feat: allow dpi per page

Fix retry settings

chandra/input.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Union, Optional
 import filetype
 from PIL import Image
 import pypdfium2 as pdfium
@@ -13,17 +13,94 @@ def flatten(page, flag=pdfium_c.FLAT_NORMALDISPLAY):
         print(f"Failed to flatten annotations / form fields on page {page}.")
 
 
-def load_pdf_images(filepath: str, page_range: List[int]):
+def load_image(
+    filepath: str, min_image_dim: int = settings.MIN_IMAGE_DIM
+) -> Image.Image:
+    image = Image.open(filepath).convert("RGB")
+    if image.width < min_image_dim or image.height < min_image_dim:
+        scale = min_image_dim / min(image.width, image.height)
+        new_size = (int(image.width * scale), int(image.height * scale))
+        image = image.resize(new_size, Image.Resampling.LANCZOS)
+    return image
+
+
+def load_pdf_images(
+    filepath: str,
+    page_range: List[int],
+    image_dpi: Optional[Union[int, List[int]]] = None,
+    min_pdf_image_dim: Optional[Union[int, List[int]]] = None,
+) -> List[Image.Image]:
+    """
+    Load PDF pages as images with configurable DPI.
+
+    Args:
+        filepath: Path to PDF file
+        page_range: List of page indices to render
+        image_dpi: Target DPI for rendering. Can be:
+            - None: use settings.IMAGE_DPI for all pages (default)
+            - int: use same DPI for all pages
+            - List[int]: per-page DPI (must match length of page_range)
+        min_pdf_image_dim: Minimum image dimension. Can be:
+            - None: use settings.MIN_PDF_IMAGE_DIM for all pages (default)
+            - int: use same value for all pages
+            - List[int]: per-page value (must match length of page_range)
+
+    Returns:
+        List of PIL Images, one per page in page_range
+    """
     doc = pdfium.PdfDocument(filepath)
     doc.init_forms()
 
+    # Determine default values
+    default_image_dpi = image_dpi if isinstance(image_dpi, int) else settings.IMAGE_DPI
+    default_min_pdf_image_dim = min_pdf_image_dim if isinstance(min_pdf_image_dim, int) else settings.MIN_PDF_IMAGE_DIM
+
+    # Handle per-page DPI lists
+    is_per_page_dpi = isinstance(image_dpi, list)
+    if not is_per_page_dpi and image_dpi is not None:
+        # Convert single DPI value to list for all pages
+        image_dpi = [image_dpi] * len(page_range)
+        is_per_page_dpi = True
+
+    is_per_page_min_dim = isinstance(min_pdf_image_dim, list)
+    if not is_per_page_min_dim and min_pdf_image_dim is not None:
+        # Convert single min_dim value to list for all pages
+        min_pdf_image_dim = [min_pdf_image_dim] * len(page_range)
+        is_per_page_min_dim = True
+
+    if is_per_page_dpi and len(image_dpi) != len(page_range):
+        raise ValueError(f"image_dpi list length ({len(image_dpi)}) must match page_range length ({len(page_range)})")
+    if is_per_page_min_dim and len(min_pdf_image_dim) != len(page_range):
+        raise ValueError(f"min_pdf_image_dim list length ({len(min_pdf_image_dim)}) must match page_range length ({len(page_range)})")
+
     images = []
+    page_idx_in_range = 0
+
     for page in range(len(doc)):
         if not page_range or page in page_range:
+            # Get DPI for this specific page
+            if is_per_page_dpi:
+                current_dpi = image_dpi[page_idx_in_range]
+            elif image_dpi is None:
+                current_dpi = settings.IMAGE_DPI
+            else:
+                current_dpi = default_image_dpi
+
+            # Get min_dim for this specific page
+            if is_per_page_min_dim:
+                current_min_dim = min_pdf_image_dim[page_idx_in_range]
+            elif min_pdf_image_dim is None:
+                current_min_dim = settings.MIN_PDF_IMAGE_DIM
+            else:
+                current_min_dim = default_min_pdf_image_dim
+
+            page_idx_in_range += 1
+
             page_obj = doc[page]
             min_page_dim = min(page_obj.get_width(), page_obj.get_height())
-            scale_dpi = (settings.MIN_IMAGE_DIM / min_page_dim) * 72
-            scale_dpi = max(scale_dpi, settings.IMAGE_DPI)
+
+            scale_dpi = (current_min_dim / min_page_dim) * 72
+            scale_dpi = max(scale_dpi, current_dpi)
             page_obj = doc[page]
             flatten(page_obj)
             page_obj = doc[page]
@@ -56,5 +133,5 @@ def load_file(filepath: str, config: dict):
     if input_type and input_type.extension == "pdf":
         images = load_pdf_images(filepath, page_range)
     else:
-        images = [Image.open(filepath).convert("RGB")]
+        images = [load_image(filepath)]
     return images

