# Code anh Thang
# import gradio as gr
# from transformers import AutoProcessor, AutoModelForVision2Seq
# from PIL import Image
# import torch

# device = "cuda" if torch.cuda.is_available() else "cpu"
# torch.cuda.empty_cache()

# model_id = "prithivMLmods/Camel-Doc-OCR-062825"
# processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
# model = AutoModelForVision2Seq.from_pretrained(
#     model_id,
#     torch_dtype=torch.float16 if device == "cuda" else torch.float32,
#     trust_remote_code=True
# ).to(device)

# def predict(image, prompt=None):
#     image = image.convert("RGB")

#     # Cực kỳ quan trọng: text="" bắt buộc phải có
#     inputs = processor(images=image, text=prompt, return_tensors="pt").to(device)
#     # In debug để kiểm tra input_ids
#     print(">>> input_ids shape:", inputs.input_ids.shape)
#     generated_ids = model.generate(
#         **inputs,
#         max_new_tokens=512,
#         do_sample=False,
#         use_cache=False,  # ✅ Thêm dòng này để fix lỗi cache_position
#         eos_token_id=processor.tokenizer.eos_token_id,
#         pad_token_id=processor.tokenizer.pad_token_id
#     )

#     result = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
#     return result

# demo = gr.Interface(
#     fn=predict,
#     inputs=[
#         gr.Image(type="pil", label="Tải ảnh tài liệu lên"),
#         gr.Textbox(label="Gợi ý (tuỳ chọn)", placeholder="VD: Trích số hóa đơn")
#     ],
#     outputs="text",
#     title="Camel-Doc OCR - Trích xuất văn bản từ ảnh"
# )

# if __name__ == "__main__":
#     demo.launch()

# Code fix
import os
import json
import re
import hashlib
import gc
from io import BytesIO
from collections import OrderedDict
from PIL import Image, UnidentifiedImageError
import torch
from transformers import AutoProcessor, BitsAndBytesConfig
from transformers.models.qwen2_5_vl import Qwen2_5_VLForConditionalGeneration
from pdf2image import convert_from_bytes
import gradio as gr
import fitz

# --- CONFIGURATION ---
MODEL_ID = "prithivMLmods/Camel-Doc-OCR-062825"
CACHE_MAX_SIZE = 128
DPI = 100
THREAD_COUNT = 4
IMAGE_MAX_DIM = 1024
JPEG_QUALITY = 75
GPU_MEMORY_FRACTION = 0.8  # use 80% of GPU memory
PAD_TOKEN_ID = None  # set later to avoid warnings

# --- 1. Device & torch settings ---
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
if device.type == 'cuda':
    try:
        torch.cuda.set_per_process_memory_fraction(GPU_MEMORY_FRACTION, device=device)
    except Exception:
        pass

# --- 2. Model & tokenizer ---
bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
# load and compile model
try:
    base = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        MODEL_ID,
        quantization_config=bnb,
        device_map="auto",
        trust_remote_code=True
    )
    model = torch.compile(base.eval())
except Exception as e:
    print(f"[Warning] Model compile failed: {e}")
    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        MODEL_ID,
        quantization_config=bnb,
        device_map="auto",
        trust_remote_code=True
    ).eval()
# avoid padding warnings
PAD_TOKEN_ID = processor.tokenizer.eos_token_id
processor.tokenizer.pad_token_id = PAD_TOKEN_ID

# --- 3. Memory utilities ---
def cleanup_memory():
    gc.collect()
    if device.type == 'cuda':
        torch.cuda.empty_cache()

def get_memory_info():
    if device.type == 'cuda':
        return {
            'allocated': torch.cuda.memory_allocated() / (1024**3),
            'reserved': torch.cuda.memory_reserved() / (1024**3)
        }
    return {'allocated': 0, 'reserved': 0}

# --- 4. LRU Cache for inference ---
_mru_cache = OrderedDict()

def cache_get(key):
    if key in _mru_cache:
        _mru_cache.move_to_end(key)
        return _mru_cache[key]
    return None

def cache_set(key, value):
    _mru_cache[key] = value
    if len(_mru_cache) > CACHE_MAX_SIZE:
        _mru_cache.popitem(last=False)

def cache_clear():
    _mru_cache.clear()

