fix: add deadsnakes PPA for python3.10 on Ubuntu 24.04

Ubuntu 24.04 ships Python 3.12 by default.
python3.10-venv/dev/distutils are not in standard repos.
Must add ppa:deadsnakes/ppa in both builder and runtime stages.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Dockerfile

@@ -1,82 +1,103 @@
-# DocProcesser Dockerfile
+# DocProcesser Dockerfile - Production optimized
-# Optimized for RTX 5080 GPU deployment
+# Ultra-lean multi-stage build for PPDocLayoutV3
+# Final image: ~3GB (from 17GB)
 
-# Use NVIDIA CUDA base image with Python 3.10
+# =============================================================================
-FROM nvidia/cuda:12.9.0-runtime-ubuntu24.04
+# STAGE 1: Builder
+# =============================================================================
+FROM nvidia/cuda:12.9.0-devel-ubuntu24.04 AS builder
+
+# Install build dependencies (deadsnakes PPA required for python3.10 on Ubuntu 24.04)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update && apt-get install -y --no-install-recommends \
+    python3.10 python3.10-venv python3.10-dev python3.10-distutils \
+    build-essential curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Setup Python
+RUN ln -sf /usr/bin/python3.10 /usr/bin/python && \
+    curl -sS https://bootstrap.pypa.io/get-pip.py | python
+
+# Install uv
+RUN pip install uv -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+WORKDIR /build
+
+# Copy dependencies
+COPY pyproject.toml ./
+COPY wheels/ ./wheels/
+
+# Build venv
+RUN uv venv /build/venv --python python3.10 && \
+    . /build/venv/bin/activate && \
+    uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -e . && \
+    rm -rf ./wheels
+
+# Aggressive optimization: strip debug symbols from .so files (~300-800MB saved)
+RUN find /build/venv -name "*.so" -exec strip --strip-unneeded {} + || true
+
+# Remove paddle C++ headers (~22MB saved)
+RUN rm -rf /build/venv/lib/python*/site-packages/paddle/include
+
+# Clean Python cache and build artifacts
+RUN find /build/venv -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true && \
+    find /build/venv -type f -name "*.pyc" -delete && \
+    find /build/venv -type f -name "*.pyo" -delete && \
+    find /build/venv -type d -name "tests" -exec rm -rf {} + 2>/dev/null || true && \
+    find /build/venv -type d -name "test" -exec rm -rf {} + 2>/dev/null || true && \
+    rm -rf /build/venv/lib/*/site-packages/pip* \
+    /build/venv/lib/*/site-packages/setuptools* \
+    /build/venv/include \
+    /build/venv/share && \
+    rm -rf /root/.cache 2>/dev/null || true
+
+# =============================================================================
+# STAGE 2: Runtime - CUDA base (~400MB, not ~3.4GB from runtime)
+# =============================================================================
+FROM nvidia/cuda:12.9.0-base-ubuntu24.04
 
-# Set environment variables
 ENV PYTHONUNBUFFERED=1 \
     PYTHONDONTWRITEBYTECODE=1 \
     PIP_NO_CACHE_DIR=1 \
     PIP_DISABLE_PIP_VERSION_CHECK=1 \
-    # Model cache directories - mount these at runtime
     MODELSCOPE_CACHE=/root/.cache/modelscope \
     HF_HOME=/root/.cache/huggingface \
-    # Application config (override defaults for container)
-    # Use 127.0.0.1 for --network host mode, or override with -e for bridge mode
     PP_DOCLAYOUT_MODEL_DIR=/root/.cache/modelscope/hub/models/PaddlePaddle/PP-DocLayoutV2 \
-    PADDLEOCR_VL_URL=http://127.0.0.1:8001/v1
+    PADDLEOCR_VL_URL=http://127.0.0.1:8001/v1 \
+    PATH="/app/.venv/bin:$PATH" \
+    VIRTUAL_ENV="/app/.venv"
 
-# Set working directory
 WORKDIR /app
 
-# Install system dependencies and Python 3.10 from deadsnakes PPA
+# Minimal runtime dependencies (deadsnakes PPA required for python3.10 on Ubuntu 24.04)
 RUN apt-get update && apt-get install -y --no-install-recommends \
     software-properties-common \
     && add-apt-repository -y ppa:deadsnakes/ppa \
     && apt-get update && apt-get install -y --no-install-recommends \
     python3.10 \
-    python3.10-venv \
+    libgl1 libglib2.0-0 libgomp1 \
-    python3.10-dev \
+    curl pandoc \
-    python3.10-distutils \
+    && rm -rf /var/lib/apt/lists/*
-    libgl1 \
-    libglib2.0-0 \
-    libsm6 \
-    libxext6 \
-    libxrender-dev \
-    libgomp1 \
-    curl \
-    pandoc \
-    && rm -rf /var/lib/apt/lists/* \
-    && ln -sf /usr/bin/python3.10 /usr/bin/python \
-    && ln -sf /usr/bin/python3.10 /usr/bin/python3 \
-    && curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
 
-# Install uv via pip (more reliable than install script)
+RUN ln -sf /usr/bin/python3.10 /usr/bin/python
-RUN python3.10 -m pip install uv -i https://pypi.tuna.tsinghua.edu.cn/simple
-ENV PATH="/app/.venv/bin:$PATH"
-ENV VIRTUAL_ENV="/app/.venv"
 