chandra/model/__init__.py

@@ -4,6 +4,7 @@ from chandra.model.hf import load_model, generate_hf
 from chandra.model.schema import BatchInputItem, BatchOutputItem
 from chandra.model.vllm import generate_vllm
 from chandra.output import parse_markdown, parse_html, parse_chunks, extract_images
+from chandra.settings import settings
 
 
 class InferenceManager:
@@ -26,19 +27,29 @@ class InferenceManager:
             output_kwargs["include_headers_footers"] = kwargs.pop(
                 "include_headers_footers"
             )
+        bbox_scale = kwargs.pop("bbox_scale", settings.BBOX_SCALE)
+        vllm_api_base = kwargs.pop("vllm_api_base", settings.VLLM_API_BASE)
 
         if self.method == "vllm":
             results = generate_vllm(
-                batch, max_output_tokens=max_output_tokens, **kwargs
+                batch,
+                max_output_tokens=max_output_tokens,
+                bbox_scale=bbox_scale,
+                vllm_api_base=vllm_api_base,
+                **kwargs,
             )
         else:
             results = generate_hf(
-                batch, self.model, max_output_tokens=max_output_tokens, **kwargs
+                batch,
+                self.model,
+                max_output_tokens=max_output_tokens,
+                bbox_scale=bbox_scale,
+                **kwargs,
             )
 
         output = []
         for result, input_item in zip(results, batch):
-            chunks = parse_chunks(result.raw, input_item.image)
+            chunks = parse_chunks(result.raw, input_item.image, bbox_scale=bbox_scale)
             output.append(
                 BatchOutputItem(
                     markdown=parse_markdown(result.raw, **output_kwargs),
@@ -48,6 +59,7 @@ class InferenceManager:
                     page_box=[0, 0, input_item.image.width, input_item.image.height],
                     token_count=result.token_count,
                     images=extract_images(result.raw, chunks, input_item.image),
+                    error=result.error,
                 )
             )
         return output

chandra/model/hf.py

@@ -1,8 +1,5 @@
 from typing import List
 
-from qwen_vl_utils import process_vision_info
-from transformers import Qwen3VLForConditionalGeneration, Qwen3VLProcessor
-
 from chandra.model.schema import BatchInputItem, GenerationResult
 from chandra.model.util import scale_to_fit
 from chandra.prompts import PROMPT_MAPPING
@@ -10,12 +7,20 @@ from chandra.settings import settings
 
 
 def generate_hf(
-    batch: List[BatchInputItem], model, max_output_tokens=None, **kwargs
+    batch: List[BatchInputItem],
+    model,
+    max_output_tokens=None,
+    bbox_scale: int = settings.BBOX_SCALE,
+    **kwargs,
 ) -> List[GenerationResult]:
+    from qwen_vl_utils import process_vision_info
+
     if max_output_tokens is None:
         max_output_tokens = settings.MAX_OUTPUT_TOKENS
 
-    messages = [process_batch_element(item, model.processor) for item in batch]
+    messages = [
+        process_batch_element(item, model.processor, bbox_scale) for item in batch
+    ]
     text = model.processor.apply_chat_template(
         messages, tokenize=False, add_generation_prompt=True
     )
@@ -48,12 +53,12 @@ def generate_hf(
     return results
 