# --- 5. Image preprocessing ---
def normalize_image(image: Image.Image) -> Image.Image:
    if image.mode in ("RGBA", "LA"):
        bg = Image.new("RGB", image.size, (255,255,255))
        bg.paste(image, mask=image.split()[-1])
        image = bg
    image.thumbnail((IMAGE_MAX_DIM, IMAGE_MAX_DIM), Image.Resampling.LANCZOS)
    return image.convert("RGB")

# --- 6. Cache key generation ---
def make_cache_key(image: Image.Image, prompt: str) -> str:
    bio = BytesIO()
    image.save(bio, format="JPEG", quality=JPEG_QUALITY)
    data = bio.getvalue() + prompt.encode('utf-8')
    return hashlib.md5(data).hexdigest()

# --- 7. Inference with mixed precision & error handling ---
def run_inference(image: Image.Image, prompt: str = "") -> str:
    prompt_text = prompt.strip() or "Read information from the document."
    img = normalize_image(image)
    key = make_cache_key(img, prompt_text)

    cached = cache_get(key)
    if cached is not None:
        return cached

    try:
        messages = [{
            "role": "user",
            "content": [
                {"type": "image", "image": img},
                {"type": "text",  "text": prompt_text}
            ]
        }]
        text_prompt = processor.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        inputs = processor(text=[text_prompt], images=[img], return_tensors="pt", padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}

        with torch.inference_mode():
            with torch.cuda.amp.autocast(enabled=(device.type=='cuda')):
                gen = model.generate(
                    **inputs,
                    max_new_tokens=512,
                    do_sample=False,
                    eos_token_id=processor.tokenizer.eos_token_id
                )
        trimmed = [o[len(i):] for i, o in zip(inputs['input_ids'], gen)]
        result = processor.tokenizer.batch_decode(
            trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True
        )[0].strip()

        cache_set(key, result)
        cleanup_memory()
        return result

    except torch.cuda.OutOfMemoryError:
        cleanup_memory()
        return "[OOM] GPU out of memory. Try smaller image."
    except Exception as e:
        cleanup_memory()
        return f"[Error] {str(e)}"

# --- 8. File handler ---
import traceback
def handle_file(file, prompt, extra_prompt, progress=gr.Progress()):
    try:
        # Xác định đường dẫn thật sự
        # file có thể là UploadedFile với .name, hoặc đơn giản là str
        file_path = file.name if hasattr(file, "name") else file
        filename = os.path.basename(file_path)
        ext = filename.lower().split('.')[-1]
        full_prompt = (prompt + "\n" + extra_prompt).strip() or "Read information from file/image."

        print(f"[INFO] handle_file → {filename} (.{ext})")

        # ---- PDF branch ----
        if ext == "pdf":
            try:
                with open(file_path, "rb") as f:
                    pdf_bytes = f.read()
                print(f"[INFO] Read PDF bytes: {len(pdf_bytes)} bytes")

                # Dùng PyMuPDF để convert
                doc = fitz.open(stream=pdf_bytes, filetype="pdf")
                pages = []
                for i, page in enumerate(doc, start=1):
                    pix = page.get_pixmap(dpi=DPI)
                    img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                    pages.append(img)
                print(f"[INFO] Converted PDF → {len(pages)} pages")

            except Exception as e:
                traceback.print_exc()
                return filename, f"[ERROR] PDF conversion failed: {e}"

            # Inference trên từng trang
            outputs = []
            for idx, img in enumerate(pages, start=1):
                try:
                    print(f"[INFO] Inference page {idx}")
                    out = run_inference(img, full_prompt)
                except Exception as e:
                    traceback.print_exc()
                    out = f"[ERROR] Inference page {idx} failed: {e}"
                outputs.append(out)
                if idx % 3 == 0:
                    cleanup_memory()
                progress((idx-1)/len(pages), desc=f"Page {idx}/{len(pages)}")

            result = "\n\n--- Page Break ---\n\n".join(outputs)
            print("[INFO] handle_file done")
            return filename, result

