Test

README.md

@@ -1,58 +1,12 @@
 ---
-title: Surya OCR Studio
-emoji: 📄
-colorFrom: green
-colorTo: blue
+title: Surya OCR
+emoji: 👀
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 6.11.0
+sdk_version: 4.41.0
 app_file: app.py
 pinned: false
 ---
 
-# Surya OCR Studio
-
-Complete document OCR toolkit supporting 90+ languages with ZeroGPU.
-
-## Features
-
-| Feature | Description |
-|---------|-------------|
-| **OCR** | Text recognition in 90+ languages |
-| **Text Detection** | Line-level text detection |
-| **Layout Analysis** | Identify tables, figures, headers, captions, etc. |
-| **Table Recognition** | Extract table structure to Markdown |
-| **LaTeX OCR** | Convert equation images to LaTeX |
-
-## Usage
-
-### OCR
-1. Upload an image (JPG, PNG, etc.)
-2. Specify language codes (e.g., "en" or "en, pt, es")
-3. Click "Run OCR"
-
-### Table Recognition
-1. Upload an image of a table
-2. Click "Recognize Table"
-3. Get Markdown output
-
-### LaTeX OCR
-1. Upload a cropped equation image
-2. Click "Extract LaTeX"
-3. Copy the LaTeX code
-
-## Supported Languages
-
-English (en), Portuguese (pt), Spanish (es), French (fr), German (de), Italian (it), Dutch (nl), Russian (ru), Chinese (zh), Japanese (ja), Korean (ko), Arabic (ar), Hindi (hi), and 80+ more.
-
-## Model
-
-- [Surya OCR](https://github.com/datalab-to/surya) by datalab-to
-
-## License
-
-- Code: GPL-3.0
-- Model weights: Modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $2M funding/revenue)
-
-## Space by
-
-[@artificialguybr](https://twitter.com/artificialguybr)
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,622 +1,249 @@
-"""
-Surya OCR Studio - Complete Implementation
-Features: OCR, Text Detection, Layout Analysis, Table Recognition, LaTeX OCR
-"""
-
 import gradio as gr
-import spaces
 import logging
 import os
 import json
-from PIL import Image, ImageDraw, ImageFont
-from typing import List, Optional
+from PIL import Image, ImageDraw
 import torch
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-# Performance optimizations for ZeroGPU
-os.environ["RECOGNITION_BATCH_SIZE"] = "64"
-os.environ["DETECTOR_BATCH_SIZE"] = "8"
-os.environ["LAYOUT_BATCH_SIZE"] = "8"
-os.environ["TABLE_REC_BATCH_SIZE"] = "16"
-
-# Surya imports
-from surya.foundation import FoundationPredictor
-from surya.recognition import RecognitionPredictor
-from surya.detection import DetectionPredictor
-from surya.layout import LayoutPredictor
-from surya.table_rec import TableRecPredictor
-from surya.texify import TexifyPredictor
+from surya.ocr import run_ocr
+from surya.detection import batch_text_detection
+from surya.layout import batch_layout_detection
+from surya.ordering import batch_ordering
+from surya.model.detection.model import load_model as load_det_model, load_processor as load_det_processor
+from surya.model.recognition.model import load_model as load_rec_model
+from surya.model.recognition.processor import load_processor as load_rec_processor
 from surya.settings import settings
+from surya.model.ordering.processor import load_processor as load_order_processor
+from surya.model.ordering.model import load_model as load_order_model
 