 
-def process_batch_element(item: BatchInputItem, processor):
+def process_batch_element(item: BatchInputItem, processor, bbox_scale: int):
     prompt = item.prompt
     prompt_type = item.prompt_type
 
     if not prompt:
-        prompt = PROMPT_MAPPING[prompt_type]
+        prompt = PROMPT_MAPPING[prompt_type].replace("{bbox_scale}", str(bbox_scale))
 
     content = []
     image = scale_to_fit(item.image)  # Guarantee max size
@@ -65,12 +70,15 @@ def process_batch_element(item: BatchInputItem, processor):
 
 
 def load_model():
+    import torch
+    from transformers import Qwen3VLForConditionalGeneration, Qwen3VLProcessor
+
     device_map = "auto"
     if settings.TORCH_DEVICE:
         device_map = {"": settings.TORCH_DEVICE}
 
     kwargs = {
-        "dtype": settings.TORCH_DTYPE,
+        "dtype": torch.bfloat16,
         "device_map": device_map,
     }
     if settings.TORCH_ATTN:

chandra/model/schema.py

@@ -27,3 +27,4 @@ class BatchOutputItem:
     page_box: List[int]
     token_count: int
     images: dict
+    error: bool

chandra/model/util.py

@@ -44,9 +44,10 @@ def scale_to_fit(
 
 def detect_repeat_token(
     predicted_tokens: str,
-    max_repeats: int = 4,
+    base_max_repeats: int = 4,
     window_size: int = 500,
     cut_from_end: int = 0,
+    scaling_factor: float = 3.0,
 ):
     try:
         predicted_tokens = parse_markdown(predicted_tokens)
@@ -57,11 +58,13 @@ def detect_repeat_token(
     if cut_from_end > 0:
         predicted_tokens = predicted_tokens[:-cut_from_end]
 
-    # Try different sequence lengths (1 to window_size//2)
     for seq_len in range(1, window_size // 2 + 1):
         # Extract the potential repeating sequence from the end
         candidate_seq = predicted_tokens[-seq_len:]
 
+        # Inverse scaling: shorter sequences need more repeats
+        max_repeats = int(base_max_repeats * (1 + scaling_factor / seq_len))
+
         # Count how many times this sequence appears consecutively at the end
         repeat_count = 0
         pos = len(predicted_tokens) - seq_len
@@ -75,7 +78,6 @@ def detect_repeat_token(
             else:
                 break
 
-        # If we found more than max_repeats consecutive occurrences
         if repeat_count > max_repeats:
             return True
 

chandra/model/vllm.py

@@ -1,5 +1,6 @@
 import base64
 import io
+import time
 from concurrent.futures import ThreadPoolExecutor
 from itertools import repeat
 from typing import List
@@ -25,10 +26,15 @@ def generate_vllm(
     max_output_tokens: int = None,
     max_retries: int = None,
     max_workers: int | None = None,
+    custom_headers: dict | None = None,
+    max_failure_retries: int | None = None,
+    bbox_scale: int = settings.BBOX_SCALE,
+    vllm_api_base: str = settings.VLLM_API_BASE,
 ) -> List[GenerationResult]:
     client = OpenAI(
         api_key=settings.VLLM_API_KEY,
-        base_url=settings.VLLM_API_BASE,
+        base_url=vllm_api_base,
+        default_headers=custom_headers,
     )
     model_name = settings.VLLM_MODEL_NAME
 
@@ -50,7 +56,9 @@ def generate_vllm(
     ) -> GenerationResult:
         prompt = item.prompt
         if not prompt:
-            prompt = PROMPT_MAPPING[item.prompt_type]
+            prompt = PROMPT_MAPPING[item.prompt_type].replace(
+                "{bbox_scale}", str(bbox_scale)
+            )
 
         content = []
         image = scale_to_fit(item.image)
@@ -68,41 +76,68 @@ def generate_vllm(
             completion = client.chat.completions.create(
                 model=model_name,
                 messages=[{"role": "user", "content": content}],
-                max_tokens=settings.MAX_OUTPUT_TOKENS,
+                max_tokens=max_output_tokens,
                 temperature=temperature,
                 top_p=top_p,
             )
+            raw = completion.choices[0].message.content
+            result = GenerationResult(
+                raw=raw,
+                token_count=completion.usage.completion_tokens,
+                error=False,
+            )
         except Exception as e:
             print(f"Error during VLLM generation: {e}")
             return GenerationResult(raw="", token_count=0, error=True)
 
