Update app.py

app.py

@@ -1,3 +1,7 @@
+# app.py
+# All code combined into a single file for convenience.
+
+# --- Imports ---
 import spaces
 import json
 import math
@@ -6,458 +10,714 @@ import traceback
 from io import BytesIO
 from typing import Any, Dict, List, Optional, Tuple
 import re
+import base64
+import copy
+from dataclasses import dataclass
 
+# Vision and ML Libraries
 import fitz  # PyMuPDF
 import gradio as gr
 import requests
 import torch
 from huggingface_hub import snapshot_download
 from PIL import Image, ImageDraw, ImageFont
+from transformers import AutoModelForCausalLM, AutoProcessor, VisionEncoderDecoderModel
 from qwen_vl_utils import process_vision_info
-from transformers import AutoModelForCausalLM, AutoProcessor, Qwen2_5_VLForConditionalGeneration
 
-# Constants
+# Image Processing Libraries
+import cv2
+import numpy as np
+import albumentations as alb
+from albumentations.pytorch import ToTensorV2
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+
+# --- Constants & Global State ---
 MIN_PIXELS = 3136
 MAX_PIXELS = 11289600
 IMAGE_FACTOR = 28
-
-# Prompts
-prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
-
+DOT_OCR_PROMPT = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
 1. Bbox format: [x1, y1, x2, y2]
-
 2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
-
 3. Text Extraction & Formatting Rules:
     - Picture: For the 'Picture' category, the text field should be omitted.
     - Formula: Format its text as LaTeX.
     - Table: Format its text as HTML.
     - All Others (Text, Title, etc.): Format their text as Markdown.
-
 4. Constraints:
     - The output text must be the original text from the image, with no translation.
     - All layout elements must be sorted according to human reading order.
-
 5. Final Output: The entire output must be a single JSON object.
 """
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+PDF_CACHE = {
+    "images": [],
+    "current_page": 0,
+    "total_pages": 0,
+    "file_type": None,
+    "is_parsed": False,
+    "results": [],
+    "model_used": None,
+}
+MODELS = {}
 
-# Utility Functions
-def round_by_factor(number: int, factor: int) -> int:
-    """Returns the closest integer to 'number' that is divisible by 'factor'."""
-    return round(number / factor) * factor
-
-def smart_resize(
-    height: int,
-    width: int,
-    factor: int = 28,
-    min_pixels: int = 3136,
-    max_pixels: int = 11289600,
-):
-    """Rescales the image so that dimensions are divisible by 'factor', within pixel range, maintaining aspect ratio."""
-    if max(height, width) / min(height, width) > 200:
-        raise ValueError(f"Aspect ratio must be < 200, got {max(height, width) / min(height, width)}")
-    h_bar = max(factor, round_by_factor(height, factor))
-    w_bar = max(factor, round_by_factor(width, factor))
-
-    if h_bar * w_bar > max_pixels:
-        beta = math.sqrt((height * width) / max_pixels)
-        h_bar = round_by_factor(height / beta, factor)
-        w_bar = round_by_factor(width / beta, factor)
-    elif h_bar * w_bar < min_pixels:
-        beta = math.sqrt(min_pixels / (height * width))
-        h_bar = round_by_factor(height * beta, factor)
-        w_bar = round_by_factor(width * beta, factor)
-    return h_bar, w_bar
-
-def fetch_image(image_input, min_pixels: int = None, max_pixels: int = None):
-    """Fetch and process an image."""
-    if isinstance(image_input, str):
-        if image_input.startswith(("http://", "https://")):
-            response = requests.get(image_input)
-            image = Image.open(BytesIO(response.content)).convert('RGB')
-        else:
-            image = Image.open(image_input).convert('RGB')
-    elif isinstance(image_input, Image.Image):
-        image = image_input.convert('RGB')
-    else:
-        raise ValueError(f"Invalid image input type: {type(image_input)}")
-    
-    if min_pixels is not None or max_pixels is not None:
-        min_pixels = min_pixels or MIN_PIXELS
-        max_pixels = max_pixels or MAX_PIXELS
-        height, width = smart_resize(
-            image.height,
-            image.width,
-            factor=IMAGE_FACTOR,
-            min_pixels=min_pixels,
-            max_pixels=max_pixels
-        )
-        image = image.resize((width, height), Image.LANCZOS)
-    
-    return image
+# =================================================================================
+# --- UTILITY FUNCTIONS (from markdown_utils.py and utils.py) ---
+# =================================================================================
 
-def load_images_from_pdf(pdf_path: str) -> List[Image.Image]:
-    """Load images from PDF file."""
-    images = []
-    try:
-        pdf_document = fitz.open(pdf_path)
-        for page_num in range(len(pdf_document)):
-            page = pdf_document.load_page(page_num)
-            mat = fitz.Matrix(2.0, 2.0)  # Increase resolution
-            pix = page.get_pixmap(matrix=mat)
-            img_data = pix.tobytes("ppm")
-            image = Image.open(BytesIO(img_data)).convert('RGB')
-            images.append(image)
-        pdf_document.close()
-    except Exception as e:
-        print(f"Error loading PDF: {e}")
-        return []
-    return images
-
-def draw_layout_on_image(image: Image.Image, layout_data: List[Dict]) -> Image.Image:
-    """Draw layout bounding boxes on image."""
-    img_copy = image.copy()
-    draw = ImageDraw.Draw(img_copy)
-    
-    colors = {
-        'Caption': '#FF6B6B', 'Footnote': '#4ECDC4', 'Formula': '#45B7D1',
-        'List-item': '#96CEB4', 'Page-footer': '#FFEAA7', 'Page-header': '#DDA0DD',
-        'Picture': '#FFD93D', 'Section-header': '#6C5CE7', 'Table': '#FD79A8',
-        'Text': '#74B9FF', 'Title': '#E17055'
-    }
-    
-    try:
-        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 12) or ImageFont.load_default()
-        for item in layout_data:
-            if 'bbox' in item and 'category' in item:
-                bbox = item['bbox']
-                category = item['category']
-                color = colors.get(category, '#000000')
-                
-                draw.rectangle(bbox, outline=color, width=2)
-                
-                label = category
-                label_bbox = draw.textbbox((0, 0), label, font=font)
-                label_width, label_height = label_bbox[2] - label_bbox[0], label_bbox[3] - label_bbox[1]
-                
-                label_x, label_y = bbox[0], max(0, bbox[1] - label_height - 2)
-                draw.rectangle([label_x, label_y, label_x + label_width + 4, label_y + label_height + 2], fill=color)
-                draw.text((label_x + 2, label_y + 1), label, fill='white', font=font)
-    except Exception as e:
-        print(f"Error drawing layout: {e}")
-    
-    return img_copy
+# --- Markdown Conversion Utilities ---
 