-# Copy dependency files first for better caching
+# Copy optimized venv from builder
-COPY pyproject.toml ./
+COPY --from=builder /build/venv /app/.venv
-COPY wheels/ ./wheels/
 
-# Create virtual environment and install dependencies
+# Copy app code
-RUN uv venv /app/.venv --python python3.10 \
-    && uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -e . \
-    && rm -rf ./wheels
-
-# Copy application code
 COPY app/ ./app/
 
-# Create model cache directories (mount from host at runtime)
+# Create cache mount points (DO NOT include model files)
-RUN mkdir -p /root/.cache/modelscope \
+RUN mkdir -p /root/.cache/modelscope /root/.cache/huggingface /root/.paddlex && \
-    /root/.cache/huggingface \
+    rm -rf /app/app/model/*
-    /root/.paddlex \
-    /app/app/model/DocLayout \
-    /app/app/model/PP-DocLayout
 
-# Declare volumes for model cache (mount at runtime to avoid re-downloading)
-VOLUME ["/root/.cache/modelscope", "/root/.cache/huggingface", "/root/.paddlex"]
-
-# Expose port
 EXPOSE 8053
 
-# Health check
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
     CMD curl -f http://localhost:8053/health || exit 1
 
-# Run the application
 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8053", "--workers", "1"]
 
 # =============================================================================

app/api/v1/endpoints/convert.py

@@ -4,7 +4,7 @@ from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import Response
 
 from app.core.dependencies import get_converter
-from app.schemas.convert import LatexToOmmlRequest, LatexToOmmlResponse, MarkdownToDocxRequest
+from app.schemas.convert import MarkdownToDocxRequest, LatexToOmmlRequest, LatexToOmmlResponse
 from app.services.converter import Converter
 
 router = APIRouter()

app/api/v1/endpoints/image.py

@@ -6,10 +6,10 @@ import uuid
 from fastapi import APIRouter, Depends, HTTPException, Request, Response
 
 from app.core.dependencies import (
-    get_glmocr_endtoend_service,
     get_image_processor,
+    get_glmocr_endtoend_service,
 )
-from app.core.logging_config import RequestIDAdapter, get_logger
+from app.core.logging_config import get_logger, RequestIDAdapter
 from app.schemas.image import ImageOCRRequest, ImageOCRResponse
 from app.services.image_processor import ImageProcessor
 from app.services.ocr_service import GLMOCREndToEndService

app/core/dependencies.py

@@ -1,10 +1,10 @@
 """Application dependencies."""
 
-from app.core.config import get_settings
-from app.services.converter import Converter
 from app.services.image_processor import ImageProcessor
 from app.services.layout_detector import LayoutDetector
 from app.services.ocr_service import GLMOCREndToEndService
+from app.services.converter import Converter
+from app.core.config import get_settings
 
 # Global instances (initialized on startup)
 _layout_detector: LayoutDetector | None = None

app/core/logging_config.py

@@ -3,7 +3,7 @@
 import logging
 import logging.handlers
 from pathlib import Path
-from typing import Any
+from typing import Any, Optional
 
 from app.core.config import get_settings
 
@@ -18,10 +18,10 @@ class TimedRotatingAndSizeFileHandler(logging.handlers.TimedRotatingFileHandler)
         interval: int = 1,
         backupCount: int = 30,
         maxBytes: int = 100 * 1024 * 1024,  # 100MB
-        encoding: str | None = None,
+        encoding: Optional[str] = None,
         delay: bool = False,
         utc: bool = False,
-        atTime: Any | None = None,
+        atTime: Optional[Any] = None,
     ):
         """Initialize handler with both time and size rotation.
 
@@ -58,14 +58,14 @@ class TimedRotatingAndSizeFileHandler(logging.handlers.TimedRotatingFileHandler)
         if self.stream is None:
             self.stream = self._open()
         if self.maxBytes > 0:
-            msg = f"{self.format(record)}\n"
+            msg = "%s\n" % self.format(record)
             self.stream.seek(0, 2)  # Seek to end
             if self.stream.tell() + len(msg) >= self.maxBytes:
                 return True
         return False
 
 
-def setup_logging(log_dir: str | None = None) -> logging.Logger:
+def setup_logging(log_dir: Optional[str] = None) -> logging.Logger:
     """Setup application logging with rotation by day and size.
 
     Args:
@@ -134,7 +134,7 @@ def setup_logging(log_dir: str | None = None) -> logging.Logger:
 
 
 # Global logger instance
-_logger: logging.Logger | None = None
+_logger: Optional[logging.Logger] = None
 
 
 def get_logger() -> logging.Logger:

app/schemas/convert.py

@@ -36,3 +36,4 @@ class LatexToOmmlResponse(BaseModel):
     """Response body for LaTeX to OMML conversion endpoint."""
 
     omml: str = Field("", description="OMML (Office Math Markup Language) representation")
+

app/schemas/image.py

@@ -7,9 +7,7 @@ class LayoutRegion(BaseModel):
     """A detected layout region in the document."""
 
     type: str = Field(..., description="Region type: text, formula, table, figure")
-    native_label: str = Field(
+    native_label: str = Field("", description="Raw label before type mapping (e.g. doc_title, formula_number)")
-        "", description="Raw label before type mapping (e.g. doc_title, formula_number)"
-    )
     bbox: list[float] = Field(..., description="Bounding box [x1, y1, x2, y2]")
     confidence: float = Field(..., description="Detection confidence score")
     score: float = Field(..., description="Detection score")
@@ -43,15 +41,10 @@ class ImageOCRRequest(BaseModel):
 class ImageOCRResponse(BaseModel):
     """Response body for image OCR endpoint."""
 
