Create app.py

app.py

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+ABOUT = """
+# TB-OCR Unofficial Demo
+
+This is an unofficial demo of [yifeihu/TB-OCR-preview-0.1](https://huggingface.co/yifeihu/TB-OCR-preview-0.1).
+
+Overview of TB-OCR:
+
+> TB-OCR-preview (Text Block OCR), created by [Yifei Hu](https://x.com/hu_yifei), is an end-to-end OCR model handling text, math latex, and markdown formats all at once. The model takes a block of text as the input and returns clean markdown output. Headers are marked with `##`. Math expressions are guaranteed to be wrapped in brackets `\( inline math \) \[ display math \]` for easier parsing. This model does not require line-detection or math formula detection. 
+
+(From the [model card](https://huggingface.co/yifeihu/TB-OCR-preview-0.1))
+"""
+# check out https://huggingface.co/microsoft/Phi-3.5-vision-instruct for more details
+
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor
+from PIL import Image
+import requests
+
+model_id = "yifeihu/TB-OCR-preview-0.1"
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = AutoModelForCausalLM.from_pretrained(
+  model_id, 
+  device_map="cuda", 
+  trust_remote_code=True, 
+  torch_dtype="auto", 
+  _attn_implementation='flash_attention_2',
+  load_in_4bit=True # Optional: Load model in 4-bit mode to save memory
+)
+
+processor = AutoProcessor.from_pretrained(model_id, 
+  trust_remote_code=True, 
+  num_crops=16
+)
+
+def phi_ocr(image_url):
+    question = "Convert the text to markdown format."
+    image = Image.open(image_url)
+    prompt_message = [{
+        'role': 'user',
+        'content': f'<|image_1|>\n{question}',
+    }]
+
+    prompt = processor.tokenizer.apply_chat_template(prompt_message, tokenize=False, add_generation_prompt=True)
+    inputs = processor(prompt, [image], return_tensors="pt").to("cuda") 
+
+    generation_args = { 
+        "max_new_tokens": 1024, 
+        "temperature": 0.1, 
+        "do_sample": False
+    }
+
+    generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args
+    )
+
+    generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
+    response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 
+
+    response = response.split("<image_end>")[0] # remove the image_end token 
+
+    return response
+
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.Markdown(ABOUT)
+    with gr.Row():
+        with gr.Column():
+            img = gr.Image(label="Input image", type="filename")
+            btn = gr.Button("OCR")
+        with gr.Column():
+            out = gr.Markdown()
+    btn.click(phi_ocr, inputs=img, outputs=out)