-def layoutjson2md(image: Image.Image, layout_data: List[Dict], text_key: str = 'text') -> str:
-    """Convert layout JSON to markdown format."""
-    import base64
-    
-    markdown_lines = []
+def extract_table_from_html(html_string):
+    """Extract and clean table tags from HTML string"""
     try:
-        sorted_items = sorted(layout_data, key=lambda x: (x.get('bbox', [0, 0, 0, 0])[1], x.get('bbox', [0, 0, 0, 0])[0]))
-        for item in sorted_items:
-            category = item.get('category', '')
-            text = item.get(text_key, '')
-            bbox = item.get('bbox', [])
-            
-            if category == 'Picture' and bbox and len(bbox) == 4:
-                try:
-                    x1, y1, x2, y2 = [max(0, int(x1)), max(0, int(y1)), min(image.width, int(x2)), min(image.height, int(y2))]
-                    if x2 > x1 and y2 > y1:
-                        cropped_img = image.crop((x1, y1, x2, y2))
-                        buffer = BytesIO()
-                        cropped_img.save(buffer, format='PNG')
-                        img_data = base64.b64encode(buffer.getvalue()).decode()
-                        markdown_lines.append(f"![Image](data:image/png;base64,{img_data})\n")
+        table_pattern = re.compile(r'<table.*?>.*?</table>', re.DOTALL)
+        tables = table_pattern.findall(html_string)
+        tables = [re.sub(r'<table[^>]*>', '<table>', table) for table in tables]
+        return '\n'.join(tables)
+    except Exception as e:
+        print(f"extract_table_from_html error: {str(e)}")
+        return f"<table><tr><td>Error extracting table: {str(e)}</td></tr></table>"
+
+
+class MarkdownConverter:
+    """Convert structured recognition results to Markdown format"""
+    def __init__(self):
+        self.heading_levels = {'title': '#', 'sec': '##', 'sub_sec': '###'}
+        self.special_labels = {'tab', 'fig', 'title', 'sec', 'sub_sec', 'list', 'formula', 'reference', 'alg'}
+
+    def try_remove_newline(self, text: str) -> str:
+        try:
+            text = text.strip().replace('-\n', '')
+            def is_chinese(char): return '\u4e00' <= char <= '\u9fff'
+            lines, processed_lines = text.split('\n'), []
+            for i in range(len(lines)-1):
+                current_line, next_line = lines[i].strip(), lines[i+1].strip()
+                if current_line:
+                    if next_line:
+                        if is_chinese(current_line[-1]) and is_chinese(next_line[0]):
+                            processed_lines.append(current_line)
+                        else:
+                            processed_lines.append(current_line + ' ')
                     else:
-                        markdown_lines.append("![Image](Image region detected)\n")
-                except Exception as e:
-                    print(f"Error processing image region: {e}")
-                    markdown_lines.append("![Image](Image detected)\n")
-            elif not text:
-                continue
-            elif category == 'Title':
-                markdown_lines.append(f"# {text}\n")
-            elif category == 'Section-header':
-                markdown_lines.append(f"## {text}\n")
-            elif category == 'Text':
-                markdown_lines.append(f"{text}\n")
-            elif category == 'List-item':
-                markdown_lines.append(f"- {text}\n")
-            elif category == 'Table':
-                markdown_lines.append(f"{text}\n" if text.strip().startswith('<') else f"**Table:** {text}\n")
-            elif category == 'Formula':
-                markdown_lines.append(f"$$\n{text}\n$$\n" if text.strip().startswith('$') or '\\' in text else f"**Formula:** {text}\n")
-            elif category == 'Caption':
-                markdown_lines.append(f"*{text}*\n")
-            elif category == 'Footnote':
-                markdown_lines.append(f"^{text}^\n")
-            elif category in ['Page-header', 'Page-footer']:
-                continue
+                        processed_lines.append(current_line + '\n')
+                else:
+                    processed_lines.append('\n')
+            if lines and lines[-1].strip():
+                processed_lines.append(lines[-1].strip())
+            return ''.join(processed_lines)
+        except Exception as e:
+            print(f"try_remove_newline error: {str(e)}")
+            return text
+
+    def _handle_text(self, text: str) -> str:
+        try:
+            if not text: return ""
+            if text.strip().startswith("\\begin{array}") and text.strip().endswith("\\end{array}"):
+                text = "$$" + text + "$$"
+            elif ("_{" in text or "^{" in text or "\\" in text or "_ {" in text or "^ {" in text) and ("$" not in text) and ("\\begin" not in text):
+                text = "$" + text + "$"
+            text = self._process_formulas_in_text(text)
+            text = self.try_remove_newline(text)
+            return text
+        except Exception as e:
+            print(f"_handle_text error: {str(e)}")
+            return text
+
+    def _process_formulas_in_text(self, text: str) -> str:
+        try:
+            delimiters = [('$$', '$$'), ('\\[', '\\]'), ('$', '$'), ('\\(', '\\)')]
+            result = text
+            for start_delim, end_delim in delimiters:
+                current_pos, processed_parts = 0, []
+                while current_pos < len(result):
+                    start_pos = result.find(start_delim, current_pos)
+                    if start_pos == -1:
+                        processed_parts.append(result[current_pos:])
+                        break
+                    processed_parts.append(result[current_pos:start_pos])
+                    end_pos = result.find(end_delim, start_pos + len(start_delim))
+                    if end_pos == -1:
+                        processed_parts.append(result[start_pos:])
+                        break
+                    formula_content = result[start_pos + len(start_delim):end_pos]
+                    processed_formula = formula_content.replace('\n', ' \\\\ ')
+                    processed_parts.append(f"{start_delim}{processed_formula}{end_delim}")