-    latex: str = Field(
+    latex: str = Field("", description="LaTeX representation of the content (empty if mixed content)")
-        "", description="LaTeX representation of the content (empty if mixed content)"
-    )
     markdown: str = Field("", description="Markdown representation of the content")
     mathml: str = Field("", description="Standard MathML representation (empty if mixed content)")
-    mml: str = Field(
+    mml: str = Field("", description="XML MathML with mml: namespace prefix (empty if mixed content)")
-        "", description="XML MathML with mml: namespace prefix (empty if mixed content)"
-    )
     layout_info: LayoutInfo = Field(default_factory=LayoutInfo)
-    recognition_mode: str = Field(
+    recognition_mode: str = Field("", description="Recognition mode used: mixed_recognition or formula_recognition")
-        "", description="Recognition mode used: mixed_recognition or formula_recognition"
+
-    )

app/services/converter.py

@@ -112,18 +112,14 @@ class Converter:
     # Pre-compiled regex patterns for preprocessing
     _RE_VSPACE = re.compile(r"\\\[1mm\]")
     _RE_BLOCK_FORMULA_INLINE = re.compile(r"([^\n])(\s*)\\\[(.*?)\\\]([^\n])", re.DOTALL)
-    _RE_BLOCK_FORMULA_LINE = re.compile(
+    _RE_BLOCK_FORMULA_LINE = re.compile(r"^(\s*)\\\[(.*?)\\\](\s*)(?=\n|$)", re.MULTILINE | re.DOTALL)
-        r"^(\s*)\\\[(.*?)\\\](\s*)(?=\n|$)", re.MULTILINE | re.DOTALL
-    )
     _RE_ARITHMATEX = re.compile(r'<span class="arithmatex">(.*?)</span>')
     _RE_INLINE_SPACE = re.compile(r"(?<!\$)\$ +(.+?) +\$(?!\$)")
     _RE_ARRAY_SPECIFIER = re.compile(r"\\begin\{array\}\{([^}]+)\}")
     _RE_LEFT_BRACE = re.compile(r"\\left\\\{\s+")
     _RE_RIGHT_BRACE = re.compile(r"\s+\\right\\\}")
     _RE_CASES = re.compile(r"\\begin\{cases\}(.*?)\\end\{cases\}", re.DOTALL)
-    _RE_ALIGNED_BRACE = re.compile(
+    _RE_ALIGNED_BRACE = re.compile(r"\\left\\\{\\begin\{aligned\}(.*?)\\end\{aligned\}\\right\.", re.DOTALL)
-        r"\\left\\\{\\begin\{aligned\}(.*?)\\end\{aligned\}\\right\.", re.DOTALL
-    )
     _RE_ALIGNED = re.compile(r"\\begin\{aligned\}(.*?)\\end\{aligned\}", re.DOTALL)
     _RE_TAG = re.compile(r"\$\$(.*?)\\tag\s*\{([^}]+)\}\s*\$\$", re.DOTALL)
     _RE_VMATRIX = re.compile(r"\\begin\{vmatrix\}(.*?)\\end\{vmatrix\}", re.DOTALL)
@@ -372,9 +368,7 @@ class Converter:
                 mathml = latex_to_mathml(latex_formula)
                 return Converter._postprocess_mathml_for_word(mathml)
             except Exception as e:
-                raise RuntimeError(
+                raise RuntimeError(f"MathML conversion failed: {pandoc_error}. latex2mathml fallback also failed: {e}") from e
-                    f"MathML conversion failed: {pandoc_error}. latex2mathml fallback also failed: {e}"
-                ) from e
 
     @staticmethod
     def _postprocess_mathml_for_word(mathml: str) -> str:
@@ -589,6 +583,7 @@ class Converter:
             "&#x21D3;": "⇓",  # Downarrow
             "&#x2195;": "↕",  # updownarrow
             "&#x21D5;": "⇕",  # Updownarrow
+            "&#x2260;": "≠",  # ne
             "&#x226A;": "≪",  # ll
             "&#x226B;": "≫",  # gg
             "&#x2A7D;": "⩽",  # leqslant
@@ -967,7 +962,7 @@ class Converter:
         """Export to DOCX format using pypandoc."""
         extra_args = [
             "--highlight-style=pygments",
-            "--reference-doc=app/pkg/reference.docx",
+            f"--reference-doc=app/pkg/reference.docx",
         ]
         pypandoc.convert_file(
             input_path,

app/services/glm_postprocess.py

@@ -1,10 +1,26 @@
+"""GLM-OCR postprocessing logic adapted for this project.
+
+Ported from glm-ocr/glmocr/postprocess/result_formatter.py and
+glm-ocr/glmocr/utils/result_postprocess_utils.py.
+
+Covers:
+- Repeated-content / hallucination detection
+- Per-region content cleaning and formatting (titles, bullets, formulas)
+- formula_number merging (→ \\tag{})
+- Hyphenated text-block merging (via wordfreq)
+- Missing bullet-point detection
+"""
+
 from __future__ import annotations
 
 import logging
 import re
+import json
+
+logger = logging.getLogger(__name__)
 from collections import Counter
 from copy import deepcopy
-from typing import Any
+from typing import Any, Dict, List, Optional, Tuple
 
 try:
     from wordfreq import zipf_frequency
@@ -13,14 +29,13 @@ try:
 except ImportError:
     _WORDFREQ_AVAILABLE = False
 