-logger.info("Loading Surya models...")
-
-# Initialize predictors (lazy loading for faster startup)
-_foundation_predictor = None
-_detection_predictor = None
-_recognition_predictor = None
-_layout_predictor = None
-_table_rec_predictor = None
-_texify_predictor = None
-
-def get_foundation_predictor():
-    global _foundation_predictor
-    if _foundation_predictor is None:
-        _foundation_predictor = FoundationPredictor()
-    return _foundation_predictor
-
-def get_detection_predictor():
-    global _detection_predictor
-    if _detection_predictor is None:
-        _detection_predictor = DetectionPredictor()
-    return _detection_predictor
-
-def get_recognition_predictor():
-    global _recognition_predictor
-    if _recognition_predictor is None:
-        _recognition_predictor = RecognitionPredictor(get_foundation_predictor())
-    return _recognition_predictor
-
-def get_layout_predictor():
-    global _layout_predictor
-    if _layout_predictor is None:
-        _layout_predictor = LayoutPredictor(
-            FoundationPredictor(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)
-        )
-    return _layout_predictor
-
-def get_table_rec_predictor():
-    global _table_rec_predictor
-    if _table_rec_predictor is None:
-        _table_rec_predictor = TableRecPredictor()
-    return _table_rec_predictor
-
-def get_texify_predictor():
-    global _texify_predictor
-    if _texify_predictor is None:
-        _texify_predictor = TexifyPredictor()
-    return _texify_predictor
-
-logger.info("Models will be loaded on first use.")
-
-# Layout labels and colors
-LAYOUT_LABELS = {
-    'Text': '#10B981',        # Green
-    'Title': '#EF4444',       # Red
-    'Section-header': '#F59E0B',  # Amber
-    'Table': '#3B82F6',       # Blue
-    'Figure': '#8B5CF6',      # Purple
-    'Picture': '#8B5CF6',     # Purple
-    'Caption': '#EC4899',     # Pink
-    'Page-header': '#6366F1', # Indigo
-    'Page-footer': '#6366F1', # Indigo
-    'Footnote': '#84CC16',    # Lime
-    'Formula': '#F97316',     # Orange
-    'List-item': '#14B8A6',   # Teal
-    'Form': '#A855F7',        # Fuchsia
-    'Handwriting': '#64748B', # Slate
-    'Table-of-contents': '#0EA5E9',  # Sky
-}
-
-# Supported languages
-LANGUAGES = {
-    "en": "English", "pt": "Portuguese", "es": "Spanish", "fr": "French",
-    "de": "German", "it": "Italian", "nl": "Dutch", "ru": "Russian",
-    "zh": "Chinese", "ja": "Japanese", "ko": "Korean", "ar": "Arabic",
-    "hi": "Hindi", "bn": "Bengali", "tr": "Turkish", "vi": "Vietnamese",
-    "th": "Thai", "id": "Indonesian", "pl": "Polish", "uk": "Ukrainian",
-    "cs": "Czech", "sv": "Swedish", "da": "Danish", "no": "Norwegian",
-    "fi": "Finnish", "el": "Greek", "he": "Hebrew", "hu": "Hungarian",
-    "ro": "Romanian", "sk": "Slovak", "bg": "Bulgarian", "hr": "Croatian",
-    "sl": "Slovenian", "et": "Estonian", "lv": "Latvian", "lt": "Lithuanian",
-    "fa": "Persian", "ur": "Urdu", "ta": "Tamil", "te": "Telugu",
-    "ml": "Malayalam", "kn": "Kannada", "gu": "Gujarati", "mr": "Marathi",
-    "pa": "Punjabi", "ne": "Nepali", "si": "Sinhala", "my": "Burmese",
-    "km": "Khmer", "lo": "Lao", "ka": "Georgian", "hy": "Armenian",
-}
-
-
-def prepare_image(image) -> Image.Image:
-    """Prepare image for processing"""
-    if isinstance(image, str):
-        image = Image.open(image)
-    elif hasattr(image, 'name'):
-        image = Image.open(image.name)
-    
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
-    return image
-
-
-def draw_text_lines(image, text_lines, color=(0, 255, 0)):
-    """Draw text line bounding boxes"""
-    draw = ImageDraw.Draw(image)
-    for line in text_lines:
-        if hasattr(line, 'bbox'):
-            bbox = line.bbox
-            if len(bbox) == 4:
-                draw.rectangle(bbox, outline=color, width=2)
-    return image
-
-
-def draw_layout_boxes(image, bboxes):
-    """Draw layout boxes with labels and colors"""
-    draw = ImageDraw.Draw(image)
-    
-    for bbox in bboxes:
-        label = getattr(bbox, 'label', 'Text')
-        color = LAYOUT_LABELS.get(label, '#FFFFFF')
-        
-        # Convert hex to RGB
-        rgb = tuple(int(color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4))
-        
-        box = bbox.bbox if hasattr(bbox, 'bbox') else bbox
-        if len(box) == 4:
-            draw.rectangle(box, outline=rgb, width=2)
-            
-            # Draw label
-            label_text = label.replace('-', ' ').title()
-            draw.text((box[0], box[1] - 12), label_text, fill=rgb)
-    
-    return image
+# Configuração do TorchDynamo
+torch._dynamo.config.capture_scalar_outputs = True
 
