add

app.py

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+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
+    import os
+    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()  # convert all params from bfloat16 to float32 for CPU
+    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"
+        ).to("cpu")
+
+        with torch.no_grad():
+            generated_ids = model.generate(**inputs, max_new_tokens=16384, do_sample=False)
+
+        input_ids = 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, show_copy_button=True)
+    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")

requirements.txt

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+git+https://github.com/huggingface/transformers.git@82a06db03535c49aa987719ed0746a76093b1ec4
+torch
+torchvision
+gradio
+accelerate
+Pillow