Add precommit

.pre-commit-config.yaml

@@ -0,0 +1,10 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.14.1
+  hooks:
+    # Run the linter.
+    - id: ruff-check
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format

chandra/model/__init__.py

@@ -16,11 +16,17 @@ class InferenceManager:
         else:
             self.model = None
 
-    def generate(self, batch: List[BatchInputItem], **kwargs) -> List[BatchOutputItem]:
+    def generate(
+        self, batch: List[BatchInputItem], max_output_tokens=None, **kwargs
+    ) -> List[BatchOutputItem]:
         if self.method == "vllm":
-            results = generate_vllm(batch, **kwargs)
+            results = generate_vllm(
+                batch, max_output_tokens=max_output_tokens, **kwargs
+            )
         else:
-            results = generate_hf(batch, self.model, **kwargs)
+            results = generate_hf(
+                batch, self.model, max_output_tokens=max_output_tokens, **kwargs
+            )
 
         output = []
         for result, input_item in zip(results, batch):
@@ -31,7 +37,7 @@ class InferenceManager:
                     chunks=parse_chunks(result.raw, input_item.image),
                     raw=result.raw,
                     page_box=[0, 0, input_item.image.width, input_item.image.height],
-                    token_count=result.token_count
+                    token_count=result.token_count,
                 )
             )
         return output

chandra/model/hf.py

@@ -9,7 +9,12 @@ from chandra.prompts import PROMPT_MAPPING
 from chandra.settings import settings
 
 
-def generate_hf(batch: List[BatchInputItem], model, **kwargs) -> List[GenerationResult]:
+def generate_hf(
+    batch: List[BatchInputItem], model, max_output_tokens=None, **kwargs
+) -> List[GenerationResult]:
+    if max_output_tokens is None:
+        max_output_tokens = settings.MAX_OUTPUT_TOKENS
+
     messages = [process_batch_element(item, model.processor) for item in batch]
     text = model.processor.apply_chat_template(
         messages, tokenize=False, add_generation_prompt=True
@@ -21,17 +26,20 @@ def generate_hf(batch: List[BatchInputItem], model, **kwargs) -> List[Generation
         images=image_inputs,
         padding=True,
         return_tensors="pt",
-        padding_side="left"
+        padding_side="left",
     )
     inputs = inputs.to("cuda")
 
     # Inference: Generation of the output
-    generated_ids = model.generate_hf(**inputs, max_new_tokens=settings.MAX_OUTPUT_TOKENS)
+    generated_ids = model.generate_hf(**inputs, max_new_tokens=max_output_tokens)
     generated_ids_trimmed = [
-        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        out_ids[len(in_ids) :]
+        for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
     ]
     output_text = model.processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        generated_ids_trimmed,
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=False,
     )
     results = [
         GenerationResult(raw=out, token_count=len(ids))
@@ -52,10 +60,7 @@ def process_batch_element(item: BatchInputItem, processor):
     content.append({"type": "image", "image": image})
 
     content.append({"type": "text", "text": prompt})
-    message = {
+    message = {"role": "user", "content": content}
-        "role": "user",
-        "content": content
-    }
     return message
 
 

chandra/model/vllm.py

@@ -20,7 +20,12 @@ def image_to_base64(image: Image.Image) -> str:
     return base64.b64encode(buffered.getvalue()).decode()
 
 
-def generate_vllm(batch: List[BatchInputItem], max_retries: int = None, max_workers: int | None = None) -> List[GenerationResult]:
+def generate_vllm(
+    batch: List[BatchInputItem],
+    max_output_tokens: int = None,
+    max_retries: int = None,
+    max_workers: int | None = None,
+) -> List[GenerationResult]:
     client = OpenAI(
         api_key=settings.VLLM_API_KEY,
         base_url=settings.VLLM_API_BASE,
@@ -33,11 +38,16 @@ def generate_vllm(batch: List[BatchInputItem], max_retries: int = None, max_work
     if max_workers is None:
         max_workers = min(64, len(batch))
 
+    if max_output_tokens is None:
+        max_output_tokens = settings.MAX_OUTPUT_TOKENS
+
     if model_name is None:
         models = client.models.list()
         model_name = models.data[0].id
 
