app.py

import gradio as gr
import torch
import tempfile
import os
from PIL import Image
from transformers import AutoProcessor, HunYuanVLForConditionalGeneration

# ============================================================
# HunyuanOCR - Image Text Extraction
# ============================================================
MODEL_ID = "tencent/HunyuanOCR"
model = None
processor = None

def clean_repeated_substrings(text):
    n = len(text)
    if n < 8000:
        return text
    for length in range(2, n // 10 + 1):
        candidate = text[-length:]
        count = 0
        i = n - length
        while i >= 0 and text[i:i + length] == candidate:
            count += 1
            i -= length
        if count >= 10:
            return text[:n - length * (count - 1)]
    return text

def load_model():
    global model, processor
    if model is not None:
        return
    token = os.getenv("HF_TOKEN", None)
    print("Loading HunyuanOCR ...")
    processor = AutoProcessor.from_pretrained(MODEL_ID, use_fast=False, token=token)
    model = HunYuanVLForConditionalGeneration.from_pretrained(
        MODEL_ID,
        attn_implementation="eager",
        device_map=None,
        low_cpu_mem_usage=True,
        token=token,
    ).float()
    model.eval()
    print("HunyuanOCR loaded.")

def ocr_process(image):
    if image is None:
        return "Please upload an image."

    load_model()

    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
        image.save(tmp.name)
        img_path = tmp.name

    try:
        messages = [
            {
                "role": "system",
                "content": ""
            },
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": img_path},
                    {"type": "text", "text": "检测并识别图片中的文字，将文本坐标格式化输出。"}
                ]
            }
        ]

        text_prompt = processor.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        image_input = Image.open(img_path)
        inputs = processor(
            text=[text_prompt], images=[image_input],
            padding=True, return_tensors="pt"
        )

        # The processor outputs bfloat16 tensors, but model is float32.
        # BatchFeature doesn't support in-place modification well,
        # so rebuild as a plain dict with float32 tensors.
        clean_inputs = {}
        for k, v in inputs.items():
            if isinstance(v, torch.Tensor):
                if v.dtype == torch.bfloat16:
                    clean_inputs[k] = v.to(torch.float32)
                else:
                    clean_inputs[k] = v
            else:
                clean_inputs[k] = v

        with torch.no_grad():
            generated_ids = model.generate(**clean_inputs, max_new_tokens=16384, do_sample=False)

        input_ids = clean_inputs["input_ids"]
        generated_ids_trimmed = [
            out_ids[len(in_ids):] for in_ids, out_ids in zip(input_ids, generated_ids)
        ]
        output_text = clean_repeated_substrings(
            processor.batch_decode(
                generated_ids_trimmed,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=False
            )[0]
        )

        return output_text
    finally:
        if os.path.exists(img_path):
            os.remove(img_path)

# ============================================================
# Gradio Interface
# ============================================================
with gr.Blocks(title="HunyuanOCR") as demo:
    gr.Markdown("""
    # HunyuanOCR - Text Extraction
    Upload an image and the model will detect and extract all text with coordinates.
    """)

    image_input = gr.Image(type="pil", label="Upload Image")
    ocr_output = gr.Textbox(label="Extracted Text", lines=15)
    ocr_btn = gr.Button("Extract Text", variant="primary")

    ocr_btn.click(ocr_process, image_input, ocr_output)
    image_input.change(ocr_process, image_input, ocr_output)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")