-        end_time = datetime.now()
-        processing_time = (end_time - start_time).total_seconds()
-
-        # Create summary
-        summary = f"""
-📊 **OCR Processing Complete!**
-
-⏱️ **Processing Time:** {processing_time:.2f} seconds
-📄 **Original File:** {filename}
-📝 **Extracted Characters:** {len(extracted_text):,}
-
-🔤 **Text Distribution:**
-- English regions: {analysis_data.get("type_distribution", {}).get("english", 0)}
-- Bangla regions: {analysis_data.get("type_distribution", {}).get("bangla", 0)}
-- Math regions: {analysis_data.get("type_distribution", {}).get("math", 0)}
-- Mixed regions: {analysis_data.get("type_distribution", {}).get("mixed", 0)}
-
-📈 **Quality Metrics:**
-- Total text regions: {analysis_data.get("total_regions", 0)}
-- Pages processed: {analysis_data.get("total_pages", 0)}
-- Average confidence: {analysis_data.get("confidence_stats", {}).get("avg", 0):.1f}%
-
-🔧 **Extraction Methods:**
-- Pix2Text (Math): {analysis_data.get("extraction_methods", {}).get("pix2text", 0)} regions
-- Tesseract (Text): {analysis_data.get("extraction_methods", {}).get("tesseract", 0)} regions
-
-✅ **Status:** Extraction completed successfully!
-"""
-
-        logger.info(f"✅ OCR processing completed in {processing_time:.2f} seconds")
-
-        return extracted_text, summary, str(text_path), str(json_path)
-
-    except Exception as e:
-        error_message = f"❌ **Error during OCR processing:**\n\n{str(e)}"
-        logger.error(f"OCR processing failed: {e}")
-        return error_message, error_message, "", ""
-
-
-def evaluate_ocr_files(
-    extracted_file, baseline_file, evaluation_name: str = ""
-) -> Tuple[str, str]:
-    """
-    Evaluate OCR accuracy by comparing extracted text with baseline.
-
-    Returns:
-        - results_text: Formatted evaluation results
-        - summary_text: Summary of the evaluation
-    """
-    if extracted_file is None or baseline_file is None:
-        return "❌ Please upload both files for evaluation", "Missing files"
-
-    try:
-        start_time = datetime.now()
-
-        # Read file contents
-        with open(extracted_file.name, "r", encoding="utf-8") as f:
-            extracted_text = f.read()
-
-        with open(baseline_file.name, "r", encoding="utf-8") as f:
-            baseline_text = f.read()
-
-        logger.info(f"📊 Starting evaluation: {evaluation_name or 'Unnamed'}")
-        logger.info(f"Extracted text length: {len(extracted_text)} characters")
-        logger.info(f"Baseline text length: {len(baseline_text)} characters")
-
-        # Clean input texts
-        extracted_text_clean = clean_control_characters(extracted_text)
-        baseline_text_clean = clean_control_characters(baseline_text)
-
-        # Perform evaluation
-        evaluation_results = evaluate_ocr_accuracy(
-            extracted_text=extracted_text_clean,
-            baseline_text=baseline_text_clean,
-        )
-
-        # Check for evaluation errors
-        if "error" in evaluation_results:
-            return (
-                f"❌ **Evaluation Error:** {evaluation_results['error']}",
-                "Error occurred",
-            )
-
-        # Calculate processing time
-        end_time = datetime.now()
-        processing_time = (end_time - start_time).total_seconds()
-
-        # Format results
-        results_text = f"""
-📊 **OCR Evaluation Results**
-{f"📝 **Evaluation Name:** {evaluation_name}" if evaluation_name else ""}
-
-🎯 **Overall Performance**
-- **Overall Accuracy:** {evaluation_results["overall_accuracy"]:.2f}%
-- **Similarity Score:** {evaluation_results["similarity_score"]:.2f}%
-- **Grade:** {evaluation_results["evaluation_summary"]["grade"]}
-
-📝 **Character-Level Analysis**
-- **Character Accuracy:** {evaluation_results["character_metrics"]["character_accuracy"]:.2f}%
-- **Character Error Rate:** {evaluation_results["character_metrics"]["character_error_rate"]:.2f}%
-- **Edit Distance:** {evaluation_results["character_metrics"]["edit_distance"]}
-- **Total Characters:** {evaluation_results["character_metrics"]["total_characters"]:,}
-
-📚 **Word-Level Analysis**
-- **Word Accuracy:** {evaluation_results["word_metrics"]["word_accuracy"]:.2f}%
-- **Word Error Rate:** {evaluation_results["word_metrics"]["word_error_rate"]:.2f}%
-- **Correct Words:** {evaluation_results["word_metrics"]["correct_words"]} / {evaluation_results["word_metrics"]["total_words"]}
-- **Missing Words:** {evaluation_results["word_metrics"]["missing_words"]}
-- **Extra Words:** {evaluation_results["word_metrics"]["extra_words"]}
-
-📄 **Line-Level Analysis**
-- **Line Accuracy:** {evaluation_results["line_metrics"]["line_accuracy"]:.2f}%
-- **Average Line Similarity:** {evaluation_results["line_metrics"]["average_line_similarity"]:.2f}%
-- **Lines Matched:** {evaluation_results["line_metrics"]["lines_matched"]} / {evaluation_results["line_metrics"]["total_lines"]}
-
-🌐 **Language-Specific Accuracy**
-- **English:** {evaluation_results["language_specific"].get("english_accuracy", "N/A")}%
-- **Bangla:** {evaluation_results["language_specific"].get("bangla_accuracy", "N/A")}%
-- **Mathematics:** {evaluation_results["language_specific"].get("math_accuracy", "N/A")}%
-- **Numbers:** {evaluation_results["language_specific"].get("number_accuracy", "N/A")}%
-
-📈 **Text Statistics**
-- **Extracted Length:** {evaluation_results["text_statistics"]["extracted_length"]:,} characters
-- **Baseline Length:** {evaluation_results["text_statistics"]["baseline_length"]:,} characters
-- **Extracted Words:** {evaluation_results["text_statistics"]["extracted_words"]:,}
-- **Baseline Words:** {evaluation_results["text_statistics"]["baseline_words"]:,}
-
-💡 **Recommendations**
-"""
-
-        for i, rec in enumerate(
-            evaluation_results["evaluation_summary"]["recommendations"], 1
-        ):
-            results_text += f"{i}. {rec}\n"
-
-        # Create summary
-        summary = f"""