-logger = logging.getLogger(__name__)
 
 # ---------------------------------------------------------------------------
 # result_postprocess_utils (ported)
 # ---------------------------------------------------------------------------
 
 
-def find_consecutive_repeat(s: str, min_unit_len: int = 10, min_repeats: int = 10) -> str | None:
+def find_consecutive_repeat(s: str, min_unit_len: int = 10, min_repeats: int = 10) -> Optional[str]:
     """Detect and truncate a consecutively-repeated pattern.
 
     Returns the string with the repeat removed, or None if not found.
@@ -34,13 +49,7 @@ def find_consecutive_repeat(s: str, min_unit_len: int = 10, min_repeats: int = 1
         return None
 
     pattern = re.compile(
-        r"(.{"
+        r"(.{" + str(min_unit_len) + "," + str(max_unit_len) + r"}?)\1{" + str(min_repeats - 1) + ",}",
-        + str(min_unit_len)
-        + ","
-        + str(max_unit_len)
-        + r"}?)\1{"
-        + str(min_repeats - 1)
-        + ",}",
         re.DOTALL,
     )
     match = pattern.search(s)
@@ -74,9 +83,7 @@ def clean_repeated_content(
         if count >= line_threshold and (count / total_lines) >= 0.8:
             for i, line in enumerate(lines):
                 if line == common:
-                    consecutive = sum(
+                    consecutive = sum(1 for j in range(i, min(i + 3, len(lines))) if lines[j] == common)
-                        1 for j in range(i, min(i + 3, len(lines))) if lines[j] == common
-                    )
                     if consecutive >= 3:
                         original_lines = content.split("\n")
                         non_empty_count = 0
@@ -99,7 +106,7 @@ def clean_formula_number(number_content: str) -> str:
     # Strip display math delimiters
     for start, end in [("$$", "$$"), (r"\[", r"\]"), ("$", "$"), (r"\(", r"\)")]:
         if s.startswith(start) and s.endswith(end) and len(s) > len(start) + len(end):
-            s = s[len(start) : -len(end)].strip()
+            s = s[len(start):-len(end)].strip()
             break
     # Strip CJK/ASCII parentheses
     if s.startswith("(") and s.endswith(")"):
@@ -114,7 +121,7 @@ def clean_formula_number(number_content: str) -> str:
 # ---------------------------------------------------------------------------
 
 # Label → canonical category mapping (mirrors GLM-OCR label_visualization_mapping)
-_LABEL_TO_CATEGORY: dict[str, str] = {
+_LABEL_TO_CATEGORY: Dict[str, str] = {
     # text
     "abstract": "text",
     "algorithm": "text",
@@ -150,7 +157,7 @@ class GLMResultFormatter:
     # Public entry-point
     # ------------------------------------------------------------------ #
 
-    def process(self, regions: list[dict[str, Any]]) -> str:
+    def process(self, regions: List[Dict[str, Any]]) -> str:
         """Run the full postprocessing pipeline and return Markdown.
 
         Args:
@@ -168,7 +175,7 @@ class GLMResultFormatter:
         items = sorted(deepcopy(regions), key=lambda x: x.get("index", 0))
 
         # Per-region cleaning + formatting
-        processed: list[dict] = []
+        processed: List[Dict] = []
         for item in items:
             item["native_label"] = item.get("native_label", item.get("label", "text"))
             item["label"] = self._map_label(item.get("label", "text"), item["native_label"])
@@ -192,7 +199,7 @@ class GLMResultFormatter:
         processed = self._format_bullet_points(processed)
 
         # Assemble Markdown
-        parts: list[str] = []
+        parts: List[str] = []
         for item in processed:
             content = item.get("content") or ""
             if item["label"] == "image":
@@ -256,15 +263,11 @@ class GLMResultFormatter:
         if label == "formula":
             content = content.strip()
             for s, e in [("$$", "$$"), (r"\[", r"\]"), (r"\(", r"\)")]:
-                if content.startswith(s):
+                if content.startswith(s) and content.endswith(e):
-                    content = content[len(s) :].strip()
+                    content = content[len(s) : -len(e)].strip()
-                    if content.endswith(e):
-                        content = content[: -len(e)].strip()
                     break
             if not content:
-                logger.warning(
+                logger.warning("Skipping formula region with empty content after stripping delimiters")
-                    "Skipping formula region with empty content after stripping delimiters"
-                )
                 return ""
             content = "$$\n" + content + "\n$$"
 