-        return GenerationResult(
-            raw=completion.choices[0].message.content,
-            token_count=completion.usage.completion_tokens,
-            error=False,
-        )
+        return result
 
-    def process_item(item, max_retries):
+    def process_item(item, max_retries, max_failure_retries=None):
         result = _generate(item)
         retries = 0
 
-        while retries < max_retries and (
-            detect_repeat_token(result.raw)
-            or (
-                len(result.raw) > 50
-                and detect_repeat_token(result.raw, cut_from_end=50)
-            )
-            or result.error
-        ):
-            print(
-                f"Detected repeat token or error, retrying generation (attempt {retries + 1})..."
-            )
+        while _should_retry(result, retries, max_retries, max_failure_retries):
             result = _generate(item, temperature=0.3, top_p=0.95)
             retries += 1
 
         return result
 
+    def _should_retry(result, retries, max_retries, max_failure_retries):
+        has_repeat = detect_repeat_token(result.raw) or (
+            len(result.raw) > 50 and detect_repeat_token(result.raw, cut_from_end=50)
+        )
+
+        if retries < max_retries and has_repeat:
+            print(
+                f"Detected repeat token, retrying generation (attempt {retries + 1})..."
+            )
+            return True
+
+        if retries < max_retries and result.error:
+            print(
+                f"Detected vllm error, retrying generation (attempt {retries + 1})..."
+            )
+            time.sleep(2 * (retries + 1))  # Sleeping can help under load
+            return True
+
+        if (
+            result.error
+            and max_failure_retries is not None
+            and retries < max_failure_retries
+        ):
+            print(
+                f"Detected vllm error, retrying generation (attempt {retries + 1})..."
+            )
+            time.sleep(2 * (retries + 1))  # Sleeping can help under load
+            return True
+
+        return False
+
     with ThreadPoolExecutor(max_workers=max_workers) as executor:
-        results = list(executor.map(process_item, batch, repeat(max_retries)))
+        results = list(
+            executor.map(
+                process_item, batch, repeat(max_retries), repeat(max_failure_retries)
+            )
+        )
 
     return results

chandra/output.py

@@ -6,9 +6,11 @@ from functools import lru_cache
 
 import six
 from PIL import Image
-from bs4 import BeautifulSoup, NavigableString
+from bs4 import BeautifulSoup
 from markdownify import MarkdownConverter, re_whitespace
 
+from chandra.settings import settings
+
 
 @lru_cache
 def _hash_html(html: str):
@@ -30,7 +32,11 @@ def extract_images(html: str, chunks: dict, image: Image.Image):
             if not img:
                 continue
             bbox = chunk["bbox"]
-            block_image = image.crop(bbox)
+            try:
+                block_image = image.crop(bbox)
+            except ValueError:
+                # Happens when bbox coordinates are invalid
+                continue
             img_name = get_image_name(html, div_idx)
             images[img_name] = block_image
     return images
@@ -67,44 +73,22 @@ def parse_html(
             else:
                 img = BeautifulSoup(f"<img src='{img_src}'/>", "html.parser")
                 div.append(img)
+
+        # Wrap text content in <p> tags if no inner HTML tags exist
+        if label in ["Text"] and not re.search(
+            "<.+>", str(div.decode_contents()).strip()
+        ):
+            # Add inner p tags if missing for text blocks
+            text_content = str(div.decode_contents()).strip()
+            text_content = f"<p>{text_content}</p>"
+            div.clear()
+            div.append(BeautifulSoup(text_content, "html.parser"))
+
         content = str(div.decode_contents())
         out_html += content
     return out_html
 
 
-def escape_dollars(text):
-    return text.replace("$", r"\$")
-
-
-def get_formatted_table_text(element):
-    text = []
-    for content in element.contents:
-        if content is None:
-            continue
-
-        if isinstance(content, NavigableString):
-            stripped = content.strip()
-            if stripped:
-                text.append(escape_dollars(stripped))
-        elif content.name == "br":
-            text.append("<br>")
-        elif content.name == "math":
-            text.append("$" + content.text + "$")
-        else:
-            content_str = escape_dollars(str(content))
-            text.append(content_str)
-
-    full_text = ""
-    for i, t in enumerate(text):
-        if t == "<br>":
-            full_text += t
-        elif i > 0 and text[i - 1] != "<br>":
-            full_text += " " + t
-        else:
-            full_text += t
-    return full_text
-
-
 class Markdownify(MarkdownConverter):
     def __init__(
         self,
@@ -204,19 +188,25 @@ class LayoutBlock:
     content: str
 