+                    current_pos = end_pos + len(end_delim)
+                result = ''.join(processed_parts)
+            return result
+        except Exception as e:
+            print(f"_process_formulas_in_text error: {str(e)}")
+            return text
+
+    def _remove_newline_in_heading(self, text: str) -> str:
+        try:
+            def is_chinese(char): return '\u4e00' <= char <= '\u9fff'
+            return text.replace('\n', '') if any(is_chinese(char) for char in text) else text.replace('\n', ' ')
+        except Exception as e:
+            print(f"_remove_newline_in_heading error: {str(e)}")
+            return text
+
+    def _handle_heading(self, text: str, label: str) -> str:
+        try:
+            level = self.heading_levels.get(label, '#')
+            text = self._remove_newline_in_heading(text.strip())
+            text = self._handle_text(text)
+            return f"{level} {text}\n\n"
+        except Exception as e:
+            print(f"_handle_heading error: {str(e)}")
+            return f"# Error processing heading: {text}\n\n"
+
+    def _handle_list_item(self, text: str) -> str:
+        try:
+            return f"- {text.strip()}\n"
+        except Exception as e:
+            print(f"_handle_list_item error: {str(e)}")
+            return f"- Error processing list item: {text}\n"
+
+    def _handle_figure(self, text: str, section_count: int) -> str:
+        try:
+            if not text.strip():
+                return f"![Figure {section_count}](data:image/png;base64,)\n\n"
+            if text.startswith("data:image/"):
+                return f"![Figure {section_count}]({text})\n\n"
             else:
-                markdown_lines.append(f"{text}\n")
-            markdown_lines.append("")
-    except Exception as e:
-        print(f"Error converting to markdown: {e}")
-        return str(layout_data)
-    
-    return "\n".join(markdown_lines)
-
-# Load Models
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load dot.ocr
-model_id = "rednote-hilab/dots.ocr"
-model_path = "./models/dots-ocr-local"
-snapshot_download(repo_id=model_id, local_dir=model_path, local_dir_use_symlinks=False)
-model = AutoModelForCausalLM.from_pretrained(
-    model_path,
-    attn_implementation="flash_attention_2",
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    trust_remote_code=True
-)
-processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
-
-# Load Camel-Doc-OCR-062825
-MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_M,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load Megalodon-OCR-Sync-0713
-MODEL_ID_T = "prithivMLmods/Megalodon-OCR-Sync-0713"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_T,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Model Dictionary
-model_dict = {
-    "dot.ocr": {"model": model, "processor": processor, "process_layout": True},
-    "Camel-Doc-OCR-062825": {"model": model_m, "processor": processor_m, "process_layout": False},
-    "Megalodon-OCR-Sync-0713": {"model": model_t, "processor": processor_t, "process_layout": False},
-}
-
-# Global State
-pdf_cache = {"images": [], "current_page": 0, "total_pages": 0, "file_type": None, "is_parsed": False, "results": []}
-
-@spaces.GPU()
-def inference(model, processor, image: Image.Image, prompt: str, max_new_tokens: int = 24000) -> str:
-    """Run inference on an image with the given prompt using the specified model and processor."""
+                return f"![Figure {section_count}](data:image/png;base64,{text})\n\n"
+        except Exception as e:
+            print(f"_handle_figure error: {str(e)}")
+            return f"*[Error processing figure: {str(e)}]*\n\n"
+
+    def _handle_table(self, text: str) -> str:
+        try:
+            if '<table' in text.lower() or '<tr' in text.lower():
+                return extract_table_from_html(text) + "\n\n"
+            else:
+                table_lines = text.split('\n')
+                if not table_lines: return "\n\n"
+                col_count = len(table_lines[0].split()) if table_lines[0] else 1
+                header = '| ' + ' | '.join(table_lines[0].split()) + ' |'
+                separator = '| ' + ' | '.join(['---'] * col_count) + ' |'
+                rows = [f"| {' | '.join(line.split())} |" for line in table_lines[1:]]
+                return '\n'.join([header, separator] + rows) + '\n\n'
+        except Exception as e:
+            print(f"_handle_table error: {str(e)}")
+            return f"*[Error processing table: {str(e)}]*\n\n"
+
+    def _handle_algorithm(self, text: str) -> str:
+        try:
+            text = re.sub(r'\\begin\{algorithm\}(.*?)\\end\{algorithm\}', r'\1', text, flags=re.DOTALL)
+            text = text.replace('\\begin{algorithmic}', '').replace('\\end{algorithmic}', '')
+            caption_match = re.search(r'\\caption\{(.*?)\}', text)
+            caption = f"**{caption_match.group(1)}**\n\n" if caption_match else ""
+            algorithm_text = re.sub(r'\\caption\{.*?\}', '', text).strip()
+            return f"{caption}```\n{algorithm_text}\n```\n\n"
+        except Exception as e:
+            print(f"_handle_algorithm error: {str(e)}")
+            return f"*[Error processing algorithm: {str(e)}]*\n\n{text}\n\n"
+
+    def _handle_formula(self, text: str) -> str:
+        try:
+            processed_text = self._process_formulas_in_text(text)
+            if '$$' not in processed_text and '\\[' not in processed_text:
+                processed_text = f'$${processed_text}$$'
+            return f"{processed_text}\n\n"
+        except Exception as e:
+            print(f"_handle_formula error: {str(e)}")
+            return f"*[Error processing formula: {str(e)}]*\n\n"
+
+    def convert(self, recognition_results: List[Dict[str, Any]]) -> str:
+        markdown_content = []
+        for i, result in enumerate(recognition_results):
+            try:
+                label, text = result.get('label', ''), result.get('text', '').strip()
+                if label == 'fig':
+                    markdown_content.append(self._handle_figure(text, i))
+                    continue
+                if not text: continue