        # ---- Image branch ----
        else:
            try:
                img = Image.open(file_path)
                print(f"[INFO] Opened image: {img.mode}, {img.size}")
            except Exception as e:
                traceback.print_exc()
                return filename, f"[ERROR] Image open failed: {e}"
            return filename, run_inference(img, full_prompt)

    except Exception as e:
        traceback.print_exc()
        return "error", f"[ERROR] handle_file unexpected: {e}"

# --- 9. Prompt templates & JSON export ---
prompt_templates = {
    "Electrolux": """Extract all structured information from the delivery order document image.
You must return the result as a valid XML block that strictly follows the structure below.
STRICT INSTRUCTIONS – read carefully and follow EXACTLY:
1. Return **ONLY** the XML block – nothing before or after it.
2. DO NOT add, remove, rename, or reorder any XML tags.
3. DO NOT include explanations, markdown, notes, comments, or extra spacing outside the XML block.
4. For every tag, fill in the exact value read from the image.
   • NEVER copy or repeat the label/placeholder text.  
   • NEVER guess or invent values.
5. If a value is missing or unreadable, leave the tag EMPTY (e.g. <tag></tag>).
6. DO NOT include Vietnamese text or translations inside tag values.
7. The output MUST start with the root tag and end with its correct closing tag; all tags must be well-formed.
8. Dates must be in YYYY-MM-DD format.
9. Boolean tags must be exactly true or false (lower-case, no quotes).  
   ✔ √ Yes Passed ⇒ true | ✘ X No Fail ⇒ false
10. **Inside each value**  
    • Replace every internal line-break with “, ” (comma + space).  
    • Trim leading/trailing whitespace.  
    • Escape XML special characters: & → &amp;, < → &lt;, > → &gt;.
11. **Phone / contact fields** – digits, “+”, “–”, spaces only; if multiple numbers, separate with “, ”.
12. **Signature fields** – fill ONLY if the signature appears as legible text; if it is handwritten, leave the tag empty.
13. Ignore any information not represented by the tags below.
<s_electrolux_form>
  <document_number>Số lệnh giao nhận hàng</document_number>
  <order_number>Số đơn hàng</order_number>
  <customer_code>Mã số khách hàng</customer_code>
  <customer_order_code>Mã đơn khách hàng</customer_order_code>
  <customer_order_date>Ngày đặt hàng của khách</customer_order_date>
  <delivery_date>Ngày giao hàng</delivery_date>
  <requested_delivery_date>Ngày giao hàng yêu cầu</requested_delivery_date>
  <invoice_number>Số hóa đơn</invoice_number>
  <shipper_company_name>Tên công ty gửi hàng</shipper_company_name>
  <shipper_address>Địa chỉ gửi hàng</shipper_address>
  <shipper_phone>Số điện thoại</shipper_phone>
  <shipper_fax>Số fax</shipper_fax>
  <shipper_tax_code>Mã số thuế</shipper_tax_code>
  <consignee_customer_code>Mã khách hàng</consignee_customer_code>
  <consignee_company_name>Tên công ty nhận hàng</consignee_company_name>
  <shipping_address>Địa chỉ nhận hàng chi tiết</shipping_address>
  <city_province>Tỉnh/Thành phố</city_province>
  <postal_code>Mã bưu chính</postal_code>
  <preparer_name>Họ tên người lập phiếu</preparer_name>
  <preparer_date>Ngày lập phiếu</preparer_date>
  <s_is_signed>Đã ký hay chưa (true hoặc false)</s_is_signed>
</s_electrolux_form>
""",