+# Configuração de logging
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
 
-def draw_table_cells(image, predictions):
-    """Draw table cells with row/column info"""
+# Configuração de variáveis de ambiente
+logger.info("Configurando variáveis de ambiente para otimização de performance")
+os.environ["RECOGNITION_BATCH_SIZE"] = "512"
+os.environ["DETECTOR_BATCH_SIZE"] = "36"
+os.environ["ORDER_BATCH_SIZE"] = "32"
+os.environ["RECOGNITION_STATIC_CACHE"] = "true"
+
+# Carregamento de modelos
+logger.info("Iniciando carregamento dos modelos...")
+
+try:
+    logger.debug("Carregando modelo e processador de detecção...")
+    det_processor, det_model = load_det_processor(), load_det_model()
+    logger.debug("Modelo e processador de detecção carregados com sucesso")
+except Exception as e:
+    logger.error(f"Erro ao carregar modelo de detecção: {e}")
+    raise
+
+try:
+    logger.debug("Carregando modelo e processador de reconhecimento...")
+    rec_model, rec_processor = load_rec_model(), load_rec_processor()
+    logger.debug("Modelo e processador de reconhecimento carregados com sucesso")
+except Exception as e:
+    logger.error(f"Erro ao carregar modelo de reconhecimento: {e}")
+    raise
+
+try:
+    logger.debug("Carregando modelo e processador de layout...")
+    layout_model = load_det_model(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)
+    layout_processor = load_det_processor(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)
+    logger.debug("Modelo e processador de layout carregados com sucesso")
+except Exception as e:
+    logger.error(f"Erro ao carregar modelo de layout: {e}")
+    raise
+
+try:
+    logger.debug("Carregando modelo e processador de ordenação...")
+    order_model = load_order_model()
+    order_processor = load_order_processor()
+    logger.debug("Modelo e processador de ordenação carregados com sucesso")
+except Exception as e:
+    logger.error(f"Erro ao carregar modelo de ordenação: {e}")
+    raise
+
+logger.info("Todos os modelos foram carregados com sucesso")
+
+# Compilação do modelo de reconhecimento
+logger.info("Iniciando compilação do modelo de reconhecimento...")
+try:
+    rec_model.decoder.model = torch.compile(rec_model.decoder.model)
+    logger.info("Compilação do modelo de reconhecimento concluída com sucesso")
+except Exception as e:
+    logger.error(f"Erro durante a compilação do modelo de reconhecimento: {e}")
+    logger.warning("Continuando sem compilação do modelo")
+
+class CustomJSONEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, Image.Image):
+            return "Image object (not serializable)"
+        if hasattr(obj, '__dict__'):
+            return {k: self.default(v) for k, v in obj.__dict__.items()}
+        return str(obj)
+
+def serialize_result(result):
+    return json.dumps(result, cls=CustomJSONEncoder, indent=2)
+
+def draw_boxes(image, predictions, color=(255, 0, 0)):
     draw = ImageDraw.Draw(image)
-    
-    # Draw rows in blue
-    for row in predictions.rows:
-        draw.rectangle(row.bbox, outline=(0, 0, 255), width=2)
-    
-    # Draw columns in green
-    for col in predictions.cols:
-        draw.rectangle(col.bbox, outline=(0, 255, 0), width=2)
-    
-    # Draw cells in red
-    for cell in predictions.cells:
-        draw.rectangle(cell.bbox, outline=(255, 0, 0), width=1)
-        # Draw cell text if available
-        if hasattr(cell, 'text') and cell.text:
-            draw.text((cell.bbox[0], cell.bbox[1]), cell.text[:10], fill=(100, 100, 100))
-    
+    if isinstance(predictions, list):
+        for pred in predictions:
+            if hasattr(pred, 'bboxes'):
+                for bbox in pred.bboxes:
+                    draw.rectangle(bbox, outline=color, width=2)
+            elif hasattr(pred, 'bbox'):
+                draw.rectangle(pred.bbox, outline=color, width=2)
+            elif hasattr(pred, 'polygon'):
+                draw.polygon(pred.polygon, outline=color, width=2)
+    elif hasattr(predictions, 'bboxes'):
+        for bbox in predictions.bboxes:
+            draw.rectangle(bbox, outline=color, width=2)
     return image
 
-
-@spaces.GPU(duration=120)
-def process_ocr(image, languages: str, disable_math: bool = False):
-    """Run OCR on image"""
-    logger.info(f"Running OCR with languages: {languages}")
+def ocr_workflow(image, langs):
+    logger.info(f"Iniciando workflow OCR com idiomas: {langs}")
     try:
-        image = prepare_image(image)
-        
-        # Parse languages
-        langs = [l.strip() for l in languages.split(',') if l.strip()]
-        if not langs:
-            langs = ['en']
-        
-        # Get predictors
-        det_pred = get_detection_predictor()
-        rec_pred = get_recognition_predictor()
-        
-        # Run OCR
-        predictions = rec_pred(
-            [image], 
-            det_predictor=det_pred, 
-            langs=[langs],
-            disable_math=disable_math
-        )
-        
-        if not predictions or len(predictions) == 0:
-            return "", {}, None
-        
-        pred = predictions[0]
-        
-        # Extract text
-        text_lines = pred.text_lines if hasattr(pred, 'text_lines') else []
-        full_text = "\n".join([line.text for line in text_lines if hasattr(line, 'text')])
-        
-        # Build JSON result
-        result = {
-            "text": full_text,
-            "languages": langs,
-            "num_lines": len(text_lines),
-            "lines": [
-                {
-                    "text": line.text,
-                    "confidence": round(line.confidence, 3) if hasattr(line, 'confidence') else 1.0,
-                    "bbox": list(line.bbox) if hasattr(line, 'bbox') else []
-                }
-                for line in text_lines
-            ]
-        }
-        
-        # Draw bounding boxes
-        img_with_boxes = draw_text_lines(image.copy(), text_lines)
-        
-        return full_text, result, img_with_boxes
-        
-    except Exception as e:
-        logger.error(f"OCR Error: {e}", exc_info=True)
-        return f"Error: {str(e)}", {"error": str(e)}, None
-
-
-@spaces.GPU(duration=60)
-def process_detection(image):
-    """Run text detection on image"""
-    logger.info("Running text detection")
-    try:
-        image = prepare_image(image)
-        
-        det_pred = get_detection_predictor()
-        predictions = det_pred([image])
-        
-        if not predictions or len(predictions) == 0:
-            return {}, None
-        
-        pred = predictions[0]
-        
-        # Build result
-        result = {
-            "num_lines": len(pred.bboxes) if hasattr(pred, 'bboxes') else 0,
-            "image_size": list(image.size),
-            "bboxes": [
-                {
-                    "bbox": list(bbox.bbox) if hasattr(bbox, 'bbox') else list(bbox),
-                    "confidence": round(bbox.confidence, 3) if hasattr(bbox, 'confidence') else 1.0
-                }
-                for bbox in (pred.bboxes if hasattr(pred, 'bboxes') else [])
-            ]
-        }
+        image = Image.open(image.name)
+        logger.debug(f"Imagem carregada: {image.size}")
+        predictions = run_ocr([image], [langs.split(',')], det_model, det_processor, rec_model, rec_processor)
         