-🎯 **Evaluation Summary**
-
-⏱️ **Processing Time:** {processing_time:.3f} seconds
-📊 **Overall Score:** {evaluation_results["overall_accuracy"]:.2f}%
-🏆 **Grade:** {evaluation_results["evaluation_summary"]["grade"]}
-📝 **Character Accuracy:** {evaluation_results["character_metrics"]["character_accuracy"]:.2f}%
-📚 **Word Accuracy:** {evaluation_results["word_metrics"]["word_accuracy"]:.2f}%
-
-✅ **Evaluation completed successfully!**
-"""
-
-        logger.info(f"✅ Evaluation completed in {processing_time:.3f} seconds")
-        logger.info(
-            f"📊 Overall accuracy: {evaluation_results['overall_accuracy']:.2f}%"
-        )
-
-        return results_text, summary
-
-    except Exception as e:
-        error_message = f"❌ **Error during evaluation:**\n\n{str(e)}"
-        logger.error(f"Evaluation failed: {e}")
-        return error_message, error_message
-
-
-# Create Gradio interface
-def create_gradio_interface():
-    """Create and configure the Gradio interface."""
-
-    # Custom CSS for better styling
-    css = """
-    .gradio-container {
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-    }
-    .output-text {
-        font-family: 'Courier New', monospace;
-        font-size: 14px;
-    }
-    .summary-box {
-        background-color: #f0f8ff;
-        border: 1px solid #d0e7ff;
-        border-radius: 8px;
-        padding: 16px;
-        margin: 8px 0;
-    }
-    """
-
-    with gr.Blocks(
-        css=css, title="Advanced Multi-Language OCR System", theme=gr.themes.Soft()
-    ) as app:
-        # Header
-        gr.Markdown("""
-        # 🔍 Advanced Multi-Language OCR System
-        
-        **Powered by Pix2Text, Tesseract, and FastAPI**
-        
-        Extract text from PDFs containing **English**, **Bangla**, and **Mathematical expressions** with high accuracy.
-        Evaluate OCR performance with comprehensive metrics and detailed analysis.
-        """)
-
-        with gr.Tabs():
-            # Tab 1: OCR Extraction
-            with gr.Tab("📄 PDF Text Extraction"):
-                gr.Markdown("""
-                ### Upload a PDF and extract text using advanced multi-language OCR
-                
-                **Features:**
-                - 🌐 **Multi-language support**: English, Bangla (Bengali), and Mathematical expressions
-                - 🧮 **Advanced Math Recognition**: Pix2Text integration for LaTeX and mathematical formulas
-                - 📊 **Detailed Analysis**: Character-level classification and confidence scores
-                - 💾 **Download Results**: Get extracted text and detailed JSON analysis
-                """)
-
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        pdf_input = gr.File(
-                            label="📄 Upload PDF File",
-                            file_types=[".pdf"],
-                            type="filepath",
-                        )
-                        extract_btn = gr.Button(
-                            "🚀 Extract Text", variant="primary", size="lg"
-                        )
-
-                    with gr.Column(scale=2):
-                        extraction_summary = gr.Textbox(
-                            label="📊 Extraction Summary",
-                            lines=15,
-                            elem_classes=["summary-box"],
-                        )
-
-                with gr.Row():
-                    extracted_text_output = gr.Textbox(
-                        label="📝 Extracted Text",
-                        lines=20,
-                        elem_classes=["output-text"],
-                        show_copy_button=True,
-                    )
-
-                with gr.Row():
-                    text_file_download = gr.File(
-                        label="📥 Download Text File", visible=False
-                    )
-                    json_file_download = gr.File(
-                        label="📥 Download JSON Analysis", visible=False
-                    )
-
-                # Connect extraction functionality
-                extract_btn.click(
-                    fn=extract_text_from_pdf,
-                    inputs=[pdf_input],
-                    outputs=[
-                        extracted_text_output,
-                        extraction_summary,
-                        text_file_download,
-                        json_file_download,
-                    ],
-                ).then(
-                    lambda text_path, json_path: (
-                        gr.update(
-                            visible=bool(text_path),
-                            value=text_path if text_path else None,
-                        ),
-                        gr.update(
-                            visible=bool(json_path),
-                            value=json_path if json_path else None,
-                        ),
-                    ),
-                    inputs=[text_file_download, json_file_download],
-                    outputs=[text_file_download, json_file_download],
-                )
-
-            # Tab 2: OCR Evaluation
-            with gr.Tab("📊 OCR Accuracy Evaluation"):
-                gr.Markdown("""
-                ### Compare OCR extracted text with ground truth baseline for accuracy analysis
-                
-                **Evaluation Features:**
-                - 🎯 **Character-level accuracy**: Precise character matching and edit distance
-                - 📚 **Word-level accuracy**: Word matching and error rates  
-                - 📄 **Line-level accuracy**: Line comparison and similarity scores
-                - 🌐 **Language-specific metrics**: Separate accuracy for English, Bangla, and Math
-                - 🏆 **Grading system**: Letter grades from A+ to F with recommendations
-                """)
-
-                with gr.Row():
-                    with gr.Column():
-                        extracted_file_input = gr.File(
-                            label="📄 OCR Extracted Text File (.txt)",