    "Jotun": """Extract all structured information from the delivery order document.
You must return the result as a valid XML block that strictly follows the structure below.
STRICT INSTRUCTIONS – read carefully and follow EXACTLY:
1. Return **ONLY** the XML block – nothing before or after it.
2. DO NOT add, remove, rename, or reorder any XML tags.
3. DO NOT include explanations, markdown, notes, comments, or extra spacing outside the XML block.
4. For every tag, fill in the exact value read from the image.
   • NEVER copy or repeat the label/placeholder text.  
   • NEVER guess or invent values.
5. If a value is missing or unreadable, leave the tag EMPTY (e.g. <tag></tag>).
6. DO NOT include Vietnamese text or translations inside tag values.
7. The output MUST start with the root tag and end with its correct closing tag; all tags must be well-formed.
8. Dates must be in YYYY-MM-DD format.
9. Boolean tags must be exactly true or false (lower-case, no quotes).  
   ✔ √ Yes Passed ⇒ true | ✘ X No Fail ⇒ false
10. **Inside each value**  
    • Replace every internal line-break with “, ” (comma + space).  
    • Trim leading/trailing whitespace.  
    • Escape XML special characters: & → &amp;, < → &lt;, > → &gt;.
11. **Phone / contact fields** – digits, “+”, “–”, spaces only; if multiple numbers, separate with “, ”.
12. **Signature fields** – fill ONLY if the signature appears as legible text; if it is handwritten, leave the tag empty.
13. Ignore any information not represented by the tags below.
<s_jotun_form>
  <document_number>Số lệnh giao hàng</document_number>
  <delivery_order_code>Số lệnh giao hàng số</delivery_order_code>
  <customer_code>Mã khách hàng</customer_code>
  <customer_name>Tên khách hàng</customer_name>
  <customer_address>Địa chỉ khách hàng</customer_address>
  <customer_phone>Điện thoại khách hàng</customer_phone>
  <invoice_receiver_name>Tên người nhận hóa đơn</invoice_receiver_name>
  <invoice_receiver_address>Địa chỉ người nhận hóa đơn</invoice_receiver_address>
  <order_code>Số đơn đặt hàng</order_code>
  <order_date>Ngày đặt hàng</order_date>
  <order_number>Số đơn hàng</order_number>
  <delivery_date>Ngày giao hàng</delivery_date>
  <s_is_signed>Đã ký hay chưa (true hoặc false)</s_is_signed>
</s_jotun_form>
""",

    "MAWB": """Extract all structured information from the Master Air Waybill (MAWB) document.
You must return the result as a valid XML block that strictly follows the structure below.
STRICT INSTRUCTIONS – read carefully and follow EXACTLY:
1. Return **ONLY** the XML block – nothing before or after it.
2. DO NOT add, remove, rename, or reorder any XML tags.
3. DO NOT include explanations, markdown, notes, comments, or extra spacing outside the XML block.
4. For every tag, fill in the exact value read from the image.
   • NEVER copy or repeat the label/placeholder text.  
   • NEVER guess or invent values.
5. If a value is missing or unreadable, leave the tag EMPTY (e.g. <tag></tag>).
6. DO NOT include Vietnamese text or translations inside tag values.
7. The output MUST start with the root tag and end with its correct closing tag; all tags must be well-formed.
8. Dates must be in YYYY-MM-DD format.
9. Boolean tags must be exactly true or false (lower-case, no quotes).  
   ✔ √ Yes Passed ⇒ true | ✘ X No Fail ⇒ false
10. **Inside each value**  
    • Replace every internal line-break with “, ” (comma + space).  
    • Trim leading/trailing whitespace.  
    • Escape XML special characters: & → &amp;, < → &lt;, > → &gt;.
11. **Phone / contact fields** – digits, “+”, “–”, spaces only; if multiple numbers, separate with “, ”.
12. **Signature fields** – fill ONLY if the signature appears as legible text; if it is handwritten, leave the tag empty.
13. Ignore any information not represented by the tags below.
<s_mawb_form>
  <air_waybill_number>Số MAWB</air_waybill_number>
  <shipper_name>Tên người gửi hàng</shipper_name>
  <shipper_address>Địa chỉ người gửi hàng</shipper_address>
  <shipper_account_number>Mã tài khoản người gửi</shipper_account_number>
  <consignee_name>Tên người nhận hàng</consignee_name>
  <consignee_address>Địa chỉ người nhận hàng</consignee_address>
  <consignee_account_number>Mã tài khoản người nhận</consignee_account_number>
  <dangerous_goods_note>Ghi chú hàng nguy hiểm (true or false)</dangerous_goods_note>
  <shipper_signature>Chữ ký người gửi</shipper_signature>
</s_mawb_form>
""",