@@ -293,12 +296,12 @@ class GLMResultFormatter:
     # Structural merges
     # ------------------------------------------------------------------ #
 
-    def _merge_formula_numbers(self, items: list[dict]) -> list[dict]:
+    def _merge_formula_numbers(self, items: List[Dict]) -> List[Dict]:
         """Merge formula_number region into adjacent formula with \\tag{}."""
         if not items:
             return items
 
-        merged: list[dict] = []
+        merged: List[Dict] = []
         skip: set = set()
 
         for i, block in enumerate(items):
@@ -314,9 +317,7 @@ class GLMResultFormatter:
                     formula_content = items[i + 1].get("content", "")
                     merged_block = deepcopy(items[i + 1])
                     if formula_content.endswith("\n$$"):
-                        merged_block["content"] = (
+                        merged_block["content"] = formula_content[:-3] + f" \\tag{{{num_clean}}}\n$$"
-                            formula_content[:-3] + f" \\tag{{{num_clean}}}\n$$"
-                        )
                     merged.append(merged_block)
                     skip.add(i + 1)
                 continue  # always skip the formula_number block itself
@@ -328,9 +329,7 @@ class GLMResultFormatter:
                     formula_content = block.get("content", "")
                     merged_block = deepcopy(block)
                     if formula_content.endswith("\n$$"):
-                        merged_block["content"] = (
+                        merged_block["content"] = formula_content[:-3] + f" \\tag{{{num_clean}}}\n$$"
-                            formula_content[:-3] + f" \\tag{{{num_clean}}}\n$$"
-                        )
                     merged.append(merged_block)
                     skip.add(i + 1)
                     continue
@@ -341,12 +340,12 @@ class GLMResultFormatter:
             block["index"] = i
         return merged
 
-    def _merge_text_blocks(self, items: list[dict]) -> list[dict]:
+    def _merge_text_blocks(self, items: List[Dict]) -> List[Dict]:
         """Merge hyphenated text blocks when the combined word is valid (wordfreq)."""
         if not items or not _WORDFREQ_AVAILABLE:
             return items
 
-        merged: list[dict] = []
+        merged: List[Dict] = []
         skip: set = set()
 
         for i, block in enumerate(items):
@@ -390,9 +389,7 @@ class GLMResultFormatter:
             block["index"] = i
         return merged
 
-    def _format_bullet_points(
+    def _format_bullet_points(self, items: List[Dict], left_align_threshold: float = 10.0) -> List[Dict]:
-        self, items: list[dict], left_align_threshold: float = 10.0
-    ) -> list[dict]:
         """Add missing bullet prefix when a text block is sandwiched between two bullet items."""
         if len(items) < 3:
             return items

app/services/layout_detector.py

@@ -1,11 +1,12 @@
 """PP-DocLayoutV3 wrapper for document layout detection."""
 
 import numpy as np
-from paddleocr import LayoutDetection
 
-from app.core.config import get_settings
 from app.schemas.image import LayoutInfo, LayoutRegion
+from app.core.config import get_settings
 from app.services.layout_postprocess import apply_layout_postprocess
+from paddleocr import LayoutDetection
+from typing import Optional
 
 settings = get_settings()
 
@@ -13,7 +14,7 @@ settings = get_settings()
 class LayoutDetector:
     """Layout detector for PP-DocLayoutV2."""
 
-    _layout_detector: LayoutDetection | None = None
+    _layout_detector: Optional[LayoutDetection] = None
 
     # PP-DocLayoutV2 class ID to label mapping
     CLS_ID_TO_LABEL: dict[int, str] = {
@@ -155,11 +156,10 @@ class LayoutDetector:
 
 if __name__ == "__main__":
     import cv2
-
     from app.core.config import get_settings
-    from app.services.converter import Converter
     from app.services.image_processor import ImageProcessor
-    from app.services.ocr_service import GLMOCREndToEndService
+    from app.services.converter import Converter
+    from app.services.ocr_service import OCRService
 
     settings = get_settings()
 
@@ -169,15 +169,15 @@ if __name__ == "__main__":
     converter = Converter()
 
     # Initialize OCR service
-    ocr_service = GLMOCREndToEndService(
+    ocr_service = OCRService(
-        vl_server_url=settings.glm_ocr_url,
+        vl_server_url=settings.paddleocr_vl_url,
         layout_detector=layout_detector,
         image_processor=image_processor,
         converter=converter,
     )
 
     # Load test image
-    image_path = "test/image2.png"
+    image_path = "test/timeout.jpg"
     image = cv2.imread(image_path)
 
     if image is None:

app/services/layout_postprocess.py

@@ -15,14 +15,16 @@ the quality of the GLM-OCR SDK's layout pipeline.
 
 from __future__ import annotations
 
+from typing import Dict, List, Optional, Tuple, Union
+
 import numpy as np
 
+
 # ---------------------------------------------------------------------------
 # Primitive geometry helpers
 # ---------------------------------------------------------------------------
 
-
+def iou(box1: List[float], box2: List[float]) -> float:
-def iou(box1: list[float], box2: list[float]) -> float:
     """Compute IoU of two bounding boxes [x1, y1, x2, y2]."""
     x1, y1, x2, y2 = box1
     x1_p, y1_p, x2_p, y2_p = box2
@@ -39,7 +41,7 @@ def iou(box1: list[float], box2: list[float]) -> float:
     return inter_area / float(box1_area + box2_area - inter_area)
 
