updata inference demo

demo/demo_vllm.py

@@ -11,7 +11,15 @@ parser = argparse.ArgumentParser()
 parser.add_argument("--ip", type=str, default="localhost")
 parser.add_argument("--port", type=str, default="8000")
 parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.ocr")
-parser.add_argument("--prompt_mode", type=str, default="prompt_layout_all_en")
+parser.add_argument("--prompt_mode", type=str, default="prompt_layout_all_en",help=(
+        "Choose a task prompt: "
+        "prompt_layout_all_en=full document layout+OCR to JSON/MD; "
+        "prompt_layout_only_en=layout detection only; "
+        "prompt_grounding_ocr=OCR within a given bbox; "
+        "prompt_web_parsing=parse webpage screenshot layout into JSON; "
+        "prompt_scene_spotting=detect+recognize scene text (OCR boxes+texts); "
+        "prompt_image_to_svg=generate SVG code to reconstruct the image.")
+)
 
 args = parser.parse_args()
 

demo/demo_vllm_general.py

@@ -0,0 +1,40 @@
+import argparse
+
+from openai import OpenAI
+from transformers.utils.versions import require_version
+from PIL import Image
+from dots_ocr.utils import dict_promptmode_to_prompt
+from dots_ocr.model.inference import inference_with_vllm
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--ip", type=str, default="localhost")
+parser.add_argument("--port", type=str, default="8000")
+parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.ocr")
+parser.add_argument("--custom_prompt", type=str, default="Please describe the content of this image.")
+
+args = parser.parse_args()
+
+require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
+
+
+def main():
+    addr = f"http://{args.ip}:{args.port}/v1"
+    image_path = "demo/demo_image3.jpg"
+    prompt = args.custom_prompt
+    image = Image.open(image_path)
+    response = inference_with_vllm(
+        image,
+        prompt, 
+        ip=args.ip,
+        port=args.port,
+        temperature=0.1,
+        top_p=0.9,
+        model_name=args.model_name,
+        system_prompt="You are a helpful assistant.", #general tasks need system_prompt
+    )
+    print(f"response: {response}")
+
+
+if __name__ == "__main__":
+    main()

demo/demo_vllm_svg.py

@@ -0,0 +1,43 @@
+import argparse
+
+from openai import OpenAI
+from transformers.utils.versions import require_version
+from PIL import Image
+from dots_ocr.utils import dict_promptmode_to_prompt
+from dots_ocr.model.inference import inference_with_vllm
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--ip", type=str, default="localhost")
+parser.add_argument("--port", type=str, default="8000")
+parser.add_argument("--model_name", type=str, default="rednote-hilab/dots.ocr")
+parser.add_argument("--prompt_mode", type=str, default="prompt_image_to_svg")
+
+args = parser.parse_args()
+
+require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
+
+
+def main():
+    addr = f"http://{args.ip}:{args.port}/v1"
+    image_path = "demo/demo_image2.png"
+    image = Image.open(image_path)
+    prompt = dict_promptmode_to_prompt[args.prompt_mode]
+
+    #prompt = Please generate the SVG code based on the image.viewBox="0 0 {img_width} {img_height}"
+    prompt = prompt.replace("{width}", str(image.width)).replace("{height}", str(image.height))
+
+    response = inference_with_vllm(
+        image,
+        prompt, 
+        ip=args.ip,
+        port=args.port,
+        temperature=0.9,           # SVG: low temperature often causes repetitive/looping output
+        top_p=1.0,
+        model_name=args.model_name,
+    )
+    print(f"response: {response}")
+
+
+if __name__ == "__main__":
+    main()

dots_ocr/model/inference.py

@@ -14,11 +14,14 @@ def inference_with_vllm(
         top_p=0.9,
         max_completion_tokens=32768,
         model_name='rednote-hilab/dots.ocr',
+        system_prompt=None,
         ):
     
     addr = f"{protocol}://{ip}:{port}/v1"
     client = OpenAI(api_key="{}".format(os.environ.get("API_KEY", "0")), base_url=addr)
     messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
     messages.append(
         {
             "role": "user",

dots_ocr/utils/prompts.py

@@ -22,12 +22,24 @@ dict_promptmode_to_prompt = {
     # prompt_layout_only_en: layout detection
     "prompt_layout_only_en": """Please output the layout information from this PDF image, including each layout's bbox and its category. The bbox should be in the format [x1, y1, x2, y2]. The layout categories for the PDF document include ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title']. Do not output the corresponding text. The layout result should be in JSON format.""",
 
-    # prompt_layout_only_en: parse ocr text except the Page-header and Page-footer
+    # prompt_ocr: parse ocr text except the Page-header and Page-footer
     "prompt_ocr": """Extract the text content from this image.""",
 
     # prompt_grounding_ocr: extract text content in the given bounding box
     "prompt_grounding_ocr": """Extract text from the given bounding box on the image (format: [x1, y1, x2, y2]).\nBounding Box:\n""",
 
+    # prompt_web_parsing: parse all webpage layout info in json format.
+    "prompt_web_parsing": """Parsing the layout info of this webpage image with format json:\n""",
+
+    # prompt_scene_spotting: scene spotting
+    "prompt_scene_spotting": """Detect and recognize the text in the image.""",
+    
+    # prompt_img2svg: generate the SVG code of the image
+    "prompt_image_to_svg": """Please generate the SVG code based on the image.viewBox="0 0 {width} {height}\"""",
+
+    # prompt_free_qa: general prompt 
+    "prompt_general": """ """,
+
     # "prompt_table_html": """Convert the table in this image to HTML.""",
     # "prompt_table_latex": """Convert the table in this image to LaTeX.""",
     # "prompt_formula_latex": """Convert the formula in this image to LaTeX.""",