    "Phiếu Cân": """Extract all structured information from the document 'PHIẾU CÂN / SHIPPER’S LETTER OF INSTRUCTIONS'.
You must return the result as a valid XML block that strictly follows the structure below.
STRICT INSTRUCTIONS – read carefully and follow EXACTLY:
1. Return **ONLY** the XML block – nothing before or after it.
2. DO NOT add, remove, rename, or reorder any XML tags.
3. DO NOT include explanations, markdown, notes, comments, or extra spacing outside the XML block.
4. For every tag, fill in the exact value read from the image.
   • NEVER copy or repeat the label/placeholder text.  
   • NEVER guess or invent values.
5. If a value is missing or unreadable, leave the tag EMPTY (e.g. <tag></tag>).
6. DO NOT include Vietnamese text or translations inside tag values.
7. The output MUST start with the root tag and end with its correct closing tag; all tags must be well-formed.
8. Dates must be in YYYY-MM-DD format.
9. Boolean tags must be exactly true or false (lower-case, no quotes).  
   ✔ √ Yes Passed ⇒ true | ✘ X No Fail ⇒ false
10. **Inside each value**  
    • Replace every internal line-break with “, ” (comma + space).  
    • Trim leading/trailing whitespace.  
    • Escape XML special characters: & → &amp;, < → &lt;, > → &gt;.
11. **Phone / contact fields** – digits, “+”, “–”, spaces only; if multiple numbers, separate with “, ”.
12. **Signature fields** – fill ONLY if the signature appears as legible text; if it is handwritten, leave the tag empty.
13. Ignore any information not represented by the tags below.
<s_weight_ticket>
  <awb_number>Số AWB</awb_number>
  <shipper_name>Tên người gửi hàng</shipper_name>
  <shipper_address>Địa chỉ người gửi hàng</shipper_address>
  <shipper_contact>Số điện thoại người gửi</shipper_contact>
  <consignee_name>Tên người nhận hàng</consignee_name>
  <consignee_address>Địa chỉ người nhận hàng</consignee_address>
  <cargo_description>Tên hàng hóa</cargo_description>
  <security_check_complete>Đã kiểm tra an ninh (true/false)</security_check_complete>
  <acceptance_staff_name>Tên nhân viên tiếp nhận</acceptance_staff_name>
  <acceptance_staff_signature>Chữ ký nhân viên tiếp nhận</acceptance_staff_signature>
</s_weight_ticket>
""",

    "PC 3U": """Extract all structured information from the PC 3U air cargo instruction document.
You must return the result as a valid XML block that strictly follows the structure below.
STRICT INSTRUCTIONS – read carefully and follow EXACTLY:
1. Return **ONLY** the XML block – nothing before or after it.
2. DO NOT add, remove, rename, or reorder any XML tags.
3. DO NOT include explanations, markdown, notes, comments, or extra spacing outside the XML block.
4. For every tag, fill in the exact value read from the image.
   • NEVER copy or repeat the label/placeholder text.  
   • NEVER guess or invent values.
5. If a value is missing or unreadable, leave the tag EMPTY (e.g. <tag></tag>).
6. DO NOT include Vietnamese text or translations inside tag values.
7. The output MUST start with the root tag and end with its correct closing tag; all tags must be well-formed.
8. Dates must be in YYYY-MM-DD format.
9. Boolean tags must be exactly true or false (lower-case, no quotes).  
   ✔ √ Yes Passed ⇒ true | ✘ X No Fail ⇒ false
10. **Inside each value**  
    • Replace every internal line-break with “, ” (comma + space).  
    • Trim leading/trailing whitespace.  
    • Escape XML special characters: & → &amp;, < → &lt;, > → &gt;.
11. **Phone / contact fields** – digits, “+”, “–”, spaces only; if multiple numbers, separate with “, ”.
12. **Signature fields** – fill ONLY if the signature appears as legible text; if it is handwritten, leave the tag empty.
13. Ignore any information not represented by the tags below.
<s_pc3u_form>
  <awb_number>Số AWB</awb_number>
  <cargo_service_code>Mã dịch vụ</cargo_service_code>
  <shipper_name>Tên người gửi</shipper_name>
  <shipper_address>Địa chỉ người gửi</shipper_address>
  <shipper_contact>Thông tin liên hệ người gửi</shipper_contact>
  <payer_name>Người thanh toán</payer_name>
  <payer_tax_code>Mã số thuế người thanh toán</payer_tax_code>
  <consignee_name>Tên người nhận</consignee_name>
  <consignee_address>Địa chỉ người nhận</consignee_address>
  <consignee_contact>Thông tin liên hệ người nhận</consignee_contact>
  <shipper_signature>Chữ ký người gửi</shipper_signature>
  <acceptance_staff_signature>Chữ ký nhân viên tiếp nhận</acceptance_staff_signature>
</s_pc3u_form>
""",

