Lints

olmocr/bench/runners/run_nanonetsocr_2.py

@@ -1,21 +1,10 @@
-import json
-from typing import Literal
-
-import httpx
 import base64
 import subprocess
-from PIL import Image
+
-from olmocr.bench.prompts import (
+import httpx
-    build_basic_prompt,
+
-    build_openai_silver_data_prompt_no_document_anchoring,
-)
 from olmocr.data.renderpdf import get_pdf_media_box_width_height
-from olmocr.prompts.anchor import get_anchor_text
+
-from olmocr.prompts.prompts import (
-    PageResponse,
-    build_finetuning_prompt,
-    build_openai_silver_data_prompt,
-)
 
 # Logic to set min size from here: https://github.com/NanoNets/Nanonets-OCR2/blob/main/Nanonets-OCR2-Cookbook/image2md.ipynb
 def render_pdf_to_base64png_min_short_size(local_pdf_path: str, page_num: int, target_shortest_dim: int = 2048) -> str:
@@ -64,19 +53,21 @@ async def run_server(
     """
     # Convert the first page of the PDF to a base64-encoded PNG image.
     image_base64 = render_pdf_to_base64png_min_short_size(pdf_path, page_num=page_num, target_shortest_dim=page_dimensions)
-   
+
     # Now use th
     prompt = """Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes."""
 
-
     request = {
         "model": model,
         "messages": [
             {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": [
+            {
-                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
+                "role": "user",
-                {"type": "text", "text": prompt},
+                "content": [
-            ]},
+                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
+                    {"type": "text", "text": prompt},
+                ],
+            },
         ],
         "temperature": temperature,
         "max_tokens": 4096,

olmocr/pipeline.py

@@ -214,17 +214,17 @@ async def apost(url, json_data, api_key=None):
                 # Read chunk size line
                 size_line = await reader.readline()
                 chunk_size = int(size_line.strip(), 16)  # Hex format
-                
+
                 if chunk_size == 0:
                     await reader.readline()  # Read final CRLF
                     break
-                
+
                 chunk_data = await reader.readexactly(chunk_size)
                 chunks.append(chunk_data)
-                
+
                 # Read trailing CRLF after chunk data
                 await reader.readline()
-            
+
             response_body = b"".join(chunks)
         elif headers.get("connection", "") == "close":
             # Read until connection closes
@@ -1115,7 +1115,6 @@ async def main():
     )
     server_group.add_argument("--api_key", type=str, default=None, help="API key for authenticated remote servers (e.g., DeepInfra)")
 
-
     vllm_group = parser.add_argument_group(
         "VLLM arguments", "These arguments are passed to vLLM. Any unrecognized arguments are also automatically forwarded to vLLM."
     )
@@ -1127,7 +1126,6 @@ async def main():
     vllm_group.add_argument("--data-parallel-size", "-dp", type=int, default=1, help="Data parallel size for vLLM")
     vllm_group.add_argument("--port", type=int, default=30024, help="Port to use for the VLLM server")
 
-
     # Beaker/job running stuff
     beaker_group = parser.add_argument_group("beaker/cluster execution")
     beaker_group.add_argument("--beaker", action="store_true", help="Submit this job to beaker instead of running locally")
@@ -1277,7 +1275,7 @@ async def main():
     if use_internal_server:
         model_name_or_path = await download_model(args.model)
         args.server = f"http://localhost:{args.port}/v1"
-        args.model = "olmocr" # Internal server always uses this name for the model, for supporting weird local model paths
+        args.model = "olmocr"  # Internal server always uses this name for the model, for supporting weird local model paths
         logger.info(f"Using internal server at {args.server}")
     else:
         logger.info(f"Using external server at {args.server}")