-        # Draw boxes
-        img_with_boxes = image.copy()
-        draw = ImageDraw.Draw(img_with_boxes)
-        for bbox in (pred.bboxes if hasattr(pred, 'bboxes') else []):
-            box = bbox.bbox if hasattr(bbox, 'bbox') else bbox
-            draw.rectangle(box, outline=(0, 255, 0), width=2)
+        # Draw bounding boxes on the image
+        image_with_boxes = draw_boxes(image.copy(), predictions[0].text_lines)
         
-        return result, img_with_boxes
+        # Format the OCR results
+        formatted_text = "\n".join([line.text for line in predictions[0].text_lines])
         
+        logger.info("Workflow OCR concluído com sucesso")
+        return serialize_result(predictions), image_with_boxes, formatted_text
     except Exception as e:
-        logger.error(f"Detection Error: {e}", exc_info=True)
-        return {"error": str(e)}, None
+        logger.error(f"Erro durante o workflow OCR: {e}")
+        return serialize_result({"error": str(e)}), None, ""
 
-
-@spaces.GPU(duration=60)
-def process_layout(image):
-    """Run layout analysis on image"""
-    logger.info("Running layout analysis")
+def text_detection_workflow(image):
+    logger.info("Iniciando workflow de detecção de texto")
     try:
-        image = prepare_image(image)
-        
-        layout_pred = get_layout_predictor()
-        predictions = layout_pred([image])
-        
-        if not predictions or len(predictions) == 0:
-            return {}, None
-        
-        pred = predictions[0]
-        
-        # Count by label
-        label_counts = {}
-        for bbox in (pred.bboxes if hasattr(pred, 'bboxes') else []):
-            label = getattr(bbox, 'label', 'Unknown')
-            label_counts[label] = label_counts.get(label, 0) + 1
-        
-        # Build result
-        result = {
-            "num_elements": len(pred.bboxes) if hasattr(pred, 'bboxes') else 0,
-            "label_counts": label_counts,
-            "elements": [
-                {
-                    "label": getattr(bbox, 'label', 'Unknown'),
-                    "confidence": round(getattr(bbox, 'confidence', 1.0), 3),
-                    "position": getattr(bbox, 'position', -1),
-                    "bbox": list(bbox.bbox) if hasattr(bbox, 'bbox') else []
-                }
-                for bbox in (pred.bboxes if hasattr(pred, 'bboxes') else [])
-            ]
-        }
-        
-        # Draw layout boxes
-        img_with_boxes = draw_layout_boxes(image.copy(), pred.bboxes if hasattr(pred, 'bboxes') else [])
-        
-        return result, img_with_boxes
-        
+        image = Image.open(image.name)
+        logger.debug(f"Imagem carregada: {image.size}")
+        predictions = batch_text_detection([image], det_model, det_processor)
+        
+        # Draw bounding boxes on the image
+        image_with_boxes = draw_boxes(image.copy(), predictions)
+        
+        # Convert predictions to a serializable format
+        serializable_predictions = []
+        for pred in predictions:
+            serializable_pred = {
+                'bboxes': [bbox.tolist() if hasattr(bbox, 'tolist') else bbox for bbox in pred.bboxes],
+                'polygons': [poly.tolist() if hasattr(poly, 'tolist') else poly for poly in pred.polygons],
+                'confidences': pred.confidences,
+                'vertical_lines': [line.tolist() if hasattr(line, 'tolist') else line for line in pred.vertical_lines],
+                'image_bbox': pred.image_bbox.tolist() if hasattr(pred.image_bbox, 'tolist') else pred.image_bbox
+            }
+            serializable_predictions.append(serializable_pred)
+        
+        logger.info("Workflow de detecção de texto concluído com sucesso")
+        return serialize_result(serializable_predictions), image_with_boxes
     except Exception as e:
-        logger.error(f"Layout Error: {e}", exc_info=True)
-        return {"error": str(e)}, None
-
+        logger.error(f"Erro durante o workflow de detecção de texto: {e}")
+        return serialize_result({"error": str(e)}), None
 