-                            file_types=[".txt"],
-                            type="filepath",
-                        )
-                        baseline_file_input = gr.File(
-                            label="📑 Ground Truth Baseline File (.txt)",
-                            file_types=[".txt"],
-                            type="filepath",
-                        )
-                        evaluation_name_input = gr.Textbox(
-                            label="📝 Evaluation Name (Optional)",
-                            placeholder="e.g., Math Document Test #1",
-                        )
-                        evaluate_btn = gr.Button(
-                            "📊 Evaluate Accuracy", variant="primary", size="lg"
-                        )
-
-                    with gr.Column():
-                        evaluation_summary = gr.Textbox(
-                            label="🎯 Evaluation Summary",
-                            lines=10,
-                            elem_classes=["summary-box"],
-                        )
-
-                with gr.Row():
-                    evaluation_results = gr.Textbox(
-                        label="📈 Detailed Evaluation Results",
-                        lines=25,
-                        elem_classes=["output-text"],
-                        show_copy_button=True,
-                    )
-
-                # Connect evaluation functionality
-                evaluate_btn.click(
-                    fn=evaluate_ocr_files,
-                    inputs=[
-                        extracted_file_input,
-                        baseline_file_input,
-                        evaluation_name_input,
-                    ],
-                    outputs=[evaluation_results, evaluation_summary],
-                )
-
-            # Tab 3: About & Help
-            with gr.Tab("ℹ️ About & Help"):
-                gr.Markdown("""
-                ## 🔍 Advanced Multi-Language OCR System
-                
-                This application provides state-of-the-art Optical Character Recognition (OCR) for documents containing mixed languages and mathematical expressions.
-                
-                ### 🌟 Key Features
-                
-                #### 📄 **PDF Text Extraction**
-                - **Multi-language Support**: Simultaneously process English and Bangla (Bengali) text
-                - **Mathematical Recognition**: Advanced extraction of mathematical formulas and equations using Pix2Text
-                - **Intelligent Classification**: Automatic detection and classification of text regions by language/content type
-                - **High Accuracy**: Optimized preprocessing and multiple OCR engines for best results
-                - **Detailed Analysis**: Character-by-character analysis with confidence scores and language distribution
-                
-                #### 📊 **OCR Accuracy Evaluation**
-                - **Comprehensive Metrics**: Character, word, and line-level accuracy measurements
-                - **Language-Specific Analysis**: Separate accuracy scores for different languages and mathematical content
-                - **Edit Distance Calculation**: Precise measurement of text differences using Levenshtein distance
-                - **Grading System**: Letter grades (A+ to F) with improvement recommendations
-                - **Detailed Comparison**: Side-by-side diff analysis showing insertions, deletions, and matches
-                
-                ### 🛠️ **Technology Stack**
-                
-                - **Pix2Text**: Advanced mathematical expression recognition
-                - **Tesseract OCR**: Multi-language text recognition with Bengali support
-                - **OpenCV**: Image preprocessing and enhancement
-                - **PDF2Image**: High-quality PDF to image conversion
-                - **FastAPI**: RESTful API backend
-                - **Gradio**: Interactive web interface
-                
-                ### 📝 **Usage Instructions**
-                
-                #### **For PDF Text Extraction:**
-                1. Upload a PDF file using the file picker
-                2. Click "🚀 Extract Text" to start processing
-                3. Review the extraction summary for statistics
-                4. Copy the extracted text or download the files
-                5. Download the JSON file for detailed analysis data
-                
-                #### **For OCR Evaluation:**
-                1. Upload the OCR-extracted text file (what you want to evaluate)
-                2. Upload the ground truth baseline file (the correct text)
-                3. Optionally provide an evaluation name for identification
-                4. Click "📊 Evaluate Accuracy" to run the comparison
-                5. Review the detailed metrics and recommendations
-                
-                ### 🎯 **Accuracy Grading System**
-                
-                - **A+ (95-100%)**: Excellent - Professional-grade accuracy
-                - **A (90-94%)**: Very Good - High-quality results with minor errors
-                - **B (80-89%)**: Good - Acceptable for most applications
-                - **C (70-79%)**: Fair - May require manual review
-                - **D (60-69%)**: Poor - Significant improvements needed
-                - **F (<60%)**: Very Poor - Major issues requiring attention
-                
-                ### 📚 **Supported Languages & Content**
-                
-                - **English**: Full Latin alphabet with punctuation and symbols
-                - **Bangla (Bengali)**: Complete Bengali Unicode range (U+0980-U+09FF)
-                - **Mathematical Expressions**: 
-                  - Basic arithmetic operators (+, -, ×, ÷, =)
-                  - Greek letters (α, β, γ, δ, π, θ, λ, μ, Ω, etc.)
-                  - Mathematical symbols (∑, ∫, √, ∞, ∂, →, ≤, ≥, etc.)