+                
+                if label in {'title', 'sec', 'sub_sec'}:
+                    markdown_content.append(self._handle_heading(text, label))
+                elif label == 'list':
+                    markdown_content.append(self._handle_list_item(text))
+                elif label == 'tab':
+                    markdown_content.append(self._handle_table(text))
+                elif label == 'alg':
+                    markdown_content.append(self._handle_algorithm(text))
+                elif label == 'formula':
+                    markdown_content.append(self._handle_formula(text))
+                elif label not in self.special_labels:
+                    markdown_content.append(f"{self._handle_text(text)}\n\n")
+            except Exception as e:
+                print(f"Error processing item {i}: {str(e)}")
+                markdown_content.append(f"*[Error processing content]*\n\n")
+        return self._post_process(''.join(markdown_content))
+
+    def _post_process(self, md: str) -> str:
+        try:
+            md = re.sub(r'\\author\{(.*?)\}', lambda m: self._handle_text(m.group(1)), md, flags=re.DOTALL)
+            md = re.sub(r'\$(\\author\{.*?\})\$', lambda m: self._handle_text(re.search(r'\\author\{(.*?)\}', m.group(1), re.DOTALL).group(1)), md, flags=re.DOTALL)
+            md = re.sub(r'\\begin\{abstract\}(.*?)\\end\{abstract\}', r'**Abstract** \1', md, flags=re.DOTALL)
+            md = re.sub(r'\\begin\{abstract\}', r'**Abstract**', md)
+            md = re.sub(r'\\eqno\{\((.*?)\)\}', r'\\tag{\1}', md)
+            md = md.replace("\[ \\\\", "$$ \\\\").replace("\\\\ \]", "\\\\ $$")
+            md = re.sub(r'_ {', r'_{', md)
+            md = re.sub(r'^ {', r'^{', md)
+            md = re.sub(r'\n{3,}', r'\n\n', md)
+            return md
+        except Exception as e:
+            print(f"_post_process error: {str(e)}")
+            return md
+
+# --- General Processing Utilities ---
+@dataclass
+class ImageDimensions:
+    original_w: int
+    original_h: int
+    padded_w: int
+    padded_h: int
+
+def adjust_box_edges(image, boxes: List[List[float]], max_pixels=15, threshold=0.2):
+    if isinstance(image, str):
+        image = cv2.imread(image)
+    img_h, img_w = image.shape[:2]
+    new_boxes = []
+    for box in boxes:
+        best_box = copy.deepcopy(box)
+
+        def check_edge(img, current_box, i, is_vertical):
+            edge = current_box[i]
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+            if is_vertical:
+                line = binary[current_box[1] : current_box[3] + 1, edge]
+            else:
+                line = binary[edge, current_box[0] : current_box[2] + 1]
+            transitions = np.abs(np.diff(line))
+            return np.sum(transitions) / len(transitions)
+
+        edges = [(0, -1, True), (2, 1, True), (1, -1, False), (3, 1, False)]
+        current_box = copy.deepcopy(box)
+        current_box = [min(max(c, 0), d - 1) for c, d in zip(current_box, [img_w, img_h, img_w, img_h])]
+
+        for i, direction, is_vertical in edges:
+            best_score = check_edge(image, current_box, i, is_vertical)
+            if best_score <= threshold: continue
+            for _ in range(max_pixels):
+                current_box[i] += direction
+                dim = img_w if i in [0, 2] else img_h
+                current_box[i] = min(max(current_box[i], 0), dim - 1)
+                score = check_edge(image, current_box, i, is_vertical)
+                if score < best_score:
+                    best_score, best_box = score, copy.deepcopy(current_box)
+                if score <= threshold: break
+        new_boxes.append(best_box)
+    return new_boxes
+
+def parse_layout_string(bbox_str):
+    pattern = r"\[(\d*\.?\d+),\s*(\d*\.?\d+),\s*(\d*\.?\d+),\s*(\d*\.?\d+)\]\s*(\w+)"
+    matches = re.finditer(pattern, bbox_str)
+    return [([float(m.group(i)) for i in range(1, 5)], m.group(5).strip()) for m in matches]
+
+def map_to_original_coordinates(x1, y1, x2, y2, dims: ImageDimensions) -> Tuple[int, int, int, int]:
     try:
-        messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": prompt}]}]
-        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        image_inputs, video_inputs = process_vision_info(messages)
-        inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt").to(device)
-        
-        with torch.no_grad():
-            generated_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=0.1)
-        generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
-        output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-        return output_text[0] if output_text else ""
+        top, left = (dims.padded_h - dims.original_h) // 2, (dims.padded_w - dims.original_w) // 2
+        orig_x1, orig_y1 = max(0, x1 - left), max(0, y1 - top)
+        orig_x2, orig_y2 = min(dims.original_w, x2 - left), min(dims.original_h, y2 - top)
+        if orig_x2 <= orig_x1: orig_x2 = min(orig_x1 + 1, dims.original_w)
+        if orig_y2 <= orig_y1: orig_y2 = min(orig_y1 + 1, dims.original_h)
+        return int(orig_x1), int(orig_y1), int(orig_x2), int(orig_y2)
     except Exception as e:
-        print(f"Error during inference: {e}")
-        traceback.print_exc()
-        return f"Error during inference: {str(e)}"
-
-def process_image(
-    image: Image.Image,
-    model,
-    processor,
-    process_layout: bool,
-    min_pixels: Optional[int] = None,
-    max_pixels: Optional[int] = None
-) -> Dict[str, Any]:
-    """Process a single image with the specified model and processor."""
+        print(f"map_to_original_coordinates error: {str(e)}")
+        return 0, 0, min(100, dims.original_w), min(100, dims.original_h)
+
+def process_coordinates(coords, padded_image, dims: ImageDimensions, previous_box=None):
     try:
-        if min_pixels is not None or max_pixels is not None:
-            image = fetch_image(image, min_pixels=min_pixels, max_pixels=max_pixels)
+        x1, y1 = int(coords[0] * dims.padded_w), int(coords[1] * dims.padded_h)
+        x2, y2 = int(coords[2] * dims.padded_w), int(coords[3] * dims.padded_h)
         