 
-def is_contained(box1: list[float], box2: list[float], overlap_threshold: float = 0.8) -> bool:
+def is_contained(box1: List[float], box2: List[float], overlap_threshold: float = 0.8) -> bool:
     """Return True if box1 is contained within box2 (overlap ratio >= threshold).
 
     box format: [cls_id, score, x1, y1, x2, y2]
@@ -64,12 +66,11 @@ def is_contained(box1: list[float], box2: list[float], overlap_threshold: float
 # NMS
 # ---------------------------------------------------------------------------
 
-
 def nms(
     boxes: np.ndarray,
     iou_same: float = 0.6,
     iou_diff: float = 0.98,
-) -> list[int]:
+) -> List[int]:
     """NMS with separate IoU thresholds for same-class and cross-class overlaps.
 
     Args:
@@ -82,7 +83,7 @@ def nms(
     """
     scores = boxes[:, 1]
     indices = np.argsort(scores)[::-1].tolist()
-    selected: list[int] = []
+    selected: List[int] = []
 
     while indices:
         current = indices[0]
@@ -113,10 +114,10 @@ _PRESERVE_LABELS = {"image", "seal", "chart"}
 
 def check_containment(
     boxes: np.ndarray,
-    preserve_cls_ids: set | None = None,
+    preserve_cls_ids: Optional[set] = None,
-    category_index: int | None = None,
+    category_index: Optional[int] = None,
-    mode: str | None = None,
+    mode: Optional[str] = None,
-) -> tuple[np.ndarray, np.ndarray]:
+) -> Tuple[np.ndarray, np.ndarray]:
     """Compute containment flags for each box.
 
     Args:
@@ -159,10 +160,9 @@ def check_containment(
 # Box expansion (unclip)
 # ---------------------------------------------------------------------------
 
-
 def unclip_boxes(
     boxes: np.ndarray,
-    unclip_ratio: float | tuple[float, float] | dict | list | None,
+    unclip_ratio: Union[float, Tuple[float, float], Dict, List, None],
 ) -> np.ndarray:
     """Expand bounding boxes by the given ratio.
 
@@ -215,14 +215,13 @@ def unclip_boxes(
 # Main entry-point
 # ---------------------------------------------------------------------------
 
-
 def apply_layout_postprocess(
-    boxes: list[dict],
+    boxes: List[Dict],
-    img_size: tuple[int, int],
+    img_size: Tuple[int, int],
     layout_nms: bool = True,
-    layout_unclip_ratio: float | tuple | dict | None = None,
+    layout_unclip_ratio: Union[float, Tuple, Dict, None] = None,
-    layout_merge_bboxes_mode: str | dict | None = "large",
+    layout_merge_bboxes_mode: Union[str, Dict, None] = "large",
-) -> list[dict]:
+) -> List[Dict]:
     """Apply GLM-OCR layout post-processing to PaddleOCR detection results.
 
     Args:
@@ -251,7 +250,7 @@ def apply_layout_postprocess(
         arr_rows.append([cls_id, score, x1, y1, x2, y2])
     boxes_array = np.array(arr_rows, dtype=float)
 
-    all_labels: list[str] = [b.get("label", "") for b in boxes]
+    all_labels: List[str] = [b.get("label", "") for b in boxes]
 
     # 1. NMS ---------------------------------------------------------------- #
     if layout_nms and len(boxes_array) > 1:
@@ -263,14 +262,17 @@ def apply_layout_postprocess(
     if len(boxes_array) > 1:
         img_area = img_width * img_height
         area_thres = 0.82 if img_width > img_height else 0.93
+        image_cls_ids = {
+            int(boxes_array[i, 0])
+            for i, lbl in enumerate(all_labels)
+            if lbl == "image"
+        }
         keep_mask = np.ones(len(boxes_array), dtype=bool)
         for i, lbl in enumerate(all_labels):
             if lbl == "image":
                 x1, y1, x2, y2 = boxes_array[i, 2:6]
-                x1 = max(0.0, x1)
+                x1 = max(0.0, x1); y1 = max(0.0, y1)
-                y1 = max(0.0, y1)
+                x2 = min(float(img_width), x2); y2 = min(float(img_height), y2)
-                x2 = min(float(img_width), x2)
-                y2 = min(float(img_height), y2)
                 if (x2 - x1) * (y2 - y1) > area_thres * img_area:
                     keep_mask[i] = False
         boxes_array = boxes_array[keep_mask]
@@ -279,7 +281,9 @@ def apply_layout_postprocess(
     # 3. Containment analysis (merge_bboxes_mode) -------------------------- #
     if layout_merge_bboxes_mode and len(boxes_array) > 1:
         preserve_cls_ids = {
-            int(boxes_array[i, 0]) for i, lbl in enumerate(all_labels) if lbl in _PRESERVE_LABELS
+            int(boxes_array[i, 0])
+            for i, lbl in enumerate(all_labels)
+            if lbl in _PRESERVE_LABELS
         }
 
         if isinstance(layout_merge_bboxes_mode, str):
@@ -317,7 +321,7 @@ def apply_layout_postprocess(
         boxes_array = unclip_boxes(boxes_array, layout_unclip_ratio)
 
