prithivMLmods commited on
Commit
eb3026d
·
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1 Parent(s): 0861843

Update app.py

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Files changed (1) hide show
  1. app.py +7 -8
app.py CHANGED
@@ -15,7 +15,6 @@ import cv2
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  from transformers import (
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  Qwen2VLForConditionalGeneration,
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- Glm4vForConditionalGeneration,
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  Qwen2_5_VLForConditionalGeneration,
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  AutoModelForImageTextToText,
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  AutoProcessor,
@@ -67,10 +66,10 @@ model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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  ).to(device).eval()
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  #-----------------------------subfolder-----------------------------#
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- # Load GLM-4.1V-9B-Thinking
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- MODEL_ID_O = "THUDM/GLM-4.1V-9B-Thinking"
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  processor_o = AutoProcessor.from_pretrained(MODEL_ID_O, trust_remote_code=True)
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- model_o = Glm4vForConditionalGeneration.from_pretrained(
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  MODEL_ID_O,
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  trust_remote_code=True,
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  torch_dtype=torch.float16
@@ -117,7 +116,7 @@ def generate_image(model_name: str, text: str, image: Image.Image,
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  elif model_name == "MonkeyOCR-Recognition":
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  processor = processor_g
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  model = model_g
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- elif model_name == "GLM-4.1V-9B-Thinking":
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  processor = processor_o
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  model = model_o
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  else:
@@ -175,7 +174,7 @@ def generate_video(model_name: str, text: str, video_path: str,
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  elif model_name == "MonkeyOCR-Recognition":
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  processor = processor_g
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  model = model_g
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- elif model_name == "GLM-4.1V-9B-Thinking":
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  processor = processor_o
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  model = model_o
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  else:
@@ -291,14 +290,14 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
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  markdown_output = gr.Markdown(label="(Result.Md)")
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  model_choice = gr.Radio(
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- choices=["docscopeOCR-7B-050425-exp", "MonkeyOCR-Recognition", "coreOCR-7B-050325-preview", "GLM-4.1V-9B-Thinking"],
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  label="Select Model",
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  value="docscopeOCR-7B-050425-exp"
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  )
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  gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/core-OCR/discussions)")
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- gr.Markdown("> [GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking): GLM-4.1V-9B-Thinking, designed to explore the upper limits of reasoning in vision-language models. By introducing a thinking paradigm and leveraging reinforcement learning, the model significantly enhances its capabilities. It achieves state-of-the-art performance among 10B-parameter VLMs.")
300
  gr.Markdown("> [docscopeOCR-7B-050425-exp](https://huggingface.co/prithivMLmods/docscopeOCR-7B-050425-exp): The docscopeOCR-7B-050425-exp model is a fine-tuned version of Qwen2.5-VL-7B-Instruct, optimized for Document-Level Optical Character Recognition (OCR), long-context vision-language understanding, and accurate image-to-text conversion with mathematical LaTeX formatting.")
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  gr.Markdown("> [MonkeyOCR](https://huggingface.co/echo840/MonkeyOCR): MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.")
 
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  gr.Markdown("> [coreOCR-7B-050325-preview](https://huggingface.co/prithivMLmods/coreOCR-7B-050325-preview): The coreOCR-7B-050325-preview model is a fine-tuned version of Qwen2-VL-7B, optimized for Document-Level Optical Character Recognition (OCR), long-context vision-language understanding, and accurate image-to-text conversion with mathematical LaTeX formatting.")
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  gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")
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15
 
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  from transformers import (
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  Qwen2VLForConditionalGeneration,
 
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  Qwen2_5_VLForConditionalGeneration,
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  AutoModelForImageTextToText,
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  AutoProcessor,
 
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  ).to(device).eval()
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  #-----------------------------subfolder-----------------------------#
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+ # Load Pollux-OCR-Caption-2B
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+ MODEL_ID_O = "prithivMLmods/Pollux-OCR-Caption-2B"
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  processor_o = AutoProcessor.from_pretrained(MODEL_ID_O, trust_remote_code=True)
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+ model_o = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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  MODEL_ID_O,
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  trust_remote_code=True,
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  torch_dtype=torch.float16
 
116
  elif model_name == "MonkeyOCR-Recognition":
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  processor = processor_g
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  model = model_g
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+ elif model_name == "Pollux-OCR-Caption-2B":
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  processor = processor_o
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  model = model_o
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  else:
 
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  elif model_name == "MonkeyOCR-Recognition":
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  processor = processor_g
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  model = model_g
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+ elif model_name == "Pollux-OCR-Caption-2B":
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  processor = processor_o
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  model = model_o
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  else:
 
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  markdown_output = gr.Markdown(label="(Result.Md)")
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  model_choice = gr.Radio(
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+ choices=["docscopeOCR-7B-050425-exp", "MonkeyOCR-Recognition", "coreOCR-7B-050325-preview", "Pollux-OCR-Caption-2B"],
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  label="Select Model",
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  value="docscopeOCR-7B-050425-exp"
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  )
297
  gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/core-OCR/discussions)")
 
298
  gr.Markdown("> [docscopeOCR-7B-050425-exp](https://huggingface.co/prithivMLmods/docscopeOCR-7B-050425-exp): The docscopeOCR-7B-050425-exp model is a fine-tuned version of Qwen2.5-VL-7B-Instruct, optimized for Document-Level Optical Character Recognition (OCR), long-context vision-language understanding, and accurate image-to-text conversion with mathematical LaTeX formatting.")
299
  gr.Markdown("> [MonkeyOCR](https://huggingface.co/echo840/MonkeyOCR): MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.")
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+ gr.Markdown("> [Pollux-OCR-Caption-2B](https://huggingface.co/prithivMLmods/Pollux-OCR-Caption-2B): The Pollux-OCR-Caption-2B model is a fine-tuned version of Qwen2.5-VL architecture, optimized for Document Retrieval, Content Extraction, and Analysis Recognition. Built on top of the Qwen2.5-VL architecture.")
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  gr.Markdown("> [coreOCR-7B-050325-preview](https://huggingface.co/prithivMLmods/coreOCR-7B-050325-preview): The coreOCR-7B-050325-preview model is a fine-tuned version of Qwen2-VL-7B, optimized for Document-Level Optical Character Recognition (OCR), long-context vision-language understanding, and accurate image-to-text conversion with mathematical LaTeX formatting.")
302
  gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")
303