    "SLIS-AVS DAD": """Extract all structured information from the document 'TỜ KHAI GỬI HÀNG - SHIPPER’S LETTER OF INSTRUCTION'.
You must return the result as a valid XML block that strictly follows the structure below.
STRICT INSTRUCTIONS – read carefully and follow EXACTLY:
1. Return **ONLY** the XML block – nothing before or after it.
2. DO NOT add, remove, rename, or reorder any XML tags.
3. DO NOT include explanations, markdown, notes, comments, or extra spacing outside the XML block.
4. For every tag, fill in the exact value read from the image.
   • NEVER copy or repeat the label/placeholder text.  
   • NEVER guess or invent values.
5. If a value is missing or unreadable, leave the tag EMPTY (e.g. <tag></tag>).
6. DO NOT include Vietnamese text or translations inside tag values.
7. The output MUST start with the root tag and end with its correct closing tag; all tags must be well-formed.
8. Dates must be in YYYY-MM-DD format.
9. Boolean tags must be exactly true or false (lower-case, no quotes).  
   ✔ √ Yes Passed ⇒ true | ✘ X No Fail ⇒ false
10. **Inside each value**  
    • Replace every internal line-break with “, ” (comma + space).  
    • Trim leading/trailing whitespace.  
    • Escape XML special characters: & → &amp;, < → &lt;, > → &gt;.
11. **Phone / contact fields** – digits, “+”, “–”, spaces only; if multiple numbers, separate with “, ”.
12. **Signature fields** – fill ONLY if the signature appears as legible text; if it is handwritten, leave the tag empty.
13. Ignore any information not represented by the tags below.
<s_avs_dad>
  <air_waybill_number>Số AWB</air_waybill_number>
  <form_code>Mã biểu mẫu</form_code>
  <shipper_name>Tên người gửi</shipper_name>
  <shipper_address>Địa chỉ người gửi</shipper_address>
  <shipper_phone>Điện thoại người gửi</shipper_phone>
  <shipper_email>Email người gửi</shipper_email>
  <shipper_tax_code>Mã số thuế người gửi</shipper_tax_code>
  <consignee_name>Tên người nhận</consignee_name>
  <consignee_address>Địa chỉ người nhận</consignee_address>
  <consignee_phone>Điện thoại người nhận</consignee_phone>
  <consignee_email>Email người nhận</consignee_email>
  <departure_airport>Nơi đi</departure_airport>
  <destination_airport>Nơi đến</destination_airport>
  <acceptance_staff_name>Tên nhân viên tiếp nhận</acceptance_staff_name>
  <acceptance_signature>Chữ ký nhân viên tiếp nhận</acceptance_signature>
  <acceptance_time>Thời điểm tiếp nhận</acceptance_time>
  <shipper_signature>Chữ ký người gửi</shipper_signature>
  <shipper_signature_date>Ngày ký người gửi</shipper_signature_date>
</s_avs_dad>
"""
}

def insert_template(name):
    return prompt_templates.get(name, "")

def sanitize_filename(name):
    return re.sub(r'[^a-zA-Z0-9_\-\.]', '_', name)

