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Inference fixes

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This commit is contained in:
Vik Paruchuri
2025-10-16 12:05:38 -04:00
parent a9ffa789c6
commit aa59df2996
11 changed files with 771 additions and 111 deletions
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68
README.md
View File

@@ -1,9 +1,71 @@
# Chandra
Try:
Chandra is a highly accurate OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.
```shell
## Features
- Convert documents to markdown, html, or json with detailed layout information
- Math equation support (LaTeX)
- Reconstructs forms, including checkboxes
- Precise table reconstruction
- Support for 40+ languages
- Two inference modes: local (HuggingFace) and remote (vLLM server)
## Installation
```bash
uv sync
source .venv/bin/activate
```
## Usage
### Streamlit Web App
Launch the interactive demo:
```bash
streamlit run chandra_app.py --server.fileWatcherType none --server.headless true
```
```
**Inference Modes:**
- **hf**: Loads model locally using HuggingFace Transformers (requires GPU)
- **vllm**: Connects to a running vLLM server for optimized batch inference
### vLLM Server (Optional)
For production deployments or batch processing, use the vLLM server:
```bash
python scripts/start_vllm.py
```
This launches a Docker container with optimized inference settings. Configure via environment variables:
- `VLLM_API_BASE`: Server URL (default: `http://localhost:8000/v1`)
- `VLLM_MODEL_NAME`: Model name for the server (default: `chandra`)
- `VLLM_GPUS`: GPU device IDs (default: `0`)
- `HF_TOKEN`: HuggingFace token for model access
### Configuration
Settings can be configured via environment variables or a `local.env` file:
```bash
# Model settings
MODEL_CHECKPOINT=datalab-to/chandra-0.2.8
MAX_OUTPUT_TOKENS=8192
# vLLM settings
VLLM_API_BASE=http://localhost:8000/v1
VLLM_MODEL_NAME=chandra
VLLM_GPUS=0
```
## Output Formats
Chandra provides three output formats:
1. **HTML**: Structured HTML with layout blocks and bounding boxes
2. **Markdown**: Clean, readable Markdown conversion
3. **Layout Image**: Visual representation of detected layout blocks

11
chandra/input.py
View File

@@ -8,7 +8,16 @@ from chandra.settings import settings
def load_pdf_images(filepath: str, page_range: List[int]):
doc = pdfium.PdfDocument(filepath)
images = [doc[i].render(scale=settings.IMAGE_DPI / 72).to_pil().convert("RGB") for i in range(len(doc)) if not page_range or i in page_range]
images = []
for page in range(len(doc)):
if not page_range or page in page_range:
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)
pil_image = doc[page].render(scale=scale_dpi / 72).to_pil().convert("RGB")
images.append(pil_image)
doc.close()
return images

1
chandra/model/__init__.py
View File

@@ -30,6 +30,7 @@ class InferenceManager:
html=parse_html(result),
chunks=parse_chunks(result, input_item.image),
raw=result,
page_box=[0, 0, input_item.image.width, input_item.image.height]
)
)
return output

1
chandra/model/schema.py
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@@ -16,3 +16,4 @@ class BatchOutputItem:
html: str
chunks: dict
raw: str
page_box: List[int]

31
chandra/model/util.py
View File

@@ -3,6 +3,8 @@ from typing import Tuple
from PIL import Image
from chandra.output import parse_markdown
def scale_to_fit(
img: Image.Image,
@@ -41,29 +43,30 @@ def scale_to_fit(
def detect_repeat_token(
predicted_tokens: str, max_repeats: int = 4, window_size: int = 50
predicted_tokens: str, max_repeats: int = 4, window_size: int = 500, cut_from_end: int = 0
):
if len(predicted_tokens) < window_size:
return False
try:
predicted_tokens = parse_markdown(predicted_tokens)
except Exception as e:
print(f"Error parsing markdown: {e}")
return True
# Look at the last window_size tokens
recent_tokens = predicted_tokens[-window_size:].lower()
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):
# Skip if we can't fit enough repetitions
if seq_len * (max_repeats + 1) > window_size:
continue
# Extract the potential repeating sequence from the end
candidate_seq = recent_tokens[-seq_len:]
candidate_seq = predicted_tokens[-seq_len:]
# Count how many times this sequence appears consecutively at the end
repeat_count = 0
pos = len(recent_tokens) - seq_len
pos = len(predicted_tokens) - seq_len
if pos < 0:
continue
while pos >= 0:
if recent_tokens[pos : pos + seq_len] == candidate_seq:
if predicted_tokens[pos : pos + seq_len] == candidate_seq:
repeat_count += 1
pos -= seq_len
else:
@@ -74,3 +77,7 @@ def detect_repeat_token(
return True
return False
def layout_failed(predicted_tokens: str, image: Image.Image):
pass