-                  - Subscripts and superscripts
-                  - Functions and equations
-                  - LaTeX-style expressions
-                
-                ### 🔧 **Tips for Best Results**
-                
-                1. **PDF Quality**: Use high-resolution PDFs (300+ DPI) for better accuracy
-                2. **Text Clarity**: Ensure text is not blurry, skewed, or low contrast
-                3. **Language Consistency**: Mixed-language documents work best when languages are clearly separated
-                4. **Mathematical Content**: Complex equations may require manual verification
-                5. **File Size**: Larger documents may take longer to process
-                
-                ### 🐛 **Troubleshooting**
-                
-                - **Empty Results**: Check if the PDF contains selectable text or if images need OCR
-                - **Low Accuracy**: Try preprocessing the PDF to improve image quality
-                - **Mixed Languages**: Ensure the document has clear language boundaries
-                - **Mathematical Errors**: Complex formulas may need manual correction
-                
-                ### 📞 **Support & Feedback**
-                
-                For issues, suggestions, or contributions, please visit our [GitHub repository](https://github.com/ashfaqbracu/aaladinai).
-                
-                ---
-                
-                **Made with ❤️ for advancing multilingual text recognition**
-                """)
-
-        # Footer
-        gr.Markdown("""
-        ---
-        
-        **🔗 Links:** [GitHub Repository](https://github.com/ashfaqbracu/aaladinai) | [Documentation](https://github.com/ashfaqbracu/aaladinai#readme)
-        
-        **⚡ Powered by:** Pix2Text • Tesseract OCR • OpenCV • FastAPI • Gradio
-        """)
-
-    return app
-
-
-# Main execution
-if __name__ == "__main__":
-    logger.info("🚀 Starting Advanced Multi-Language OCR Gradio Interface...")
-
-    # Create and launch the interface
-    app = create_gradio_interface()
-
-    # Launch configuration
-    app.launch(
-        server_name="0.0.0.0",  # Allow external access for Hugging Face Spaces
-        server_port=7860,  # Standard port for Hugging Face Spaces
-        share=False,  # Don't create gradio.live link
-        show_error=True,  # Show detailed error messages
-        show_tips=True,  # Show helpful tips
-        enable_queue=True,  # Enable request queuing for better performance
-        max_threads=4,  # Limit concurrent requests
-    )
+# app.py - Gradio Interface for Advanced Multi-Language OCR System
+# Hugging Face Spaces compatible application
+
+import os
+import json
+import shutil
+from datetime import datetime
+from pathlib import Path
+from typing import Tuple
+import gradio as gr
+
+# Set up logging first
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Try to import our OCR functionality with error handling
+try:
+    from main6_pix2text import extract_all_text_advanced_pix2text, initialize_pix2text
+    from eval import evaluate_ocr_accuracy, clean_control_characters
+
+    OCR_AVAILABLE = True
+    logger.info("✅ OCR modules imported successfully")
+except ImportError as e:
+    logger.error(f"❌ OCR modules not available: {e}")
+    OCR_AVAILABLE = False
+
+    # Create dummy functions as fallbacks
+    def extract_all_text_advanced_pix2text(*args, **kwargs):
+        raise RuntimeError(
+            "OCR functionality not available due to missing dependencies"
+        )
+
+    def initialize_pix2text():
+        return None
+
+    def evaluate_ocr_accuracy(*args, **kwargs):
+        raise RuntimeError(
+            "Evaluation functionality not available due to missing dependencies"
+        )
+
+    def clean_control_characters(text):
+        return text
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Create necessary directories
+def create_directories():
+    """Create necessary directories for file storage."""
+    directories = ["documents", "extracted", "temp"]
+    for directory in directories:
+        Path(directory).mkdir(exist_ok=True)
+        logger.info(f"✅ Created/verified directory: {directory}")
+
+
+# Initialize directories
+create_directories()
+
+# Initialize Pix2Text model at startup with error handling
+logger.info("🚀 Initializing Pix2Text model...")
+if OCR_AVAILABLE:
+    try:
+        PIX2TEXT_MODEL = initialize_pix2text()
+        if PIX2TEXT_MODEL:
+            logger.info("✅ Pix2Text model loaded successfully")
+        else:
+            logger.warning("⚠️ Pix2Text model not available, using fallback OCR")
+    except Exception as e:
+        logger.error(f"❌ Failed to initialize Pix2Text: {e}")
+        PIX2TEXT_MODEL = None
+else:
+    logger.warning("⚠️ OCR modules not available - running in demo mode")
+    PIX2TEXT_MODEL = None
+
+
+def get_safe_filename(filename: str) -> str:
+    """Generate a safe filename with timestamp."""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    name, ext = os.path.splitext(filename)
+    # Remove special characters and replace spaces
+    safe_name = "".join(c for c in name if c.isalnum() or c in ("-", "_")).rstrip()
+    return f"{safe_name}_{timestamp}{ext}"
+
+
+def get_extraction_filename(pdf_filename: str, file_type: str) -> str:
+    """Generate extraction filename with convention: [pdf_filename]_extract.[extension]"""
+    base_name = os.path.splitext(pdf_filename)[0]
+    extensions = {"txt": "txt", "json": "json", "analysis": "json"}
+    return f"{base_name}_extract.{extensions.get(file_type, 'txt')}"
+
+
+def extract_text_from_pdf(pdf_file) -> Tuple[str, str, str, str]:
+    """
+    Extract text from uploaded PDF file using advanced OCR.