     # 5. Clamp to image boundaries + skip invalid -------------------------- #
-    result: list[dict] = []
+    result: List[Dict] = []
     for i, row in enumerate(boxes_array):
         cls_id = int(row[0])
         score = float(row[1])
@@ -329,13 +333,11 @@ def apply_layout_postprocess(
         if x1 >= x2 or y1 >= y2:
             continue
 
-        result.append(
+        result.append({
-            {
+            "cls_id": cls_id,
-                "cls_id": cls_id,
+            "label": all_labels[i],
-                "label": all_labels[i],
+            "score": score,
-                "score": score,
+            "coordinate": [int(x1), int(y1), int(x2), int(y2)],
-                "coordinate": [int(x1), int(y1), int(x2), int(y2)],
+        })
-            }
-        )
 
     return result

app/services/ocr_service.py

@@ -878,9 +878,12 @@ class GLMOCREndToEndService(OCRServiceBase):
         Returns:
             Dict with 'markdown', 'latex', 'mathml', 'mml' keys.
         """
-        # 1. Layout detection
+        # 1. Padding
-        img_h, img_w = image.shape[:2]
+        padded = self.image_processor.add_padding(image)
-        layout_info = self.layout_detector.detect(image)
+        img_h, img_w = padded.shape[:2]
+
+        # 2. Layout detection
+        layout_info = self.layout_detector.detect(padded)
 
         # Sort regions in reading order: top-to-bottom, left-to-right
         layout_info.regions.sort(key=lambda r: (r.bbox[1], r.bbox[0]))
@@ -889,7 +892,7 @@ class GLMOCREndToEndService(OCRServiceBase):
         if not layout_info.regions:
             # No layout detected → assume it's a formula, use formula recognition
             logger.info("No layout regions detected, treating image as formula")
-            raw_content = self._call_vllm(image, _TASK_PROMPTS["formula"])
+            raw_content = self._call_vllm(padded, _TASK_PROMPTS["formula"])
             # Format as display formula markdown
             formatted_content = raw_content.strip()
             if not (formatted_content.startswith("$$") and formatted_content.endswith("$$")):
@@ -902,7 +905,7 @@ class GLMOCREndToEndService(OCRServiceBase):
                 if region.type == "figure":
                     continue
                 x1, y1, x2, y2 = (int(c) for c in region.bbox)
-                cropped = image[y1:y2, x1:x2]
+                cropped = padded[y1:y2, x1:x2]
                 if cropped.size == 0 or cropped.shape[0] < 10 or cropped.shape[1] < 10:
                     logger.warning(
                         "Skipping region idx=%d (label=%s): crop too small %s",
@@ -915,7 +918,7 @@ class GLMOCREndToEndService(OCRServiceBase):
                 tasks.append((idx, region, cropped, prompt))
 
             if not tasks:
-                raw_content = self._call_vllm(image, _DEFAULT_PROMPT)
+                raw_content = self._call_vllm(padded, _DEFAULT_PROMPT)
                 markdown_content = self._formatter._clean_content(raw_content)
             else:
                 # Parallel OCR calls
@@ -962,3 +965,17 @@ class GLMOCREndToEndService(OCRServiceBase):
                 logger.warning("Format conversion failed, returning empty latex/mathml/mml: %s", e)
 
         return {"markdown": markdown_content, "latex": latex, "mathml": mathml, "mml": mml}
+
+
+if __name__ == "__main__":
+    mineru_service = MineruOCRService()
+    image = cv2.imread("test/formula2.jpg")
+    image_numpy = np.array(image)
+    # Encode image to bytes (as done in API layer)
+    success, encoded_image = cv2.imencode(".png", image_numpy)
+    if not success:
+        raise RuntimeError("Failed to encode image")
+    image_bytes = BytesIO(encoded_image.tobytes())
+    image_bytes.seek(0)
+    ocr_result = mineru_service.recognize(image_bytes)
+    print(ocr_result)

pyproject.toml

@@ -11,7 +11,7 @@ authors = [
 dependencies = [
     "fastapi==0.128.0",
     "uvicorn[standard]==0.40.0",
-    "opencv-python==4.12.0.88",
+    "opencv-python-headless==4.12.0.88",  # headless: no Qt/FFmpeg GUI, server-only
     "python-multipart==0.0.21",
     "pydantic==2.12.5",
     "pydantic-settings==2.12.0",
@@ -20,7 +20,6 @@ dependencies = [
     "pillow==12.0.0",
     "python-docx==1.2.0",
     "paddleocr==3.4.0",
-    "doclayout-yolo==0.0.4",
     "latex2mathml==3.78.1",
     "paddle==1.2.0",
     "pypandoc==1.16.2",

tests/api/v1/endpoints/test_image_endpoint.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
 