19
chandra/model/vllm.py
View File

@@ -1,6 +1,7 @@
import base64
import io
from concurrent.futures import ThreadPoolExecutor
from itertools import repeat
from typing import List
from PIL import Image
@@ -19,13 +20,19 @@ def image_to_base64(image: Image.Image) -> str:
return base64.b64encode(buffered.getvalue()).decode()
def generate_vllm(batch: List[BatchInputItem], max_retries: int = 5):
def generate_vllm(batch: List[BatchInputItem], max_retries: int = None, max_workers: int | None = None):
client = OpenAI(
api_key=settings.VLLM_API_KEY,
base_url=settings.VLLM_API_BASE,
)
model_name = settings.VLLM_MODEL_NAME
if max_retries is None:
max_retries = settings.MAX_VLLM_RETRIES
if max_workers is None:
max_workers = min(64, len(batch))
if model_name is None:
models = client.models.list()
model_name = models.data[0].id
@@ -62,19 +69,19 @@ def generate_vllm(batch: List[BatchInputItem], max_retries: int = 5):
)
return completion.choices[0].message.content
def process_item(item, max_retries=3):
def process_item(item, max_retries):
result = _generate(item)
retries = 0
while retries < max_retries and (detect_repeat_token(result) or
(len(result) > 50 and detect_repeat_token(result[:-50]))):
(len(result) > 50 and detect_repeat_token(result, cut_from_end=50))):
print(f"Detected repeat token, retrying generation (attempt {retries + 1})...")
result = _generate(item, temperature=0.2, top_p=0.9)
result = _generate(item, temperature=0.3, top_p=0.95)
retries += 1
return result
with ThreadPoolExecutor(max_workers=len(batch)) as executor:
results = list(executor.map(process_item, batch))
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(process_item, batch, repeat(max_retries)))
return results

6
chandra/output.py
View File

@@ -159,7 +159,11 @@ def parse_layout(html: str, image: Image.Image):
layout_blocks = []
for div in top_level_divs:
bbox = div.get("data-bbox")
bbox = json.loads(bbox)
try:
bbox = json.loads(bbox)
except Exception as e:
bbox = [0, 0, 1, 1] # Fallback to a default bbox if parsing fails
bbox = list(map(int, bbox))
# Normalize bbox
bbox = [

5
chandra/settings.py
View File

@@ -8,17 +8,18 @@ import os
class Settings(BaseSettings):
# Paths
BASE_DIR: str = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
IMAGE_DPI: int = 96
IMAGE_DPI: int = 192
MIN_IMAGE_DIM: int = 1024
MODEL_CHECKPOINT: str = "datalab-to/chandra-0.2.8"
TORCH_DEVICE: str | None = None
MAX_OUTPUT_TOKENS: int = 8192
# vLLM server settings
USE_VLLM: bool = False
VLLM_API_KEY: str = "EMPTY"
VLLM_API_BASE: str = "http://localhost:8000/v1"
VLLM_MODEL_NAME: str = "chandra"
VLLM_GPUS: str = "0"
MAX_VLLM_RETRIES: int = 6
# Transformers settings
@computed_field