+
+    Returns:
+        - extracted_text: The full extracted text
+        - summary_text: A summary of the extraction process
+        - text_file_path: Path to the text file (for download)
+        - json_file_path: Path to the JSON file (for download)
+    """
+    if pdf_file is None:
+        return "❌ No file uploaded", "Please upload a PDF file", "", ""
+
+    try:
+        start_time = datetime.now()
+
+        # Get the uploaded file path
+        pdf_path = pdf_file.name
+        filename = os.path.basename(pdf_path)
+
+        logger.info(f"📄 Processing uploaded file: {filename}")
+
+        # Generate safe filename
+        safe_filename = get_safe_filename(filename)
+
+        # Copy uploaded file to documents directory
+        documents_path = Path("documents") / safe_filename
+        shutil.copy2(pdf_path, documents_path)
+
+        # Generate output filenames
+        text_filename = get_extraction_filename(safe_filename, "txt")
+        json_filename = get_extraction_filename(safe_filename, "json")
+        analysis_filename = get_extraction_filename(safe_filename, "analysis")
+
+        # Create full paths for extracted files
+        text_path = Path("extracted") / text_filename
+        json_path = Path("extracted") / json_filename
+        analysis_path = Path("extracted") / analysis_filename
+
+        logger.info("🔄 Starting OCR processing...")
+
+        # Check if OCR functionality is available
+        if not OCR_AVAILABLE:
+            return (
+                """❌ **OCR functionality not available**
+
+This appears to be a demo environment where the OCR dependencies are not fully installed.
+
+**Missing components:**
+- OpenCV (cv2) for image processing
+- Tesseract OCR for text recognition
+- Pix2Text for mathematical expression extraction
+
+**To use this system:**
+1. Deploy to Hugging Face Spaces with proper dependencies
+2. Or install missing packages locally:
+   ```bash
+   pip install opencv-python pytesseract pix2text
+   apt-get install tesseract-ocr tesseract-ocr-ben poppler-utils
+   ```
+
+**Demo Features Available:**
+- Interface navigation and design preview
+- File upload testing (files are validated but not processed)
+- System architecture demonstration
+""",
+                "OCR dependencies not available in this environment",
+                "",
+                "",
+            )
+
+        # Process the PDF using our advanced OCR system
+        extract_all_text_advanced_pix2text(
+            pdf_path=str(documents_path),
+            output_text_file=str(text_path),
+            output_json_file=str(json_path),
+            output_analysis_file=str(analysis_path),
+        )
+
+        # Read the extracted text
+        with open(text_path, "r", encoding="utf-8") as f:
+            extracted_text = f.read()
+
+        # Read the analysis for summary
+        with open(analysis_path, "r", encoding="utf-8") as f:
+            analysis_data = json.load(f)
+
+        # Calculate processing time
+        end_time = datetime.now()
+        processing_time = (end_time - start_time).total_seconds()
+
+        # Create summary
+        summary = f"""
+📊 **OCR Processing Complete!**
+
+⏱️ **Processing Time:** {processing_time:.2f} seconds
+📄 **Original File:** {filename}
+📝 **Extracted Characters:** {len(extracted_text):,}
+
+🔤 **Text Distribution:**
+- English regions: {analysis_data.get("type_distribution", {}).get("english", 0)}
+- Bangla regions: {analysis_data.get("type_distribution", {}).get("bangla", 0)}
+- Math regions: {analysis_data.get("type_distribution", {}).get("math", 0)}
+- Mixed regions: {analysis_data.get("type_distribution", {}).get("mixed", 0)}
+
+📈 **Quality Metrics:**
+- Total text regions: {analysis_data.get("total_regions", 0)}
+- Pages processed: {analysis_data.get("total_pages", 0)}
+- Average confidence: {analysis_data.get("confidence_stats", {}).get("avg", 0):.1f}%
+
+🔧 **Extraction Methods:**
+- Pix2Text (Math): {analysis_data.get("extraction_methods", {}).get("pix2text", 0)} regions
+- Tesseract (Text): {analysis_data.get("extraction_methods", {}).get("tesseract", 0)} regions
+
+✅ **Status:** Extraction completed successfully!
+"""
+
+        logger.info(f"✅ OCR processing completed in {processing_time:.2f} seconds")
+
+        return extracted_text, summary, str(text_path), str(json_path)
+
+    except Exception as e:
+        error_message = f"❌ **Error during OCR processing:**\n\n{str(e)}"
+        logger.error(f"OCR processing failed: {e}")
+        return error_message, error_message, "", ""
+
+
+def evaluate_ocr_files(
+    extracted_file, baseline_file, evaluation_name: str = ""
+) -> Tuple[str, str]:
+    """
+    Evaluate OCR accuracy by comparing extracted text with baseline.
+
+    Returns:
+        - results_text: Formatted evaluation results
+        - summary_text: Summary of the evaluation
+    """
+    if extracted_file is None or baseline_file is None:
+        return "❌ Please upload both files for evaluation", "Missing files"
+
+    try:
+        start_time = datetime.now()
+
+        # Read file contents
+        with open(extracted_file.name, "r", encoding="utf-8") as f:
+            extracted_text = f.read()
+
+        with open(baseline_file.name, "r", encoding="utf-8") as f:
+            baseline_text = f.read()
+
+        logger.info(f"📊 Starting evaluation: {evaluation_name or 'Unnamed'}")
+        logger.info(f"Extracted text length: {len(extracted_text)} characters")
+        logger.info(f"Baseline text length: {len(baseline_text)} characters")
+
+        # Check if evaluation functionality is available
+        if not OCR_AVAILABLE:
+            return (
+                """❌ **Evaluation functionality not available**
+
+This appears to be a demo environment where the evaluation dependencies are not fully installed.