-        raw_output = inference(model, processor, image, prompt)
-        result = {'original_image': image, 'raw_output': raw_output, 'processed_image': image, 'layout_result': None, 'markdown_content': raw_output}
+        x1, y1, x2, y2 = max(0, x1), max(0, y1), min(dims.padded_w, x2), min(dims.padded_h, y2)
+        if x2 <= x1: x2 = min(x1 + 1, dims.padded_w)
+        if y2 <= y1: y2 = min(y1 + 1, dims.padded_h)
         
-        if process_layout:
-            try:
-                layout_data = json.loads(raw_output)
-                result['layout_result'] = layout_data
-                result['processed_image'] = draw_layout_on_image(image, layout_data)
-                result['markdown_content'] = layoutjson2md(image, layout_data, text_key='text')
-            except json.JSONDecodeError:
-                print("Failed to parse JSON output, using raw output")
-            except Exception as e:
-                print(f"Error processing layout: {e}")
+        x1, y1, x2, y2 = adjust_box_edges(padded_image, [[x1, y1, x2, y2]])[0]
         
-        return result
+        if previous_box:
+            prev_x1, prev_y1, prev_x2, prev_y2 = previous_box
+            if (x1 < prev_x2 and x2 > prev_x1) and (y1 < prev_y2 and y2 > prev_y1):
+                y1 = min(prev_y2, dims.padded_h - 1)
+                if y2 <= y1: y2 = min(y1 + 1, dims.padded_h)
+
+        orig_coords = map_to_original_coordinates(x1, y1, x2, y2, dims)
+        return x1, y1, x2, y2, *orig_coords, [x1, y1, x2, y2]
     except Exception as e:
-        print(f"Error processing image: {e}")
-        traceback.print_exc()
-        return {'original_image': image, 'raw_output': str(e), 'processed_image': image, 'layout_result': None, 'markdown_content': str(e)}
-
-def load_file_for_preview(file_path: str) -> Tuple[Optional[Image.Image], str]:
-    """Load file for preview (supports PDF and images)."""
-    global pdf_cache
-    if not file_path or not os.path.exists(file_path):
-        return None, "No file selected"
-    
-    file_ext = os.path.splitext(file_path)[1].lower()
+        print(f"process_coordinates error: {str(e)}")
+        orig_coords = 0, 0, min(100, dims.original_w), min(100, dims.original_h)
+        return 0, 0, 100, 100, *orig_coords, [0, 0, 100, 100]
+
+def prepare_image(image) -> Tuple[np.ndarray, ImageDimensions]:
     try:
-        if file_ext == '.pdf':
-            images = load_images_from_pdf(file_path)
-            if not images:
-                return None, "Failed to load PDF"
-            pdf_cache.update({"images": images, "current_page": 0, "total_pages": len(images), "file_type": "pdf", "is_parsed": False, "results": []})
-            return images[0], f"Page 1 / {len(images)}"
-        elif file_ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
-            image = Image.open(file_path).convert('RGB')
-            pdf_cache.update({"images": [image], "current_page": 0, "total_pages": 1, "file_type": "image", "is_parsed": False, "results": []})
-            return image, "Page 1 / 1"
-        else:
-            return None, f"Unsupported file format: {file_ext}"
+        image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        original_h, original_w = image_cv.shape[:2]
+        max_size = max(original_h, original_w)
+        top, bottom = (max_size - original_h) // 2, max_size - original_h - ((max_size - original_h) // 2)
+        left, right = (max_size - original_w) // 2, max_size - original_w - ((max_size - original_w) // 2)
+        padded_image = cv2.copyMakeBorder(image_cv, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(0, 0, 0))
+        padded_h, padded_w = padded_image.shape[:2]
+        dims = ImageDimensions(original_w, original_h, padded_w, padded_h)
+        return padded_image, dims
     except Exception as e:
-        print(f"Error loading file: {e}")
-        return None, f"Error loading file: {str(e)}"
-
-def turn_page(direction: str) -> Tuple[Optional[Image.Image], str, Any, Optional[Image.Image], Optional[Dict]]:
-    """Navigate through PDF pages and update outputs."""
-    global pdf_cache
-    if not pdf_cache["images"]:
-        return None, '<div class="page-info">No file loaded</div>', "No results yet", None, None
+        print(f"prepare_image error: {str(e)}")
+        dims = ImageDimensions(image.width, image.height, image.width, image.height)
+        return np.zeros((image.height, image.width, 3), dtype=np.uint8), dims
+
+
+# =================================================================================
+# --- MODEL WRAPPER CLASSES ---
+# =================================================================================
+
+class DotOcrModel:
+    def __init__(self, device: str):
+        self.model, self.processor, self.device = None, None, device
+        self.model_id, self.model_path = "rednote-hilab/dots.ocr", "./models/dots-ocr-local"
+
+    @spaces.GPU()
+    def load_model(self):
+        if self.model is None:
+            print("Loading dot.ocr model...")
+            snapshot_download(repo_id=self.model_id, local_dir=self.model_path, local_dir_use_symlinks=False)
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_path, attn_implementation="flash_attention_2",
+                torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+            )
+            self.processor = AutoProcessor.from_pretrained(self.model_path, trust_remote_code=True)
+            print("dot.ocr model loaded.")
+
+    @staticmethod
+    def smart_resize(height, width, factor, min_pixels, max_pixels):
+        if max(height, width) / min(height, width) > 200: raise ValueError("Aspect ratio too high")
+        h_bar, w_bar = max(factor, round(height / factor) * factor), max(factor, round(width / factor) * factor)
+        if h_bar * w_bar > max_pixels:
+            beta = math.sqrt((height * width) / max_pixels)
+            h_bar, w_bar = round(height / beta / factor) * factor, round(width / beta / factor) * factor
+        elif h_bar * w_bar < min_pixels:
+            beta = math.sqrt(min_pixels / (height * width))
+            h_bar, w_bar = round(height * beta / factor) * factor, round(width / beta / factor) * factor
+        return h_bar, w_bar
+
+    def fetch_image(self, image_input, min_pixels, max_pixels):
+        image = image_input.convert('RGB')
+        height, width = self.smart_resize(image.height, image.width, IMAGE_FACTOR, min_pixels, max_pixels)
+        return image.resize((width, height), Image.LANCZOS)
 
-    pdf_cache["current_page"] = max(0, pdf_cache["current_page"] - 1) if direction == "prev" else min(pdf_cache["total_pages"] - 1, pdf_cache["current_page"] + 1)
-    index = pdf_cache["current_page"]
-    current_image_preview = pdf_cache["images"][index]
-    page_info_html = f'<div class="page-info">Page {index + 1} / {pdf_cache["total_pages"]}</div>'
+    @spaces.GPU()
+    def inference(self, image: Image.Image, prompt: str, max_new_tokens: int = 24000) -> str:
+        self.load_model()
+        messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": prompt}]}]
+        text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, _ = process_vision_info(messages)
+        inputs = self.processor(text=[text], images=image_inputs, padding=True, return_tensors="pt").to(self.device)
+        with torch.no_grad():
+            generated_ids = self.model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=0.1)
+        generated_ids_trimmed = [out[len(ins):] for ins, out in zip(inputs.input_ids, generated_ids)]
+        return self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+    def process_image(self, image: Image.Image, min_pixels: int, max_pixels: int):
+        resized_image = self.fetch_image(image, min_pixels, max_pixels)
+        raw_output = self.inference(resized_image, DOT_OCR_PROMPT)
+        result = {'original_image': image, 'raw_output': raw_output, 'layout_result': None}
+        try:
+            layout_data = json.loads(raw_output)
+            result['layout_result'] = layout_data
+            result['processed_image'] = self.draw_layout_on_image(image, layout_data)
+            result['markdown_content'] = self.layoutjson2md(image, layout_data)
+        except (json.JSONDecodeError, KeyError) as e:
+            print(f"Failed to parse or process dot.ocr layout: {e}")
+            result['processed_image'] = image
+            result['markdown_content'] = f"### Error processing output\nRaw model output:\n```json\n{raw_output}\n```"
+        return result
 
-    markdown_content, processed_img, layout_json = "Page not processed yet", None, None
-    if pdf_cache["is_parsed"] and index < len(pdf_cache["results"]) and pdf_cache["results"][index]:
-        result = pdf_cache["results"][index]
-        markdown_content = result.get('markdown_content') or result.get('raw_output', 'No content available')
-        processed_img = result.get('processed_image')
-        layout_json = result.get('layout_result')
+    def draw_layout_on_image(self, image: Image.Image, layout_data: List[Dict]) -> Image.Image:
+        img_copy, draw = image.copy(), ImageDraw.Draw(img_copy)
+        colors = {'Caption': '#FF6B6B', 'Footnote': '#4ECDC4', 'Formula': '#45B7D1', 'List-item': '#96CEB4',
+                  'Page-footer': '#FFEAA7', 'Page-header': '#DDA0DD', 'Picture': '#FFD93D', 'Section-header': '#6C5CE7',
+                  'Table': '#FD79A8', 'Text': '#74B9FF', 'Title': '#E17055'}
+        try: font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 15)
+        except: font = ImageFont.load_default()
+        for item in layout_data:
+            if 'bbox' in item and 'category' in item:
+                bbox, category, color = item['bbox'], item['category'], colors.get(category, '#000000')
+                draw.rectangle(bbox, outline=color, width=3)
+                label_bbox = draw.textbbox((0, 0), category, font=font)
+                label_width, label_height = label_bbox[2] - label_bbox[0], label_bbox[3] - label_bbox[1]
+                label_x, label_y = bbox[0], max(0, bbox[1] - label_height - 5)
+                draw.rectangle([label_x, label_y, label_x + label_width + 4, label_y + label_height + 4], fill=color)
+                draw.text((label_x + 2, label_y + 2), category, fill='white', font=font)
+        return img_copy
 