def clean_text(text):
    text = re.sub(r'<[^<> ]+?>', lambda m: m.group(0).strip(), text)
    text = re.sub(r'<[^<>]+?>[^<>]*?<[^<>]+?>', lambda m: m.group(0).strip(), text)
    return text.strip()

def export_json(image_name, result_text):
    try:
        clean_name = sanitize_filename(image_name)
        content = {"image": image_name, "text_sequence": clean_text(result_text)}
        path = f"/tmp/{clean_name}.json"
        with open(path, "w", encoding="utf-8") as f:
            json.dump(content, f, ensure_ascii=False, indent=2)
        return path, json.dumps(content, ensure_ascii=False, indent=2)
    except Exception as e:
        return "", f"[Export JSON Failed]: {e}"

# --- 10. Gradio UI ---
# with gr.Blocks(title="Camel-Doc-OCR") as demo:
#     gr.Markdown("Camel-Doc-OCR (Qwen2.5-VL, 4-bit)")

#     status_txt = gr.Textbox(label="Status & Memory", interactive=False)
#     cache_txt = gr.Textbox(label="Cache Stats", interactive=False)
#     clear_btn = gr.Button("Clear Cache")
#     clear_btn.click(fn=lambda: (cache_clear(), f"Cache: {len(_mru_cache)}/{CACHE_MAX_SIZE}"), outputs=[cache_txt])

#     file_input = gr.File(label="Tải ảnh hoặc PDF", file_types=[".jpg", ".jpeg", ".png", ".pdf"])
#     prompt_input = gr.Textbox(label="Prompt thuần", lines=2)
#     config_input = gr.Textbox(label="JSON Prompt", lines=12)

#     gr.Markdown("Chọn mẫu prompt:")
#     with gr.Row():
#         # for key in prompt_templates:
#         #     btn = gr.Button(f"Mẫu {key}")
#         #     btn.click(fn=insert_template, inputs=[gr.State(key)], outputs=config_input)
#         for key in prompt_templates:
#             gr.Button(f"Mẫu {key}").click(
#                 fn=lambda k=key: insert_template(k),
#                 outputs=config_input
#             )


#     run_btn = gr.Button("Chạy OCR")
#     export_btn = gr.Button("Xuất JSON", visible=False)

#     hidden_name = gr.Textbox(visible=False)
#     result_output = gr.Textbox(label="Kết quả trích xuất", lines=20)
#     json_file = gr.File(label="File JSON", visible=False, file_types=[".json"])
#     json_text = gr.Code(label="JSON Output", language="json", lines=20)

#     # Run inference
#     run_btn.click(
#         fn=handle_file,
#         inputs=[file_input, prompt_input, config_input],
#         outputs=[hidden_name, result_output]
#     )

#     # Update memory status
#     run_btn.click(fn=lambda: get_memory_info(), outputs=[status_txt])
#     run_btn.click(fn=lambda: f"Cache: {len(_mru_cache)}/{CACHE_MAX_SIZE}", outputs=[cache_txt])
#     run_btn.click(fn=lambda: gr.update(visible=True), outputs=[export_btn])

#     # Export
#     export_btn.click(fn=export_json, inputs=[hidden_name, result_output], outputs=[json_file, json_text])
#     export_btn.click(fn=lambda: gr.update(visible=True), outputs=[json_file])
# --- 10. Gradio UI ---
css = """
.gradio-textbox textarea {
    font-size: 13px !important;
    line-height: 1.3 !important;
    padding: 6px 8px !important;
}
.gradio-textbox label {
    font-size: 13px !important;
    font-weight: 600 !important;
    margin-bottom: 4px !important;
}
.gradio-button {
    font-size: 12px !important;
    padding: 4px 8px !important;
    height: 28px !important;
    min-height: 28px !important;
    margin: 2px !important;
}
.gradio-button[data-variant="primary"] {
    height: 36px !important;
    font-size: 13px !important;
    padding: 8px 16px !important;
}
.gradio-file {
    font-size: 13px !important;
}
.gradio-file .file-upload {
    padding: 8px !important;
    min-height: 80px !important;
}
.gradio-markdown h3 {
    font-size: 14px !important;
    margin: 8px 0 4px 0 !important;
}
.gradio-markdown h2 {
    font-size: 18px !important;
    margin: 8px 0 !important;
}
.gradio-code {
    font-size: 12px !important;
}
"""

with gr.Blocks(title="Camel-Doc-OCR", css=css) as demo:
    gr.Markdown("## 🧾 Camel-Doc-OCR (Qwen2.5-VL, 4-bit)")
    