+
+**Missing components:**
+- Text processing utilities
+- Evaluation algorithms
+- Statistical analysis functions
+
+**To use this system:**
+1. Deploy to Hugging Face Spaces with proper dependencies
+2. Or install missing packages locally
+
+**Demo Features Available:**
+- Interface navigation and design preview
+- File upload testing (files are validated but not processed)
+- System architecture demonstration
+""",
+                "Evaluation dependencies not available in this environment",
+            )
+
+        # Clean input texts
+        extracted_text_clean = clean_control_characters(extracted_text)
+        baseline_text_clean = clean_control_characters(baseline_text)
+
+        # Perform evaluation
+        evaluation_results = evaluate_ocr_accuracy(
+            extracted_text=extracted_text_clean,
+            baseline_text=baseline_text_clean,
+        )
+
+        # Check for evaluation errors
+        if "error" in evaluation_results:
+            return (
+                f"❌ **Evaluation Error:** {evaluation_results['error']}",
+                "Error occurred",
+            )
+
+        # Calculate processing time
+        end_time = datetime.now()
+        processing_time = (end_time - start_time).total_seconds()
+
+        # Format results
+        results_text = f"""
+📊 **OCR Evaluation Results**
+{f"📝 **Evaluation Name:** {evaluation_name}" if evaluation_name else ""}
+
+🎯 **Overall Performance**
+- **Overall Accuracy:** {evaluation_results["overall_accuracy"]:.2f}%
+- **Similarity Score:** {evaluation_results["similarity_score"]:.2f}%
+- **Grade:** {evaluation_results["evaluation_summary"]["grade"]}
+
+📝 **Character-Level Analysis**
+- **Character Accuracy:** {evaluation_results["character_metrics"]["character_accuracy"]:.2f}%
+- **Character Error Rate:** {evaluation_results["character_metrics"]["character_error_rate"]:.2f}%
+- **Edit Distance:** {evaluation_results["character_metrics"]["edit_distance"]}
+- **Total Characters:** {evaluation_results["character_metrics"]["total_characters"]:,}
+
+📚 **Word-Level Analysis**
+- **Word Accuracy:** {evaluation_results["word_metrics"]["word_accuracy"]:.2f}%
+- **Word Error Rate:** {evaluation_results["word_metrics"]["word_error_rate"]:.2f}%
+- **Correct Words:** {evaluation_results["word_metrics"]["correct_words"]} / {evaluation_results["word_metrics"]["total_words"]}
+- **Missing Words:** {evaluation_results["word_metrics"]["missing_words"]}
+- **Extra Words:** {evaluation_results["word_metrics"]["extra_words"]}
+
+📄 **Line-Level Analysis**
+- **Line Accuracy:** {evaluation_results["line_metrics"]["line_accuracy"]:.2f}%
+- **Average Line Similarity:** {evaluation_results["line_metrics"]["average_line_similarity"]:.2f}%
+- **Lines Matched:** {evaluation_results["line_metrics"]["lines_matched"]} / {evaluation_results["line_metrics"]["total_lines"]}
+
+🌐 **Language-Specific Accuracy**
+- **English:** {evaluation_results["language_specific"].get("english_accuracy", "N/A")}%
+- **Bangla:** {evaluation_results["language_specific"].get("bangla_accuracy", "N/A")}%
+- **Mathematics:** {evaluation_results["language_specific"].get("math_accuracy", "N/A")}%
+- **Numbers:** {evaluation_results["language_specific"].get("number_accuracy", "N/A")}%
+
+📈 **Text Statistics**
+- **Extracted Length:** {evaluation_results["text_statistics"]["extracted_length"]:,} characters
+- **Baseline Length:** {evaluation_results["text_statistics"]["baseline_length"]:,} characters
+- **Extracted Words:** {evaluation_results["text_statistics"]["extracted_words"]:,}
+- **Baseline Words:** {evaluation_results["text_statistics"]["baseline_words"]:,}
+
+💡 **Recommendations**
+"""
+
+        for i, rec in enumerate(
+            evaluation_results["evaluation_summary"]["recommendations"], 1
+        ):
+            results_text += f"{i}. {rec}\n"
+
+        # Create summary
+        summary = f"""
+🎯 **Evaluation Summary**
+
+⏱️ **Processing Time:** {processing_time:.3f} seconds
+📊 **Overall Score:** {evaluation_results["overall_accuracy"]:.2f}%
+🏆 **Grade:** {evaluation_results["evaluation_summary"]["grade"]}
+📝 **Character Accuracy:** {evaluation_results["character_metrics"]["character_accuracy"]:.2f}%
+📚 **Word Accuracy:** {evaluation_results["word_metrics"]["word_accuracy"]:.2f}%
+
+✅ **Evaluation completed successfully!**
+"""
+
+        logger.info(f"✅ Evaluation completed in {processing_time:.3f} seconds")
+        logger.info(
+            f"📊 Overall accuracy: {evaluation_results['overall_accuracy']:.2f}%"
+        )
+
+        return results_text, summary
+
+    except Exception as e:
+        error_message = f"❌ **Error during evaluation:**\n\n{str(e)}"
+        logger.error(f"Evaluation failed: {e}")
+        return error_message, error_message
+
+
+# Create Gradio interface
+def create_gradio_interface():
+    """Create and configure the Gradio interface."""