-    return current_image_preview, page_info_html, markdown_content, processed_img, layout_json
+    def layoutjson2md(self, image: Image.Image, layout_data: List[Dict]) -> str:
+        md_lines, sorted_items = [], sorted(layout_data, key=lambda x: (x.get('bbox', [0]*4)[1], x.get('bbox', [0]*4)[0]))
+        for item in sorted_items:
+            cat, txt, bbox = item.get('category'), item.get('text'), item.get('bbox')
+            if cat == 'Picture' and bbox:
+                try:
+                    x1, y1, x2, y2 = max(0, int(bbox[0])), max(0, int(bbox[1])), min(image.width, int(bbox[2])), min(image.height, int(bbox[3]))
+                    if x2 > x1 and y2 > y1:
+                        cropped = image.crop((x1, y1, x2, y2))
+                        buffer = BytesIO()
+                        cropped.save(buffer, format='PNG')
+                        img_data = base64.b64encode(buffer.getvalue()).decode()
+                        md_lines.append(f"![Image](data:image/png;base64,{img_data})\n")
+                except Exception: md_lines.append("![Image](Image region detected)\n")
+            elif not txt: continue
+            elif cat == 'Title': md_lines.append(f"# {txt}\n")
+            elif cat == 'Section-header': md_lines.append(f"## {txt}\n")
+            elif cat == 'List-item': md_lines.append(f"- {txt}\n")
+            elif cat == 'Formula': md_lines.append(f"$$\n{txt}\n$$\n")
+            elif cat == 'Caption': md_lines.append(f"*{txt}*\n")
+            elif cat == 'Footnote': md_lines.append(f"^{txt}^\n")
+            elif cat in ['Text', 'Table']: md_lines.append(f"{txt}\n")
+        return "\n".join(md_lines)
+
+class DolphinModel:
+    def __init__(self, device: str):
+        self.model, self.processor, self.tokenizer, self.device = None, None, None, device
+        self.model_id = "ByteDance/Dolphin"
+
+    @spaces.GPU()
+    def load_model(self):
+        if self.model is None:
+            print("Loading Dolphin model...")
+            self.processor = AutoProcessor.from_pretrained(self.model_id)
+            self.model = VisionEncoderDecoderModel.from_pretrained(self.model_id).eval().to(self.device).half()
+            self.tokenizer = self.processor.tokenizer
+            print("Dolphin model loaded.")
+
+    @spaces.GPU()
+    def model_chat(self, prompt, image):
+        self.load_model()
+        images = image if isinstance(image, list) else [image]
+        prompts = prompt if isinstance(prompt, list) else [prompt] * len(images)
+        batch_inputs = self.processor(images, return_tensors="pt", padding=True)
+        batch_pixel_values = batch_inputs.pixel_values.half().to(self.device)
+        prompts = [f"<s>{p} <Answer/>" for p in prompts]
+        batch_prompt_inputs = self.tokenizer(prompts, add_special_tokens=False, return_tensors="pt")
+        batch_prompt_ids = batch_prompt_inputs.input_ids.to(self.device)
+        batch_attention_mask = batch_prompt_inputs.attention_mask.to(self.device)
+        outputs = self.model.generate(
+            pixel_values=batch_pixel_values, decoder_input_ids=batch_prompt_ids,
+            decoder_attention_mask=batch_attention_mask, max_length=4096,
+            pad_token_id=self.tokenizer.pad_token_id, eos_token_id=self.tokenizer.eos_token_id,
+            use_cache=True, bad_words_ids=[[self.tokenizer.unk_token_id]],
+            return_dict_in_generate=True, do_sample=False, num_beams=1, repetition_penalty=1.1
+        )
+        sequences = self.tokenizer.batch_decode(outputs.sequences, skip_special_tokens=False)
+        results = [seq.replace(p, "").replace("<pad>", "").replace("</s>", "").strip() for p, seq in zip(prompts, sequences)]
+        return results if isinstance(image, list) else results[0]
+
+    def process_elements(self, layout_str: str, image: Image.Image, max_batch_size: int = 16):
+        padded_image, dims = prepare_image(image)
+        layout_results = parse_layout_string(layout_str)
+        elements, reading_order = [], 0
+        for bbox, label in layout_results:
+            try:
+                coords = process_coordinates(bbox, padded_image, dims)
+                x1, y1, x2, y2, orig_x1, orig_y1, orig_x2, orig_y2 = coords[:8]
+                cropped = padded_image[y1:y2, x1:x2]
+                if cropped.size > 0 and cropped.shape[0] > 3 and cropped.shape[1] > 3:
+                    pil_crop = Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB))
+                    elements.append({"crop": pil_crop, "label": label, "bbox": [orig_x1, orig_y1, orig_x2, orig_y2], "reading_order": reading_order})
+                reading_order += 1
+            except Exception as e:
+                print(f"Error processing Dolphin element bbox {bbox}: {e}")
+        
+        text_elems = self.process_element_batch([e for e in elements if e['label'] != 'tab' and e['label'] != 'fig'], "Read text in the image.", max_batch_size)
+        table_elems = self.process_element_batch([e for e in elements if e['label'] == 'tab'], "Parse the table in the image.", max_batch_size)
+        fig_elems = [{"label": e['label'], "bbox": e['bbox'], "text": "", "reading_order": e['reading_order']} for e in elements if e['label'] == 'fig']
+        
+        all_results = sorted(text_elems + table_elems + fig_elems, key=lambda x: x['reading_order'])
+        return all_results
+
+    def process_element_batch(self, elements, prompt, max_batch_size=16):
+        results = []
+        for i in range(0, len(elements), max_batch_size):
+            batch = elements[i:i+max_batch_size]
+            crops = [elem["crop"] for elem in batch]
+            prompts = [prompt] * len(crops)
+            batch_results = self.model_chat(prompts, crops)
+            for j, res_text in enumerate(batch_results):
+                elem = batch[j]
+                results.append({"label": elem["label"], "bbox": elem["bbox"], "text": res_text.strip(), "reading_order": elem["reading_order"]})
+        return results
+
+    def process_image(self, image: Image.Image):
+        layout_output = self.model_chat("Parse the reading order of this document.", image)
+        recognition_results = self.process_elements(layout_output, image)
+        markdown_content = MarkdownConverter().convert(recognition_results)
+        return {
+            'original_image': image, 'processed_image': image, 'markdown_content': markdown_content,
+            'layout_result': recognition_results, 'raw_output': layout_output
+        }
+
+
+# =================================================================================
+# --- GRADIO UI AND EVENT HANDLERS ---
+# =================================================================================
 