-@spaces.GPU(duration=90)
-def process_table(image):
-    """Run table recognition on image"""
-    logger.info("Running table recognition")
+def layout_analysis_workflow(image):
+    logger.info("Iniciando workflow de análise de layout")
     try:
-        image = prepare_image(image)
-        
-        table_pred = get_table_rec_predictor()
-        predictions = table_pred([image])
-        
-        if not predictions or len(predictions) == 0:
-            return {}, None, ""
-        
-        pred = predictions[0]
-        
-        # Build markdown table
-        md_table = ""
-        if hasattr(pred, 'cells') and pred.cells:
-            # Find max row and col
-            max_row = max(c.row_id for c in pred.cells) if pred.cells else 0
-            max_col = max(c.col_id for c in pred.cells) if pred.cells else 0
-            
-            # Create table data
-            table_data = [["" for _ in range(max_col + 1)] for _ in range(max_row + 1)]
-            
-            for cell in pred.cells:
-                text = getattr(cell, 'text', '')
-                table_data[cell.row_id][cell.col_id] = text
-            
-            # Build markdown
-            md_lines = []
-            for i, row in enumerate(table_data):
-                md_lines.append("| " + " | ".join(row) + " |")
-                if i == 0:
-                    md_lines.append("| " + " | ".join(["---"] * len(row)) + " |")
-            
-            md_table = "\n".join(md_lines)
-        
-        # Build result
-        result = {
-            "num_rows": len(pred.rows) if hasattr(pred, 'rows') else 0,
-            "num_cols": len(pred.cols) if hasattr(pred, 'cols') else 0,
-            "num_cells": len(pred.cells) if hasattr(pred, 'cells') else 0,
-            "rows": [
-                {"row_id": r.row_id, "is_header": getattr(r, 'is_header', False)}
-                for r in (pred.rows if hasattr(pred, 'rows') else [])
-            ],
-            "cols": [
-                {"col_id": c.col_id, "is_header": getattr(c, 'is_header', False)}
-                for c in (pred.cols if hasattr(pred, 'cols') else [])
-            ]
-        }
-        
-        # Draw table cells
-        img_with_boxes = draw_table_cells(image.copy(), pred)
-        
-        return result, img_with_boxes, md_table
-        
+        image = Image.open(image.name)
+        logger.debug(f"Imagem carregada: {image.size}")
+        line_predictions = batch_text_detection([image], det_model, det_processor)
+        logger.debug(f"Detecção de linhas concluída. Número de linhas detectadas: {len(line_predictions[0].bboxes)}")
+        layout_predictions = batch_layout_detection([image], layout_model, layout_processor, line_predictions)
+        
+        # Draw bounding boxes on the image
+        image_with_boxes = draw_boxes(image.copy(), layout_predictions[0], color=(0, 255, 0))
+        
+        # Convert predictions to a serializable format
+        serializable_predictions = []
+        for pred in layout_predictions:
+            serializable_pred = {
+                'bboxes': [
+                    {
+                        'bbox': bbox.bbox.tolist() if hasattr(bbox.bbox, 'tolist') else bbox.bbox,
+                        'polygon': bbox.polygon.tolist() if hasattr(bbox.polygon, 'tolist') else bbox.polygon,
+                        'confidence': bbox.confidence,
+                        'label': bbox.label
+                    } for bbox in pred.bboxes
+                ],
+                'image_bbox': pred.image_bbox.tolist() if hasattr(pred.image_bbox, 'tolist') else pred.image_bbox
+            }
+            serializable_predictions.append(serializable_pred)
+        
+        logger.info("Workflow de análise de layout concluído com sucesso")
+        return serialize_result(serializable_predictions), image_with_boxes
     except Exception as e:
-        logger.error(f"Table Recognition Error: {e}", exc_info=True)
-        return {"error": str(e)}, None, ""
-
+        logger.error(f"Erro durante o workflow de análise de layout: {e}")
+        return serialize_result({"error": str(e)}), None
 