 
-def parse_layout(html: str, image: Image.Image):
+def parse_layout(html: str, image: Image.Image, bbox_scale=settings.BBOX_SCALE):
     soup = BeautifulSoup(html, "html.parser")
     top_level_divs = soup.find_all("div", recursive=False)
     width, height = image.size
-    width_scaler = width / 1024
-    height_scaler = height / 1024
+    width_scaler = width / bbox_scale
+    height_scaler = height / bbox_scale
     layout_blocks = []
     for div in top_level_divs:
         bbox = div.get("data-bbox")
+
         try:
             bbox = json.loads(bbox)
+            assert len(bbox) == 4, "Invalid bbox length"
         except Exception:
-            bbox = [0, 0, 1, 1]  # Fallback to a default bbox if parsing fails
+            try:
+                bbox = bbox.split(" ")
+                assert len(bbox) == 4, "Invalid bbox length"
+            except Exception:
+                bbox = [0, 0, 1, 1]
 
         bbox = list(map(int, bbox))
         # Normalize bbox
@@ -232,7 +222,7 @@ def parse_layout(html: str, image: Image.Image):
     return layout_blocks
 
 
-def parse_chunks(html: str, image: Image.Image):
-    layout = parse_layout(html, image)
+def parse_chunks(html: str, image: Image.Image, bbox_scale=settings.BBOX_SCALE):
+    layout = parse_layout(html, image, bbox_scale=bbox_scale)
     chunks = [asdict(block) for block in layout]
     return chunks

chandra/prompts.py

@@ -65,7 +65,7 @@ Guidelines:
 """.strip()
 
 OCR_LAYOUT_PROMPT = f"""
-OCR this image to HTML, arranged as layout blocks.  Each layout block should be a div with the data-bbox attribute representing the bounding box of the block in [x0, y0, x1, y1] format.  Bboxes are normalized 0-1024. The data-label attribute is the label for the block.
+OCR this image to HTML, arranged as layout blocks.  Each layout block should be a div with the data-bbox attribute representing the bounding box of the block in [x0, y0, x1, y1] format.  Bboxes are normalized 0-{{bbox_scale}}. The data-label attribute is the label for the block.
 
 Use the following labels:
 - Caption

chandra/scripts/screenshot_app.py

@@ -143,6 +143,7 @@ def process():
                 "image_height": img_height,
                 "blocks": blocks_data,
                 "html": html_with_images,
+                "markdown": result.markdown,
             }
         )
 

chandra/scripts/templates/screenshot.html

@@ -64,6 +64,20 @@
             cursor: not-allowed;
         }
 
+        .controls label {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            color: white;
+            font-size: 14px;
+            cursor: pointer;
+            user-select: none;
+        }
+
+        .controls input[type="checkbox"] {
+            cursor: pointer;
+        }
+
         .loading {
             display: none;
             color: #f39c12;
@@ -75,6 +89,11 @@
             font-weight: bold;
         }
 
+        .success {
+            color: #27ae60;
+            font-weight: bold;
+        }
+
         .screenshot-container {
             display: none;
             margin-top: 60px;
@@ -88,8 +107,18 @@
             display: flex;
         }
 
-        .left-panel, .right-panel {
-            flex: 1;
+        .left-panel {
+            flex: 0 0 40%;
+            display: flex;
+            flex-direction: column;
+            background: white;
+            border-radius: 8px;
+            overflow: hidden;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.3);
+        }
+
+        .right-panel {
+            flex: 0 0 60%;
             display: flex;
             flex-direction: column;
             background: white;
@@ -137,6 +166,7 @@
             padding: 30px;
             line-height: 1.6;
             color: #333;
+            font-size: 24px;
         }
 