 def create_gradio_interface():
-    """Create the Gradio interface."""
+    """Create the main Gradio interface and define all event handlers"""
+    
     css = """
     .main-container { max-width: 1400px; margin: 0 auto; }
     .header-text { text-align: center; color: #2c3e50; margin-bottom: 20px; }
-    .process-button { 
-        border: none !important; 
-        color: white !important; 
-        font-weight: bold !important; 
-        background-color: blue !important;} 
-    .process-button:hover { 
-        background-color: darkblue !important;
-        transform: translateY(-2px) !important; 
-        box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
+    .process-button { border: none !important; color: white !important; font-weight: bold !important; background-color: blue !important;}
+    .process-button:hover { background-color: darkblue !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
     .info-box { border: 1px solid #dee2e6; border-radius: 8px; padding: 15px; margin: 10px 0; }
     .page-info { text-align: center; padding: 8px 16px; border-radius: 20px; font-weight: bold; margin: 10px 0; }
-    .model-status { padding: 10px; border-radius: 8px; margin: 10px 0; text-align: center; font-weight: bold; }
-    .status-ready { background: #d1edff; color: #0c5460; border: 1px solid #b8daff; }
     """
-
-    with gr.Blocks(theme="bethecloud/storj_theme", css=css, title="Dot●OCR Comparator") as demo:
+    
+    with gr.Blocks(theme="bethecloud/storj_theme", css=css, title="Dot.OCR Comparator") as demo:
         gr.HTML("""
         <div class="title" style="text-align: center">
-            <h1>Dot<span style="color: red;">●</span>OCR Comparator</h1>
+            <h1>Dot<span style="color: red;">●</span><strong></strong>OCR Comparator</h1>
             <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
                 Advanced vision-language model for image/PDF to markdown document processing
             </p>
         </div>
         """)
         
-        with gr.Row():
+        with gr.Row(elem_classes=["main-container"]):
             with gr.Column(scale=1):
-                model_choice = gr.Radio(
-                    choices=["dot.ocr", "Camel-Doc-OCR-062825", "Megalodon-OCR-Sync-0713"],
-                    label="Select Model",
-                    value="dot.ocr"
-                )
-                file_input = gr.File(label="Upload Image or PDF", file_types=[".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".pdf"], type="filepath")
+                file_input = gr.File(label="Upload Image or PDF", file_types=[".jpg", ".jpeg", ".png", ".pdf"], type="filepath")
+                
                 with gr.Row():
                     examples = gr.Examples(
                         examples=["examples/sample_image1.png", "examples/sample_image2.png", "examples/sample_pdf.pdf"],
                         inputs=file_input,
                         label="Example Documents"
                     )
-                image_preview = gr.Image(label="Preview", type="pil", interactive=False, height=300)
+
+                model_choice = gr.Radio(choices=["dot.ocr", "Dolphin"], label="Select Model", value="dot.ocr")
+                image_preview = gr.Image(label="Preview", type="pil", interactive=False, height=400)
+                
                 with gr.Row():
-                    prev_page_btn = gr.Button("◀ Previous", size="md")
+                    prev_page_btn = gr.Button("◀ Previous")
                     page_info = gr.HTML('<div class="page-info">No file loaded</div>')
-                    next_page_btn = gr.Button("Next ▶", size="md")
-                with gr.Accordion("Advanced Settings", open=False):
-                    max_new_tokens = gr.Slider(minimum=1000, maximum=32000, value=24000, step=1000, label="Max New Tokens")
-                    min_pixels = gr.Number(value=MIN_PIXELS, label="Min Pixels")
-                    max_pixels = gr.Number(value=MAX_PIXELS, label="Max Pixels")
-                process_btn = gr.Button("🚀 Process Document", variant="primary", elem_classes=["process-button"], size="lg")
-                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
+                    next_page_btn = gr.Button("Next ▶")
+                
+                with gr.Accordion("Advanced Settings (dot.ocr only)", open=False):
+                    min_pixels = gr.Number(value=MIN_PIXELS, label="Min Pixels", step=1)
+                    max_pixels = gr.Number(value=MAX_PIXELS, label="Max Pixels", step=1)
+                
+                with gr.Row():
+                    process_btn = gr.Button("🚀 Process Document", variant="primary", elem_classes=["process-button"], scale=2)
+                    clear_btn = gr.Button("🗑️ Clear", variant="secondary")
             
             with gr.Column(scale=2):
                 with gr.Tabs():
-                    with gr.Tab("🖼️ Processed Image"):
-                        processed_image = gr.Image(label="Image with Layout Detection", type="pil", interactive=False, height=500)
                     with gr.Tab("📝 Extracted Content"):
-                        markdown_output = gr.Markdown(value="Click 'Process Document' to see extracted content...", height=500)
+                        markdown_output = gr.Markdown(value="Click 'Process Document' to see extracted content...", elem_id="markdown_output")
+                    with gr.Tab("🖼️ Processed Image"):
+                        processed_image_output = gr.Image(label="Image with Layout Detection", type="pil", interactive=False)
                     with gr.Tab("📋 Layout JSON"):
-                        json_output = gr.JSON(label="Layout Analysis Results", value=None)
-
-        def process_document(file_path, model_choice, max_tokens, min_pix, max_pix):
-            """Process the uploaded document with the selected model."""
-            global pdf_cache
-            if not file_path:
-                return None, "Please upload a file first.", None
-            if model_choice not in model_dict:
-                return None, "Invalid model selected", None
+                        json_output = gr.JSON(label="Layout Analysis Results")
+
+        def load_file_for_preview(file_path: str) -> Tuple[List[Image.Image], str]:
+            images = []
+            if not file_path or not os.path.exists(file_path): return [], "No file selected"
+            try:
+                ext = os.path.splitext(file_path)[1].lower()
+                if ext == '.pdf':
+                    doc = fitz.open(file_path)
+                    for page in doc:
+                        pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
+                        images.append(Image.open(BytesIO(pix.tobytes("ppm"))).convert('RGB'))
+                    doc.close()
+                elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
+                    images.append(Image.open(file_path).convert('RGB'))
+                return images, f"Page 1 / {len(images)}"
+            except Exception as e:
+                print(f"Error loading file for preview: {e}")
+                return [], f"Error loading file: {e}"
+
+        def handle_file_upload(file_path):
+            global PDF_CACHE
+            images, page_info_str = load_file_for_preview(file_path)
+            if not images:
+                return None, page_info_str
+            PDF_CACHE = {
+                "images": images, "current_page": 0, "total_pages": len(images),
+                "is_parsed": False, "results": [], "model_used": None
+            }
+            return images[0], f'<div class="page-info">{page_info_str}</div>'
+
+        def process_document(file_path, model_name, min_pix, max_pix):
+            global PDF_CACHE
+            if not file_path or not PDF_CACHE["images"]:
+                return "Please upload a file first.", None, None
             