+
+    # Custom CSS for better styling
+    css = """
+    .gradio-container {
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .output-text {
+        font-family: 'Courier New', monospace;
+        font-size: 14px;
+    }
+    .summary-box {
+        background-color: #f0f8ff;
+        border: 1px solid #d0e7ff;
+        border-radius: 8px;
+        padding: 16px;
+        margin: 8px 0;
+    }
+    """
+
+    with gr.Blocks(
+        css=css, title="Advanced Multi-Language OCR System", theme=gr.themes.Soft()
+    ) as app:
+        # Header
+        gr.Markdown("""
+        # 🔍 Advanced Multi-Language OCR System
+        
+        **Powered by Pix2Text, Tesseract, and FastAPI**
+        
+        Extract text from PDFs containing **English**, **Bangla**, and **Mathematical expressions** with high accuracy.
+        Evaluate OCR performance with comprehensive metrics and detailed analysis.
+        """)
+
+        with gr.Tabs():
+            # Tab 1: OCR Extraction
+            with gr.Tab("📄 PDF Text Extraction"):
+                gr.Markdown("""
+                ### Upload a PDF and extract text using advanced multi-language OCR
+                
+                **Features:**
+                - 🌐 **Multi-language support**: English, Bangla (Bengali), and Mathematical expressions
+                - 🧮 **Advanced Math Recognition**: Pix2Text integration for LaTeX and mathematical formulas
+                - 📊 **Detailed Analysis**: Character-level classification and confidence scores
+                - 💾 **Download Results**: Get extracted text and detailed JSON analysis
+                """)
+
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        pdf_input = gr.File(
+                            label="📄 Upload PDF File",
+                            file_types=[".pdf"],
+                            type="filepath",
+                        )
+                        extract_btn = gr.Button(
+                            "🚀 Extract Text", variant="primary", size="lg"
+                        )
+
+                    with gr.Column(scale=2):
+                        extraction_summary = gr.Textbox(
+                            label="📊 Extraction Summary",
+                            lines=15,
+                            elem_classes=["summary-box"],
+                        )
+
+                with gr.Row():
+                    extracted_text_output = gr.Textbox(
+                        label="📝 Extracted Text",
+                        lines=20,
+                        elem_classes=["output-text"],
+                        show_copy_button=True,
+                    )
+
+                with gr.Row():
+                    text_file_download = gr.File(
+                        label="📥 Download Text File", visible=False
+                    )
+                    json_file_download = gr.File(
+                        label="📥 Download JSON Analysis", visible=False
+                    )
+
+                # Connect extraction functionality
+                extract_btn.click(
+                    fn=extract_text_from_pdf,
+                    inputs=[pdf_input],
+                    outputs=[
+                        extracted_text_output,
+                        extraction_summary,
+                        text_file_download,
+                        json_file_download,
+                    ],
+                ).then(
+                    lambda text_path, json_path: (
+                        gr.update(
+                            visible=bool(text_path),
+                            value=text_path if text_path else None,
+                        ),
+                        gr.update(
+                            visible=bool(json_path),
+                            value=json_path if json_path else None,
+                        ),
+                    ),
+                    inputs=[text_file_download, json_file_download],
+                    outputs=[text_file_download, json_file_download],
+                )
+
+            # Tab 2: OCR Evaluation
+            with gr.Tab("📊 OCR Accuracy Evaluation"):
+                gr.Markdown("""
+                ### Compare OCR extracted text with ground truth baseline for accuracy analysis
+                
+                **Evaluation Features:**
+                - 🎯 **Character-level accuracy**: Precise character matching and edit distance
+                - 📚 **Word-level accuracy**: Word matching and error rates  
+                - 📄 **Line-level accuracy**: Line comparison and similarity scores
+                - 🌐 **Language-specific metrics**: Separate accuracy for English, Bangla, and Math
+                - 🏆 **Grading system**: Letter grades from A+ to F with recommendations
+                """)
+
+                with gr.Row():
+                    with gr.Column():
+                        extracted_file_input = gr.File(
+                            label="📄 OCR Extracted Text File (.txt)",
+                            file_types=[".txt"],
+                            type="filepath",
+                        )
+                        baseline_file_input = gr.File(
+                            label="📑 Ground Truth Baseline File (.txt)",
+                            file_types=[".txt"],
+                            type="filepath",
+                        )
+                        evaluation_name_input = gr.Textbox(
+                            label="📝 Evaluation Name (Optional)",
+                            placeholder="e.g., Math Document Test #1",
+                        )
+                        evaluate_btn = gr.Button(
+                            "📊 Evaluate Accuracy", variant="primary", size="lg"
+                        )
+
+                    with gr.Column():
+                        evaluation_summary = gr.Textbox(
+                            label="🎯 Evaluation Summary",
+                            lines=10,
+                            elem_classes=["summary-box"],
+                        )
+
+                with gr.Row():
+                    evaluation_results = gr.Textbox(
+                        label="📈 Detailed Evaluation Results",
+                        lines=25,
+                        elem_classes=["output-text"],
+                        show_copy_button=True,
+                    )
+
+                # Connect evaluation functionality
+                evaluate_btn.click(
+                    fn=evaluate_ocr_files,
+                    inputs=[
+                        extracted_file_input,
+                        baseline_file_input,
+                        evaluation_name_input,
+                    ],
+                    outputs=[evaluation_results, evaluation_summary],
+                )
+
+            # Tab 3: About & Help
+            with gr.Tab("ℹ️ About & Help"):
+                gr.Markdown("""
+                ## 🔍 Advanced Multi-Language OCR System
+                
+                This application provides state-of-the-art Optical Character Recognition (OCR) for documents containing mixed languages and mathematical expressions.