-@spaces.GPU(duration=60)
-def process_latex(image):
-    """Run LaTeX OCR on image (equation)"""
-    logger.info("Running LaTeX OCR")
+def reading_order_workflow(image):
+    logger.info("Iniciando workflow de ordem de leitura")
     try:
-        image = prepare_image(image)
-        
-        texify_pred = get_texify_predictor()
-        predictions = texify_pred([image])
-        
-        if not predictions or len(predictions) == 0:
-            return "", {}
-        
-        pred = predictions[0]
-        latex = pred.text if hasattr(pred, 'text') else str(pred)
-        
-        result = {
-            "latex": latex,
-            "markdown": f"$$\n{latex}\n$$"
-        }
-        
-        return latex, result
-        
+        image = Image.open(image.name)
+        logger.debug(f"Imagem carregada: {image.size}")
+        line_predictions = batch_text_detection([image], det_model, det_processor)
+        logger.debug(f"Detecção de linhas concluída. Número de linhas detectadas: {len(line_predictions[0].bboxes)}")
+        layout_predictions = batch_layout_detection([image], layout_model, layout_processor, line_predictions)
+        logger.debug(f"Análise de layout concluída. Número de elementos de layout: {len(layout_predictions[0].bboxes)}")
+        bboxes = [pred.bbox for pred in layout_predictions[0].bboxes]
+        order_predictions = batch_ordering([image], [bboxes], order_model, order_processor)
+        
+        # Draw bounding boxes on the image
+        image_with_boxes = image.copy()
+        draw = ImageDraw.Draw(image_with_boxes)
+        for i, bbox in enumerate(order_predictions[0].bboxes):
+            draw.rectangle(bbox.bbox, outline=(0, 0, 255), width=2)
+            draw.text((bbox.bbox[0], bbox.bbox[1]), str(bbox.position), fill=(255, 0, 0))
+        
+        logger.info("Workflow de ordem de leitura concluído com sucesso")
+        return serialize_result(order_predictions), image_with_boxes
     except Exception as e:
-        logger.error(f"LaTeX OCR Error: {e}", exc_info=True)
-        return f"Error: {str(e)}", {"error": str(e)}
-
-
-# ============== GRADIO UI ==============
-
-CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;500;600;700;800&family=Fira+Code:wght@400;500&display=swap');
-
-:root {
-    --bg: #0a0f1a;
-    --surf: #0f1629;
-    --card: #151d32;
-    --border: #1e2a45;
-    --border2: #2a3a5a;
-    --green: #10b981;
-    --blue: #3b82f6;
-    --text: #e2e8f0;
-    --muted: #64748b;
-}
-
-body, .gradio-container {
-    background: var(--bg) !important;
-    font-family: 'Outfit', sans-serif !important;
-    color: var(--text) !important;
-}
-
-.gradio-container::before {
-    content: '';
-    position: fixed; inset: 0; pointer-events: none; z-index: 0;
-    background: radial-gradient(ellipse 70% 50% at 50% -10%, rgba(16,185,129,0.08) 0%, transparent 65%);
-}
-
-.app-hero { padding: 40px 0 20px; text-align: center; }
-.app-hero h1 {
-    font-size: 2.8rem; font-weight: 800; letter-spacing: -0.04em;
-    background: linear-gradient(135deg, #10b981, #06b6d4, #3b82f6);
-    -webkit-background-clip: text; -webkit-text-fill-color: transparent;
-    margin-bottom: 8px;
-}
-.app-hero .tagline { color: var(--muted); font-size: 1rem; }
-.pills { display: flex; justify-content: center; gap: 8px; margin-top: 16px; flex-wrap: wrap; }
-.pill {
-    background: var(--card); border: 1px solid var(--border2); border-radius: 100px;
-    padding: 5px 14px; font-size: 0.75rem; color: var(--muted); font-family: 'Fira Code', monospace;
-}
-.pill.green { color: var(--green); border-color: rgba(16,185,129,0.3); }
-
-.tabs button { font-family: 'Outfit', sans-serif !important; font-weight: 500 !important; }
-
-button.primary-btn { 
-    background: linear-gradient(135deg, #10b981, #06b6d4) !important; 
-    border: none !important; color: #000 !important; font-weight: 600 !important;
-    padding: 12px 24px !important;
-}
-
-.output-image img { border-radius: 8px; }
-
-footer { display: none !important; }
-"""
+        logger.error(f"Erro durante o workflow de ordem de leitura: {e}")
+        return serialize_result({"error": str(e)}), None
 
-# Language string for the dropdown
-LANGUAGE_OPTIONS = [f"{code} - {name}" for code, name in sorted(LANGUAGES.items(), key=lambda x: x[1])]
-
-with gr.Blocks(theme=gr.themes.Base(), css=CSS, title="Surya OCR Studio") as app:
-    
-    gr.HTML("""
-    <div class="app-hero">
-        <h1>📄 Surya OCR Studio</h1>
-        <p class="tagline">Document OCR · Layout Analysis · Table Recognition · 90+ Languages</p>
-        <div class="pills">
-            <span class="pill green">ZeroGPU ⚡</span>
-            <span class="pill">90+ Languages</span>
-            <span class="pill">Layout Detection</span>
-            <span class="pill">Table Recognition</span>
-            <span class="pill">LaTeX OCR</span>
-        </div>
-    </div>
-    """)
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Análise de Documentos com Surya")
     