-    def _generate(item: BatchInputItem, temperature: float = 0, top_p: float = .1) -> GenerationResult:
+    def _generate(
+        item: BatchInputItem, temperature: float = 0, top_p: float = 0.1
+    ) -> GenerationResult:
         prompt = item.prompt
         if not prompt:
             prompt = PROMPT_MAPPING[item.prompt_type]
@@ -45,40 +55,41 @@ def generate_vllm(batch: List[BatchInputItem], max_retries: int = None, max_work
         content = []
         image = scale_to_fit(item.image)
         image_b64 = image_to_base64(image)
-        content.append({
+        content.append(
-            "type": "image_url",
+            {
-            "image_url": {
+                "type": "image_url",
-                "url": f"data:image/png;base64,{image_b64}"
+                "image_url": {"url": f"data:image/png;base64,{image_b64}"},
             }
-        })
+        )
 
-        content.append({
+        content.append({"type": "text", "text": prompt})
-            "type": "text",
-            "text": prompt
-        })
 
         completion = client.chat.completions.create(
             model=model_name,
-            messages=[{
+            messages=[{"role": "user", "content": content}],
-                "role": "user",
-                "content": content
-            }],
             max_tokens=settings.MAX_OUTPUT_TOKENS,
             temperature=temperature,
             top_p=top_p,
         )
         return GenerationResult(
             raw=completion.choices[0].message.content,
-            token_count=completion.usage.completion_tokens
+            token_count=completion.usage.completion_tokens,
         )
 
     def process_item(item, max_retries):
         result = _generate(item)
         retries = 0
 
-        while retries < max_retries and (detect_repeat_token(result.raw) or
+        while retries < max_retries and (
-                                         (len(result.raw) > 50 and detect_repeat_token(result.raw, cut_from_end=50))):
+            detect_repeat_token(result.raw)
-            print(f"Detected repeat token, retrying generation (attempt {retries + 1})...")
+            or (
+                len(result.raw) > 50
+                and detect_repeat_token(result.raw, cut_from_end=50)
+            )
+        ):
+            print(
+                f"Detected repeat token, retrying generation (attempt {retries + 1})..."
+            )
             result = _generate(item, temperature=0.3, top_p=0.95)
             retries += 1
 

chandra/output.py

@@ -24,6 +24,7 @@ def parse_html(html: str, include_headers_footers: bool = False):
         out_html += content
     return out_html
 
+
 def escape_dollars(text):
     return text.replace("$", r"\$")
 
@@ -139,7 +140,11 @@ def parse_markdown(html: str, include_headers_footers: bool = False):
         inline_math_delimiters=("$", "$"),
         block_math_delimiters=("$$", "$$"),
     )
-    markdown = md_cls.convert(html)
+    try:
+        markdown = md_cls.convert(html)
+    except Exception as e:
+        print(f"Error converting HTML to Markdown: {e}")
+        markdown = ""
     return markdown.strip()
 
 
@@ -161,8 +166,8 @@ def parse_layout(html: str, image: Image.Image):
         bbox = div.get("data-bbox")
         try:
             bbox = json.loads(bbox)
-        except Exception as e:
+        except Exception:
-            bbox = [0, 0, 1, 1] # Fallback to a default bbox if parsing fails
+            bbox = [0, 0, 1, 1]  # Fallback to a default bbox if parsing fails
 
         bbox = list(map(int, bbox))
         # Normalize bbox
@@ -177,8 +182,8 @@ def parse_layout(html: str, image: Image.Image):
         layout_blocks.append(LayoutBlock(bbox=bbox, label=label, content=content))
     return layout_blocks
 
+
 def parse_chunks(html: str, image: Image.Image):
     layout = parse_layout(html, image)
     chunks = [asdict(block) for block in layout]
     return chunks
-

pyproject.toml

@@ -24,3 +24,8 @@ dependencies = [
 
 [tool.setuptools.packages.find]
 include = ["chandra*"]
+
+[dependency-groups]
+dev = [
+    "pre-commit>=4.3.0",
+]

uv.lock

@@ -182,6 +182,11 @@ dependencies = [
     { name = "transformers" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "pre-commit" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "beautifulsoup4", specifier = ">=4.14.2" },
@@ -201,6 +206,9 @@ requires-dist = [
     { name = "transformers", specifier = ">=4.57.1" },
 ]
 
+[package.metadata.requires-dev]
+dev = [{ name = "pre-commit", specifier = ">=4.3.0" }]
+
 [[package]]
 name = "charset-normalizer"
 version = "3.4.3"