+                
+                ### 🌟 Key Features
+                
+                #### 📄 **PDF Text Extraction**
+                - **Multi-language Support**: Simultaneously process English and Bangla (Bengali) text
+                - **Mathematical Recognition**: Advanced extraction of mathematical formulas and equations using Pix2Text
+                - **Intelligent Classification**: Automatic detection and classification of text regions by language/content type
+                - **High Accuracy**: Optimized preprocessing and multiple OCR engines for best results
+                - **Detailed Analysis**: Character-by-character analysis with confidence scores and language distribution
+                
+                #### 📊 **OCR Accuracy Evaluation**
+                - **Comprehensive Metrics**: Character, word, and line-level accuracy measurements
+                - **Language-Specific Analysis**: Separate accuracy scores for different languages and mathematical content
+                - **Edit Distance Calculation**: Precise measurement of text differences using Levenshtein distance
+                - **Grading System**: Letter grades (A+ to F) with improvement recommendations
+                - **Detailed Comparison**: Side-by-side diff analysis showing insertions, deletions, and matches
+                
+                ### 🛠️ **Technology Stack**
+                
+                - **Pix2Text**: Advanced mathematical expression recognition
+                - **Tesseract OCR**: Multi-language text recognition with Bengali support
+                - **OpenCV**: Image preprocessing and enhancement
+                - **PDF2Image**: High-quality PDF to image conversion
+                - **FastAPI**: RESTful API backend
+                - **Gradio**: Interactive web interface
+                
+                ### 📝 **Usage Instructions**
+                
+                #### **For PDF Text Extraction:**
+                1. Upload a PDF file using the file picker
+                2. Click "🚀 Extract Text" to start processing
+                3. Review the extraction summary for statistics
+                4. Copy the extracted text or download the files
+                5. Download the JSON file for detailed analysis data
+                
+                #### **For OCR Evaluation:**
+                1. Upload the OCR-extracted text file (what you want to evaluate)
+                2. Upload the ground truth baseline file (the correct text)
+                3. Optionally provide an evaluation name for identification
+                4. Click "📊 Evaluate Accuracy" to run the comparison
+                5. Review the detailed metrics and recommendations
+                
+                ### 🎯 **Accuracy Grading System**
+                
+                - **A+ (95-100%)**: Excellent - Professional-grade accuracy
+                - **A (90-94%)**: Very Good - High-quality results with minor errors
+                - **B (80-89%)**: Good - Acceptable for most applications
+                - **C (70-79%)**: Fair - May require manual review
+                - **D (60-69%)**: Poor - Significant improvements needed
+                - **F (<60%)**: Very Poor - Major issues requiring attention
+                
+                ### 📚 **Supported Languages & Content**
+                
+                - **English**: Full Latin alphabet with punctuation and symbols
+                - **Bangla (Bengali)**: Complete Bengali Unicode range (U+0980-U+09FF)
+                - **Mathematical Expressions**: 
+                  - Basic arithmetic operators (+, -, ×, ÷, =)
+                  - Greek letters (α, β, γ, δ, π, θ, λ, μ, Ω, etc.)
+                  - Mathematical symbols (∑, ∫, √, ∞, ∂, →, ≤, ≥, etc.)
+                  - Subscripts and superscripts
+                  - Functions and equations
+                  - LaTeX-style expressions
+                
+                ### 🔧 **Tips for Best Results**
+                
+                1. **PDF Quality**: Use high-resolution PDFs (300+ DPI) for better accuracy
+                2. **Text Clarity**: Ensure text is not blurry, skewed, or low contrast
+                3. **Language Consistency**: Mixed-language documents work best when languages are clearly separated
+                4. **Mathematical Content**: Complex equations may require manual verification
+                5. **File Size**: Larger documents may take longer to process
+                
+                ### 🐛 **Troubleshooting**
+                
+                - **Empty Results**: Check if the PDF contains selectable text or if images need OCR
+                - **Low Accuracy**: Try preprocessing the PDF to improve image quality
+                - **Mixed Languages**: Ensure the document has clear language boundaries
+                - **Mathematical Errors**: Complex formulas may need manual correction
+                
+                ### 📞 **Support & Feedback**
+                
+                For issues, suggestions, or contributions, please visit our [GitHub repository](https://github.com/ashfaqbracu/aaladinai).
+                
+                ---
+                
+                **Made with ❤️ for advancing multilingual text recognition**
+                """)
+
+        # Footer
+        gr.Markdown("""
+        ---
+        
+        **🔗 Links:** [GitHub Repository](https://github.com/ashfaqbracu/aaladinai) | [Documentation](https://github.com/ashfaqbracu/aaladinai#readme)
+        
+        **⚡ Powered by:** Pix2Text • Tesseract OCR • OpenCV • FastAPI • Gradio
+        """)
+
+    return app
+
+
+# Main execution
+if __name__ == "__main__":
+    logger.info("🚀 Starting Advanced Multi-Language OCR Gradio Interface...")
+
+    # Create and launch the interface
+    app = create_gradio_interface()
+
+    # Launch configuration
+    app.launch(
+        server_name="0.0.0.0",  # Allow external access for Hugging Face Spaces
+        server_port=7860,  # Standard port for Hugging Face Spaces
+        share=False,  # Don't create gradio.live link
+        show_error=True,  # Show detailed error messages
+        show_tips=True,  # Show helpful tips
+        enable_queue=True,  # Enable request queuing for better performance
+        max_threads=4,  # Limit concurrent requests
+    )