-    with gr.Tabs() as tabs:
-        
-        # ============ OCR TAB ============
-        with gr.TabItem("📝 OCR"):
-            gr.Markdown("### Optical Character Recognition\nExtract text from images in 90+ languages.")
-            
-            with gr.Row():
-                with gr.Column(scale=2):
-                    ocr_input = gr.Image(label="📄 Upload Image", type="pil", height=400)
-                    
-                    with gr.Row():
-                        ocr_langs = gr.Textbox(
-                            label="Languages (comma-separated codes)",
-                            value="en",
-                            placeholder="en, pt, es, de, fr...",
-                            info="Use language codes: en=English, pt=Portuguese, es=Spanish..."
-                        )
-                        ocr_disable_math = gr.Checkbox(label="Disable math detection", value=False)
-                    
-                    ocr_btn = gr.Button("🚀 Run OCR", variant="primary", elem_classes=["primary-btn"])
-                
-                with gr.Column(scale=3):
-                    ocr_text = gr.Textbox(label="📝 Extracted Text", lines=12, show_copy_button=True)
-                    ocr_json = gr.JSON(label="📊 Detailed Results")
-            
-            ocr_image = gr.Image(label="🖼️ Detected Text Lines", elem_classes=["output-image"])
-            
-            ocr_btn.click(
-                process_ocr,
-                [ocr_input, ocr_langs, ocr_disable_math],
-                [ocr_text, ocr_json, ocr_image]
-            )
-        
-        # ============ TEXT DETECTION TAB ============
-        with gr.TabItem("🔍 Text Detection"):
-            gr.Markdown("### Text Line Detection\nDetect text lines in documents without OCR.")
-            
-            with gr.Row():
-                det_input = gr.Image(label="📄 Upload Image", type="pil", height=400)
-                with gr.Column():
-                    det_json = gr.JSON(label="📊 Detection Results")
-                    det_btn = gr.Button("🔍 Detect Text Lines", variant="primary", elem_classes=["primary-btn"])
-            
-            det_image = gr.Image(label="🖼️ Detected Lines", elem_classes=["output-image"])
-            
-            det_btn.click(process_detection, [det_input], [det_json, det_image])
-        
-        # ============ LAYOUT ANALYSIS TAB ============
-        with gr.TabItem("📊 Layout Analysis"):
-            gr.Markdown("### Document Layout Analysis\nIdentify document structure: titles, tables, figures, etc.")
-            
-            with gr.Row():
-                layout_input = gr.Image(label="📄 Upload Image", type="pil", height=400)
-                with gr.Column():
-                    layout_json = gr.JSON(label="📊 Layout Results")
-                    layout_btn = gr.Button("📊 Analyze Layout", variant="primary", elem_classes=["primary-btn"])
-            
-            layout_image = gr.Image(label="🖼️ Layout Elements", elem_classes=["output-image"])
-            
-            # Legend
-            gr.Markdown("""
-            **Legend:** 
-            🟢 Text | 🔴 Title | 🟡 Section Header | 🔵 Table | 🟣 Figure/Picture | 🩷 Caption | 🔷 Header/Footer
-            """)
-            
-            layout_btn.click(process_layout, [layout_input], [layout_json, layout_image])
-        
-        # ============ TABLE RECOGNITION TAB ============
-        with gr.TabItem("📋 Table Recognition"):
-            gr.Markdown("### Table Recognition\nExtract table structure and convert to Markdown.")
-            
-            with gr.Row():
-                table_input = gr.Image(label="📄 Upload Table Image", type="pil", height=400)
-                with gr.Column():
-                    table_json = gr.JSON(label="📊 Table Structure")
-                    table_btn = gr.Button("📋 Recognize Table", variant="primary", elem_classes=["primary-btn"])
-            
-            table_image = gr.Image(label="🖼️ Table Cells", elem_classes=["output-image"])
-            table_md = gr.Textbox(label="📝 Markdown Output", lines=10, show_copy_button=True)
-            
-            table_btn.click(process_table, [table_input], [table_json, table_image, table_md])
-        
-        # ============ LATEX OCR TAB ============
-        with gr.TabItem("🔢 LaTeX OCR"):
-            gr.Markdown("### LaTeX Equation OCR\nConvert equation images to LaTeX code.\n\n**Tip:** Crop the image to just the equation for best results.")
-            
-            with gr.Row():
-                latex_input = gr.Image(label="📄 Upload Equation Image", type="pil", height=300)
-                with gr.Column():
-                    latex_code = gr.Textbox(label="🔢 LaTeX Code", lines=5, show_copy_button=True)
-                    latex_json = gr.JSON(label="📊 Results")
-                    latex_btn = gr.Button("🔢 Extract LaTeX", variant="primary", elem_classes=["primary-btn"])
-            
-            latex_btn.click(process_latex, [latex_input], [latex_code, latex_json])
-    
-    # ============ FOOTER ============
-    gr.Markdown("""
-    ---
-    
-    ### ℹ️ About Surya OCR
-    
-    | Feature | Description |
-    |---------|-------------|
-    | **OCR** | Text recognition in 90+ languages |
-    | **Detection** | Line-level text detection |
-    | **Layout** | Identify tables, figures, headers, etc. |
-    | **Tables** | Extract table structure to Markdown |
-    | **LaTeX** | Convert equations to LaTeX |
-    
-    **Performance Tips:**
-    - Use higher resolution images for better accuracy
-    - For blurry text, try preprocessing (binarization, deskewing)
-    - Specify correct language codes for best OCR results
-    
-    **Model:** [Surya OCR](https://github.com/datalab-to/surya) by datalab-to  
-    **Space by:** [@artificialguybr](https://twitter.com/artificialguybr)
-    """)
-
+    with gr.Tab("OCR"):
+        gr.Markdown("## Reconhecimento Óptico de Caracteres")
+        with gr.Row():
+            ocr_input = gr.File(label="Carregar Imagem ou PDF")
+            ocr_langs = gr.Textbox(label="Idiomas (separados por vírgula)", value="en")
+        ocr_button = gr.Button("Executar OCR")
+        ocr_output = gr.JSON(label="Resultados OCR")
+        ocr_image = gr.Image(label="Imagem com Bounding Boxes")
+        ocr_text = gr.Textbox(label="Texto Extraído", lines=10)
+        ocr_button.click(ocr_workflow, inputs=[ocr_input, ocr_langs], outputs=[ocr_output, ocr_image, ocr_text])
+
+    with gr.Tab("Detecção de Texto"):
+        gr.Markdown("## Detecção de Linhas de Texto")
+        det_input = gr.File(label="Carregar Imagem ou PDF")
+        det_button = gr.Button("Executar Detecção de Texto")
+        det_output = gr.JSON(label="Resultados da Detecção de Texto")
+        det_image = gr.Image(label="Imagem com Bounding Boxes")
+        det_button.click(text_detection_workflow, inputs=det_input, outputs=[det_output, det_image])
+
+    with gr.Tab("Análise de Layout"):
+        gr.Markdown("## Análise de Layout e Ordem de Leitura")
+        layout_input = gr.File(label="Carregar Imagem ou PDF")
+        layout_button = gr.Button("Executar Análise de Layout")
+        order_button = gr.Button("Determinar Ordem de Leitura")
+        layout_output = gr.JSON(label="Resultados da Análise de Layout")
+        layout_image = gr.Image(label="Imagem com Layout")
+        order_output = gr.JSON(label="Resultados da Ordem de Leitura")
+        order_image = gr.Image(label="Imagem com Ordem de Leitura")
+        layout_button.click(layout_analysis_workflow, inputs=layout_input, outputs=[layout_output, layout_image])
+        order_button.click(reading_order_workflow, inputs=layout_input, outputs=[order_output, order_image])
 