-            selected_model = model_dict[model_choice]["model"]
-            selected_processor = model_dict[model_choice]["processor"]
-            process_layout = model_dict[model_choice]["process_layout"]
+            if model_name not in MODELS:
+                if model_name == 'dot.ocr': MODELS[model_name] = DotOcrModel(DEVICE)
+                elif model_name == 'Dolphin': MODELS[model_name] = DolphinModel(DEVICE)
+            model = MODELS[model_name]
+
+            all_results, all_markdown = [], []
+            for i, img in enumerate(PDF_CACHE["images"]):
+                gr.Info(f"Processing page {i+1}/{len(PDF_CACHE['images'])} with {model_name}...")
+                if model_name == 'dot.ocr':
+                    result = model.process_image(img, int(min_pix), int(max_pix))
+                else: # Dolphin
+                    result = model.process_image(img)
+                all_results.append(result)
+                if result.get('markdown_content'):
+                    all_markdown.append(f"### Page {i+1}\n\n{result['markdown_content']}")
+
+            PDF_CACHE.update({"results": all_results, "is_parsed": True, "model_used": model_name})
+            if not all_results: return "Processing failed.", None, None
             
-            image, page_info = load_file_for_preview(file_path)
-            if image is None:
-                return None, page_info, None
+            first_result = all_results[0]
+            combined_md = "\n\n---\n\n".join(all_markdown)
             
-            if pdf_cache["file_type"] == "pdf":
-                all_results, all_markdown = [], []
-                for i, img in enumerate(pdf_cache["images"]):
-                    result = process_image(img, selected_model, selected_processor, process_layout, int(min_pix) if min_pix else None, int(max_pix) if max_pix else None)
-                    all_results.append(result)
-                    if result.get('markdown_content'):
-                        all_markdown.append(f"## Page {i+1}\n\n{result['markdown_content']}")
-                pdf_cache["results"] = all_results
-                pdf_cache["is_parsed"] = True
-                first_result = all_results[0]
-                return first_result['processed_image'], "\n\n---\n\n".join(all_markdown), first_result['layout_result']
-            else:
-                result = process_image(image, selected_model, selected_processor, process_layout, int(min_pix) if min_pix else None, int(max_pix) if max_pix else None)
-                pdf_cache["results"] = [result]
-                pdf_cache["is_parsed"] = True
-                return result['processed_image'], result['markdown_content'] or "No content extracted", result['layout_result']
+            return combined_md, first_result.get('processed_image'), first_result.get('layout_result')
 
-        def handle_file_upload(file_path):
-            image, page_info = load_file_for_preview(file_path)
-            return image, page_info
+        def turn_page(direction):
+            global PDF_CACHE
+            if not PDF_CACHE["images"] or not PDF_CACHE["is_parsed"]:
+                return None, '<div class="page-info">No file parsed</div>', "No results yet", None, None
+
+            if direction == "prev": PDF_CACHE["current_page"] = max(0, PDF_CACHE["current_page"] - 1)
+            else: PDF_CACHE["current_page"] = min(PDF_CACHE["total_pages"] - 1, PDF_CACHE["current_page"] + 1)
+            
+            idx = PDF_CACHE["current_page"]
+            page_info_html = f'<div class="page-info">Page {idx + 1} / {PDF_CACHE["total_pages"]}</div>'
+            preview_img = PDF_CACHE["images"][idx]
+            result = PDF_CACHE["results"][idx]
+            
+            all_md = [f"### Page {i+1}\n\n{res.get('markdown_content', '')}" for i, res in enumerate(PDF_CACHE["results"])]
+            md_content = "\n\n---\n\n".join(all_md) if PDF_CACHE["total_pages"] > 1 else result.get('markdown_content', 'No content')
+            
+            return preview_img, page_info_html, md_content, result.get('processed_image'), result.get('layout_result')
 
         def clear_all():
-            global pdf_cache
-            pdf_cache = {"images": [], "current_page": 0, "total_pages": 0, "file_type": None, "is_parsed": False, "results": []}
-            return None, None, '<div class="page-info">No file loaded</div>', None, "Click 'Process Document' to see extracted content...", None
-
-        file_input.change(handle_file_upload, inputs=[file_input], outputs=[image_preview, page_info])
-        prev_page_btn.click(lambda: turn_page("prev"), outputs=[image_preview, page_info, markdown_output, processed_image, json_output])
-        next_page_btn.click(lambda: turn_page("next"), outputs=[image_preview, page_info, markdown_output, processed_image, json_output])
-        process_btn.click(process_document, inputs=[file_input, model_choice, max_new_tokens, min_pixels, max_pixels], outputs=[processed_image, markdown_output, json_output])
-        clear_btn.click(clear_all, outputs=[file_input, image_preview, page_info, processed_image, markdown_output, json_output])
-    
+            global PDF_CACHE
+            PDF_CACHE = {"images": [], "current_page": 0, "total_pages": 0, "is_parsed": False, "results": [], "model_used": None}
+            return None, None, '<div class="page-info">No file loaded</div>', "Click 'Process Document' to see extracted content...", None, None
+
+        # --- Wire UI components ---
+        file_input.change(handle_file_upload, inputs=file_input, outputs=[image_preview, page_info])
+        process_btn.click(
+            process_document,
+            inputs=[file_input, model_choice, min_pixels, max_pixels],
+            outputs=[markdown_output, processed_image_output, json_output]
+        )
+        prev_page_btn.click(lambda: turn_page("prev"), outputs=[image_preview, page_info, markdown_output, processed_image_output, json_output])
+        next_page_btn.click(lambda: turn_page("next"), outputs=[image_preview, page_info, markdown_output, processed_image_output, json_output])
+        clear_btn.click(clear_all, outputs=[file_input, image_preview, page_info, markdown_output, processed_image_output, json_output])
+        
     return demo
 
 if __name__ == "__main__":
-    demo = create_gradio_interface()
-    demo.queue(max_size=50).launch(share=False, debug=True, show_error=True)
+    # Create example directory if it doesn't exist
+    if not os.path.exists("examples"):
+        os.makedirs("examples")
+        print("Created 'examples' directory. Please add sample images/PDFs there.")
+
+    app = create_gradio_interface()
+    app.queue().launch(debug=True, show_error=True)