         .markdown-content h1, .markdown-content h2, .markdown-content h3 {
@@ -215,8 +245,14 @@
         <input type="text" id="filePath" placeholder="Enter file path (e.g., /path/to/document.pdf)">
         <input type="number" id="pageNumber" placeholder="Page" value="0" min="0">
         <button id="processBtn" onclick="processFile()">Process</button>
+        <label>
+            <input type="checkbox" id="showLayoutBoxes" checked onchange="toggleLayoutBoxes()">
+            Show Layout Boxes
+        </label>
+        <button id="copyMarkdownBtn" onclick="copyMarkdown()" style="display: none;">Copy Markdown</button>
         <span class="loading" id="loading">Processing...</span>
         <span class="error" id="error"></span>
+        <span class="success" id="success"></span>
     </div>
 
     <div class="screenshot-container" id="container">
@@ -242,6 +278,11 @@
     <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.8.1/github-markdown.min.css" integrity="sha512-BrOPA520KmDMqieeM7XFe6a3u3Sb3F1JBaQnrIAmWg3EYrciJ+Qqe6ZcKCdfPv26rGcgTrJnZ/IdQEct8h3Zhw==" crossorigin="anonymous" referrerpolicy="no-referrer" />
     <script>
+        // Global state to store markdown and canvas data
+        let currentMarkdown = null;
+        let currentData = null;
+        let currentImageSrc = null;
+
         async function processFile() {
             const filePath = document.getElementById('filePath').value;
             const pageNumber = parseInt(document.getElementById('pageNumber').value) || 0;
@@ -285,6 +326,10 @@
         }
 
         function renderResults(data) {
+            // Store data for toggle functionality
+            currentData = data;
+            currentImageSrc = data.image_base64;
+
             const canvas = document.getElementById('layoutCanvas');
             const ctx = canvas.getContext('2d');
             const markdownContent = document.getElementById('markdownContent');
@@ -292,51 +337,14 @@
             // Draw image with layout overlays
             const img = new Image();
             img.onload = function() {
-                canvas.width = data.image_width;
-                canvas.height = data.image_height;
-
-                // Draw image
-                ctx.drawImage(img, 0, 0, data.image_width, data.image_height);
-
-                // Draw layout blocks
-                ctx.lineWidth = 3;
-                ctx.font = 'bold 14px -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif';
-
-                const labelCounts = {};
-                data.blocks.forEach((block) => {
-                    const [x1, y1, x2, y2] = block.bbox;
-                    const width = x2 - x1;
-                    const height = y2 - y1;
-
-                    // Draw rectangle with semi-transparent fill
-                    ctx.strokeStyle = block.color;
-                    ctx.fillStyle = block.color + '33';
-                    ctx.fillRect(x1, y1, width, height);
-                    ctx.strokeRect(x1, y1, width, height);
-
-                    // Count labels for unique identification
-                    labelCounts[block.label] = (labelCounts[block.label] || 0) + 1;
-                    const labelWithCount = `${block.label} #${labelCounts[block.label]}`;
-
-                    // Draw label with background
-                    const textMetrics = ctx.measureText(labelWithCount);
-                    const textWidth = textMetrics.width;
-                    const textHeight = 16;
-                    const padding = 6;
-
-                    const labelX = x1;
-                    const labelY = Math.max(y1 - textHeight - padding, textHeight);
-
-                    ctx.fillStyle = block.color;
-                    ctx.fillRect(labelX, labelY - textHeight, textWidth + padding * 2, textHeight + padding);
-
-                    ctx.fillStyle = 'white';
-                    ctx.textBaseline = 'top';
-                    ctx.fillText(labelWithCount, labelX + padding, labelY - textHeight + padding/2);
-                });
+                drawCanvas(img, data, ctx);
             };
             img.src = data.image_base64;
 
+            // Store markdown and show copy button
+            currentMarkdown = data.markdown;
+            document.getElementById('copyMarkdownBtn').style.display = 'inline-block';
+
             // Render HTML directly (with images embedded)
             markdownContent.innerHTML = data.html;
 