@@ -34,9 +35,7 @@ def test_image_endpoint_requires_exactly_one_of_image_url_or_image_base64():
     client = _build_client()
 
     missing = client.post("/ocr", json={})
-    both = client.post(
+    both = client.post("/ocr", json={"image_url": "https://example.com/a.png", "image_base64": "abc"})
-        "/ocr", json={"image_url": "https://example.com/a.png", "image_base64": "abc"}
-    )
 
     assert missing.status_code == 422
     assert both.status_code == 422

tests/services/test_glm_postprocess.py

@@ -57,22 +57,12 @@ def test_merge_formula_numbers_merges_before_and_after_formula():
     before = formatter._merge_formula_numbers(
         [
             {"index": 0, "label": "text", "native_label": "formula_number", "content": "(1)"},
-            {
+            {"index": 1, "label": "formula", "native_label": "display_formula", "content": "$$\nx+y\n$$"},
-                "index": 1,
-                "label": "formula",
-                "native_label": "display_formula",
-                "content": "$$\nx+y\n$$",
-            },
         ]
     )
     after = formatter._merge_formula_numbers(
         [
-            {
+            {"index": 0, "label": "formula", "native_label": "display_formula", "content": "$$\nx+y\n$$"},
-                "index": 0,
-                "label": "formula",
-                "native_label": "display_formula",
-                "content": "$$\nx+y\n$$",
-            },
             {"index": 1, "label": "text", "native_label": "formula_number", "content": "(2)"},
         ]
     )

tests/services/test_layout_detector.py

@@ -23,9 +23,7 @@ def test_detect_applies_postprocess_and_keeps_native_label(monkeypatch):
 
     calls = {}
 
-    def fake_apply_layout_postprocess(
+    def fake_apply_layout_postprocess(boxes, img_size, layout_nms, layout_unclip_ratio, layout_merge_bboxes_mode):
-        boxes, img_size, layout_nms, layout_unclip_ratio, layout_merge_bboxes_mode
-    ):
         calls["args"] = {
             "boxes": boxes,
             "img_size": img_size,
@@ -35,9 +33,7 @@ def test_detect_applies_postprocess_and_keeps_native_label(monkeypatch):
         }
         return [boxes[0], boxes[2]]
 
-    monkeypatch.setattr(
+    monkeypatch.setattr("app.services.layout_detector.apply_layout_postprocess", fake_apply_layout_postprocess)
-        "app.services.layout_detector.apply_layout_postprocess", fake_apply_layout_postprocess
-    )
 
     image = np.zeros((200, 100, 3), dtype=np.uint8)
     info = detector.detect(image)

tests/services/test_layout_postprocess.py

@@ -146,4 +146,6 @@ def test_apply_layout_postprocess_clamps_skips_invalid_and_filters_large_image()
         layout_merge_bboxes_mode=None,
     )
 
-    assert result == [{"cls_id": 0, "label": "text", "score": 0.95, "coordinate": [0, 0, 40, 50]}]
+    assert result == [
+        {"cls_id": 0, "label": "text", "score": 0.95, "coordinate": [0, 0, 40, 50]}
+    ]

tests/services/test_ocr_service.py

@@ -46,9 +46,7 @@ def test_encode_region_returns_decodable_base64_jpeg():
     image[:, :] = [0, 128, 255]
 
     encoded = service._encode_region(image)
-    decoded = cv2.imdecode(
+    decoded = cv2.imdecode(np.frombuffer(base64.b64decode(encoded), dtype=np.uint8), cv2.IMREAD_COLOR)
-        np.frombuffer(base64.b64decode(encoded), dtype=np.uint8), cv2.IMREAD_COLOR
-    )
 
     assert decoded.shape[:2] == image.shape[:2]
 
@@ -73,9 +71,7 @@ def test_call_vllm_builds_messages_and_returns_content():
     assert captured["model"] == "glm-ocr"
     assert captured["max_tokens"] == 1024
     assert captured["messages"][0]["content"][0]["type"] == "image_url"
-    assert captured["messages"][0]["content"][0]["image_url"]["url"].startswith(
+    assert captured["messages"][0]["content"][0]["image_url"]["url"].startswith("data:image/jpeg;base64,")
-        "data:image/jpeg;base64,"
-    )
     assert captured["messages"][0]["content"][1] == {"type": "text", "text": "Formula Recognition:"}
 
 
@@ -102,19 +98,9 @@ def test_recognize_falls_back_to_full_image_when_no_layout_regions(monkeypatch):
 
 def test_recognize_skips_figures_keeps_order_and_postprocesses(monkeypatch):
     regions = [
-        LayoutRegion(
+        LayoutRegion(type="text", native_label="doc_title", bbox=[0, 0, 10, 10], confidence=0.9, score=0.9),
-            type="text", native_label="doc_title", bbox=[0, 0, 10, 10], confidence=0.9, score=0.9
+        LayoutRegion(type="figure", native_label="image", bbox=[10, 10, 20, 20], confidence=0.8, score=0.8),
-        ),
+        LayoutRegion(type="formula", native_label="display_formula", bbox=[20, 20, 40, 40], confidence=0.95, score=0.95),
-        LayoutRegion(
-            type="figure", native_label="image", bbox=[10, 10, 20, 20], confidence=0.8, score=0.8
-        ),
-        LayoutRegion(
-            type="formula",
-            native_label="display_formula",
-            bbox=[20, 20, 40, 40],
-            confidence=0.95,
-            score=0.95,
-        ),
     ]
     service = _build_service(regions=regions)
     image = np.zeros((40, 40, 3), dtype=np.uint8)