11
chandra_app.py
View File

@@ -38,7 +38,7 @@ def ocr_layout(
result = model.generate([batch])[0]
layout = parse_layout(result.raw, img)
layout_image = draw_layout(img, layout)
return result.html, layout_image, result.markdown
return result, layout_image
st.set_page_config(layout="wide")
@@ -94,7 +94,7 @@ if run_ocr:
if model_mode == "None":
st.error("Please select a model mode (hf or vllm) to run OCR.")
else:
pred, layout_image, markdown = ocr_layout(
result, layout_image = ocr_layout(
pil_image,
model,
)
@@ -102,19 +102,20 @@ if run_ocr:
with col1:
html_tab, text_tab, layout_tab = st.tabs(["HTML", "HTML as text", "Layout Image"])
with html_tab:
st.markdown(markdown, unsafe_allow_html=True)
st.markdown(result.markdown, unsafe_allow_html=True)
st.download_button(
label="Download Markdown",
data=markdown,
data=result.markdown,
file_name=f"{in_file.name.rsplit('.', 1)[0]}_page{page_number if page_number is not None else 0}.md",
mime="text/markdown",
)
with text_tab:
st.text(pred)
st.text(result.html)
if layout_image:
with layout_tab:
st.image(layout_image, caption="Detected Layout", use_container_width=True)
st.text_area(result.raw)
with col2:
st.image(pil_image, caption="Uploaded Image", use_container_width=True)

6
pyproject.toml
View File

@@ -9,15 +9,17 @@ dependencies = [
"filetype>=1.2.0",
"markdownify==1.1.0",
"openai>=2.2.0",
"pillow>=11.3.0",
"pillow>=10.2.0",
"pydantic>=2.12.0",
"pydantic-settings>=2.11.0",
"pypdfium2>=4.30.0",
"python-dotenv>=1.1.1",
"qwen-vl-utils>=0.0.14",
"streamlit>=1.50.0",
"surya-ocr>=0.17.0",
"torch>=2.8.0",
"transformers>=4.57.0",
"torchvision>=0.23.0",
"transformers>=4.57.1",
]
[tool.setuptools.packages.find]

723
uv.lock generated
View File

@@ -1,5 +1,16 @@
version = 1
requires-python = ">=3.12"
requires-python = ">=3.10"
resolution-markers = [
"python_full_version >= '3.12' and sys_platform == 'darwin'",
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
"python_full_version == '3.11.*' and sys_platform == 'darwin'",
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
"python_full_version < '3.11' and sys_platform == 'darwin'",
"python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
]
[[package]]
name = "altair"
@@ -31,6 +42,7 @@ name = "anyio"
version = "4.11.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
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{ name = "idna" },
{ name = "sniffio" },
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
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[[package]]
name = "chandra"
version = "0.1.0"
@@ -141,7 +176,9 @@ dependencies = [
{ name = "python-dotenv" },
{ name = "qwen-vl-utils" },
{ name = "streamlit" },
{ name = "surya-ocr" },
{ name = "torch" },
{ name = "torchvision" },
{ name = "transformers" },
]
@@ -151,15 +188,17 @@ requires-dist = [
{ name = "filetype", specifier = ">=1.2.0" },
{ name = "markdownify", specifier = "==1.1.0" },
{ name = "openai", specifier = ">=2.2.0" },
{ name = "pillow", specifier = ">=11.3.0" },
{ name = "pillow", specifier = ">=10.2.0" },
{ name = "pydantic", specifier = ">=2.12.0" },
{ name = "pydantic-settings", specifier = ">=2.11.0" },
{ name = "pypdfium2", specifier = ">=4.30.0" },
{ name = "python-dotenv", specifier = ">=1.1.1" },
{ name = "qwen-vl-utils", specifier = ">=0.0.14" },
{ name = "streamlit", specifier = ">=1.50.0" },
{ name = "surya-ocr", specifier = ">=0.17.0" },
{ name = "torch", specifier = ">=2.8.0" },
{ name = "transformers", specifier = ">=4.57.0" },
{ name = "torchvision", specifier = ">=0.23.0" },
{ name = "transformers", specifier = ">=4.57.1" },
]
[[package]]
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