@@ -362,6 +370,85 @@
             });
         }
 
+        function drawCanvas(img, data, ctx) {
+            const canvas = document.getElementById('layoutCanvas');
+            canvas.width = data.image_width;
+            canvas.height = data.image_height;
+
+            // Draw image
+            ctx.drawImage(img, 0, 0, data.image_width, data.image_height);
+
+            // Check if layout boxes should be shown
+            const showBoxes = document.getElementById('showLayoutBoxes').checked;
+            if (!showBoxes) return;
+
+            // Draw layout blocks
+            ctx.lineWidth = 3;
+            ctx.font = 'bold 14px -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif';
+
+            const labelCounts = {};
+            data.blocks.forEach((block) => {
+                const [x1, y1, x2, y2] = block.bbox;
+                const width = x2 - x1;
+                const height = y2 - y1;
+
+                // Draw rectangle with semi-transparent fill
+                ctx.strokeStyle = block.color;
+                ctx.fillStyle = block.color + '33';
+                ctx.fillRect(x1, y1, width, height);
+                ctx.strokeRect(x1, y1, width, height);
+
+                // Count labels for unique identification
+                labelCounts[block.label] = (labelCounts[block.label] || 0) + 1;
+                const labelWithCount = `${block.label} #${labelCounts[block.label]}`;
+
+                // Draw label with background
+                const textMetrics = ctx.measureText(labelWithCount);
+                const textWidth = textMetrics.width;
+                const textHeight = 16;
+                const padding = 6;
+
+                const labelX = x1;
+                const labelY = Math.max(y1 - textHeight - padding, textHeight);
+
+                ctx.fillStyle = block.color;
+                ctx.fillRect(labelX, labelY - textHeight, textWidth + padding * 2, textHeight + padding);
+
+                ctx.fillStyle = 'white';
+                ctx.textBaseline = 'top';
+                ctx.fillText(labelWithCount, labelX + padding, labelY - textHeight + padding/2);
+            });
+        }
+
+        function toggleLayoutBoxes() {
+            if (!currentData || !currentImageSrc) return;
+
+            const canvas = document.getElementById('layoutCanvas');
+            const ctx = canvas.getContext('2d');
+            const img = new Image();
+            img.onload = function() {
+                drawCanvas(img, currentData, ctx);
+            };
+            img.src = currentImageSrc;
+        }
+
+        function copyMarkdown() {
+            if (!currentMarkdown) {
+                document.getElementById('error').textContent = 'No markdown to copy';
+                return;
+            }
+
+            navigator.clipboard.writeText(currentMarkdown).then(() => {
+                const success = document.getElementById('success');
+                success.textContent = 'Markdown copied!';
+                setTimeout(() => {
+                    success.textContent = '';
+                }, 2000);
+            }).catch((err) => {
+                document.getElementById('error').textContent = 'Failed to copy: ' + err.message;
+            });
+        }
+
         // Allow Enter key to trigger processing
         document.getElementById('filePath').addEventListener('keypress', function(e) {
             if (e.key === 'Enter') processFile();

chandra/settings.py

@@ -1,7 +1,5 @@
 from dotenv import find_dotenv
-from pydantic import computed_field
 from pydantic_settings import BaseSettings
-import torch
 import os
 
 
@@ -9,11 +7,13 @@ class Settings(BaseSettings):
     # Paths
     BASE_DIR: str = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
     IMAGE_DPI: int = 192
-    MIN_IMAGE_DIM: int = 1024
+    MIN_PDF_IMAGE_DIM: int = 1024
+    MIN_IMAGE_DIM: int = 1536
     MODEL_CHECKPOINT: str = "datalab-to/chandra"
     TORCH_DEVICE: str | None = None
-    MAX_OUTPUT_TOKENS: int = 8192
+    MAX_OUTPUT_TOKENS: int = 12384
     TORCH_ATTN: str | None = None
+    BBOX_SCALE: int = 1024
 
     # vLLM server settings
     VLLM_API_KEY: str = "EMPTY"
@@ -22,11 +22,6 @@ class Settings(BaseSettings):
     VLLM_GPUS: str = "0"
     MAX_VLLM_RETRIES: int = 6
 
-    @computed_field
-    @property
-    def TORCH_DTYPE(self) -> torch.dtype:
-        return torch.bfloat16
-
     class Config:
         env_file = find_dotenv("local.env")
         extra = "ignore"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "chandra-ocr"
-version = "0.1.7"
+version = "0.1.9"
 description = "OCR model that converts documents to markdown, HTML, or JSON."
 readme = "README.md"
 requires-python = ">=3.10"