 if __name__ == "__main__":
-    app.queue(max_size=20)
-    app.launch()
+    logger.info("Iniciando aplicativo Gradio...")
+    demo.launch()

languages.json

@@ -0,0 +1,95 @@
+{
+  "Afrikaans": "af",
+  "Amharic": "am",
+  "Arabic": "ar",
+  "Assamese": "as",
+  "Azerbaijani": "az",
+  "Belarusian": "be",
+  "Bulgarian": "bg",
+  "Bengali": "bn",
+  "Breton": "br",
+  "Bosnian": "bs",
+  "Catalan": "ca",
+  "Czech": "cs",
+  "Welsh": "cy",
+  "Danish": "da",
+  "German": "de",
+  "Greek": "el",
+  "English": "en",
+  "Esperanto": "eo",
+  "Spanish": "es",
+  "Estonian": "et",
+  "Basque": "eu",
+  "Persian": "fa",
+  "Finnish": "fi",
+  "French": "fr",
+  "Western Frisian": "fy",
+  "Irish": "ga",
+  "Scottish Gaelic": "gd",
+  "Galician": "gl",
+  "Gujarati": "gu",
+  "Hausa": "ha",
+  "Hebrew": "he",
+  "Hindi": "hi",
+  "Croatian": "hr",
+  "Hungarian": "hu",
+  "Armenian": "hy",
+  "Indonesian": "id",
+  "Icelandic": "is",
+  "Italian": "it",
+  "Japanese": "ja",
+  "Javanese": "jv",
+  "Georgian": "ka",
+  "Kazakh": "kk",
+  "Khmer": "km",
+  "Kannada": "kn",
+  "Korean": "ko",
+  "Kurdish": "ku",
+  "Kyrgyz": "ky",
+  "Latin": "la",
+  "Lao": "lo",
+  "Lithuanian": "lt",
+  "Latvian": "lv",
+  "Malagasy": "mg",
+  "Macedonian": "mk",
+  "Malayalam": "ml",
+  "Mongolian": "mn",
+  "Marathi": "mr",
+  "Malay": "ms",
+  "Burmese": "my",
+  "Nepali": "ne",
+  "Dutch": "nl",
+  "Norwegian": "no",
+  "Oromo": "om",
+  "Oriya": "or",
+  "Punjabi": "pa",
+  "Polish": "pl",
+  "Pashto": "ps",
+  "Portuguese": "pt",
+  "Romanian": "ro",
+  "Russian": "ru",
+  "Sanskrit": "sa",
+  "Sindhi": "sd",
+  "Sinhala": "si",
+  "Slovak": "sk",
+  "Slovenian": "sl",
+  "Somali": "so",
+  "Albanian": "sq",
+  "Serbian": "sr",
+  "Sundanese": "su",
+  "Swedish": "sv",
+  "Swahili": "sw",
+  "Tamil": "ta",
+  "Telugu": "te",
+  "Thai": "th",
+  "Tagalog": "tl",
+  "Turkish": "tr",
+  "Uyghur": "ug",
+  "Ukrainian": "uk",
+  "Urdu": "ur",
+  "Uzbek": "uz",
+  "Vietnamese": "vi",
+  "Xhosa": "xh",
+  "Yiddish": "yi",
+  "Chinese": "zh"
+}

requirements.txt

@@ -1,5 +1,3 @@
+torch
 surya-ocr
-transformers>=4.56.1
-torch>=2.7.0
-pillow
-spaces
+pillow