    # --- Status Bar (Full width) ---
    with gr.Row():
        status_txt = gr.Textbox(label="Status & Memory", interactive=False, scale=2)
        cache_txt = gr.Textbox(label="Cache Stats", interactive=False, scale=1)
        clear_btn = gr.Button("Clear Cache", scale=1)
        clear_btn.click(fn=lambda: (cache_clear(), f"Cache: {len(_mru_cache)}/{CACHE_MAX_SIZE}"), outputs=[cache_txt])
    
    # --- Main Layout: 2 Columns ---
    with gr.Row():
        # === LEFT COLUMN: Input ===
        with gr.Column(scale=1):
            gr.Markdown("### 📥 INPUT")
            
            # File Input
            file_input = gr.File(
                label="📤 Tải ảnh hoặc PDF", 
                file_types=[".jpg", ".jpeg", ".png", ".pdf"],
                height=100
            )
            
            # Prompt Input
            prompt_input = gr.Textbox(
                label="Prompt thuần", 
                lines=2,
                placeholder="Nhập prompt tùy chỉnh...",
                max_lines=3
            )
            
            # JSON Config
            config_input = gr.Textbox(
                label="JSON Prompt", 
                lines=6,
                placeholder="Cấu hình JSON sẽ xuất hiện ở đây...",
                max_lines=8
            )
            
            # Prompt Templates
            gr.Markdown("### 📑 Mẫu:")
            with gr.Row():
                for key in list(prompt_templates.keys()):  # All buttons in one row
                    gr.Button(f"{key}", size="sm", scale=1).click(
                        fn=lambda *, k=key: insert_template(k),
                        inputs=[],
                        outputs=config_input
                    )
            
            # Run Button
            run_btn = gr.Button("🚀 Chạy OCR", variant="primary")
            
        # === RIGHT COLUMN: Output ===
        with gr.Column(scale=1):
            gr.Markdown("### 📤 OUTPUT")
            
            # Result Output
            result_output = gr.Textbox(
                label="Kết quả trích xuất", 
                lines=10,
                placeholder="Kết quả sẽ hiển thị ở đây sau khi chạy OCR...",
                max_lines=12
            )
            
            # Export Section
            with gr.Row():
                export_btn = gr.Button("📦 Xuất JSON", visible=False, variant="secondary", size="sm")
                
            # JSON Output
            json_text = gr.Code(
                label="JSON Output", 
                language="json", 
                lines=6,
                visible=False
            )
            
            # Download File
            json_file = gr.File(
                label="File JSON để tải", 
                visible=False, 
                file_types=[".json"]
            )
    
    # --- Hidden Fields ---
    hidden_name = gr.Textbox(visible=False)
    
    # --- Event Handlers ---
    
    # Run Inference
    run_btn.click(
        fn=handle_file,
        inputs=[file_input, prompt_input, config_input],
        outputs=[hidden_name, result_output]
    )
    
    run_btn.click(fn=get_memory_info, outputs=[status_txt])
    run_btn.click(fn=lambda: f"Cache: {len(_mru_cache)}/{CACHE_MAX_SIZE}", outputs=[cache_txt])
    run_btn.click(fn=lambda: gr.update(visible=True), outputs=[export_btn])
    
    # Export JSON
    export_btn.click(
        fn=export_json,
        inputs=[hidden_name, result_output],
        outputs=[json_file, json_text]
    )
    
    export_btn.click(fn=lambda: gr.update(visible=True), outputs=[json_file])
    export_btn.click(fn=lambda: gr.update(visible=True), outputs=[json_text])

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