app.py

import os
import base64
import json
import ast
import re
from io import BytesIO
import types
import sys

# Force CPU-only & disable bitsandbytes CUDA checks in this environment
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
os.environ.setdefault("BITSANDBYTES_NOWELCOME", "1")
os.environ.setdefault("BITSANDBYTES_DISABLE_CUDA_CHECK", "1")

import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

# Stub bitsandbytes to avoid GPU driver checks in CPU-only environments
fake_bnb = types.ModuleType("bitsandbytes")
def _bnb_unavailable(*args, **kwargs):
    raise ImportError("bitsandbytes is not available in this CPU-only deployment")
fake_bnb.__all__ = ["_bnb_unavailable"]
fake_bnb._bnb_unavailable = _bnb_unavailable
sys.modules["bitsandbytes"] = fake_bnb

from transformers import AutoModel, AutoTokenizer


app = FastAPI(title="CCCD OCR with Vintern-1B-v2")


MODEL_NAME = "5CD-AI/Vintern-1B-v2"
# Force CPU-only to avoid NVIDIA driver / CUDA issues on Spaces
DEVICE = "cpu"
DTYPE = torch.float32


print(f"Loading model `{MODEL_NAME}` on {DEVICE} ...")
tokenizer = AutoTokenizer.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
    use_fast=False,
)
model = AutoModel.from_pretrained(
    MODEL_NAME,
    torch_dtype=DTYPE,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
)
model.eval().to(DEVICE)

generation_config = dict(
    max_new_tokens=512,
    do_sample=False,
    num_beams=3,
    repetition_penalty=3.5,
)


# =========================
# Image preprocessing (from notebook)
# =========================
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size: int):
    mean, std = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose(
        [
            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(mean=mean, std=std),
        ]
    )
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    target_ratios = set(
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    )
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size
    )

    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image_from_base64(base64_string: str, input_size=448, max_num=12):
    if base64_string.startswith("data:image"):
        base64_string = base64_string.split(",", 1)[1]

    image_data = base64.b64decode(base64_string)
    image = Image.open(BytesIO(image_data)).convert("RGB")
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(
        image, image_size=input_size, use_thumbnail=True, max_num=max_num
    )
    pixel_values = [transform(img) for img in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


# =========================
# Prompt & helpers
# =========================
PROMPT = """<image>
Bạn là hệ thống OCR + trích xuất dữ liệu từ ảnh Căn cước công dân (CCCD) Việt Nam.
Nhiệm vụ: đọc đúng chữ trên thẻ và trả về CHỈ 1 đối tượng JSON theo schema quy định.

QUY TẮC BẮT BUỘC:
1) Chỉ trả về JSON thuần (không markdown, không giải thích, không thêm ký tự nào ngoài JSON).
2) Chỉ được có đúng 5 khóa sau (đúng chính tả, đúng chữ thường, có dấu gạch dưới):
   - "so_no"
   - "ho_va_ten"
   - "ngay_sinh"
   - "que_quan"
   - "noi_thuong_tru"
   Không được thêm bất kỳ khóa nào khác.
3) Mapping trường (lấy theo NHÃN in trên thẻ, không lấy từ QR):
   - so_no: lấy giá trị ngay sau nhãn "Số / No." (hoặc "Số/No.").
   - ho_va_ten: lấy giá trị ngay sau nhãn "Họ và tên / Full name".
   - ngay_sinh: lấy giá trị ngay sau nhãn "Ngày sinh / Date of birth"; nếu có định dạng dd/mm/yyyy thì giữ đúng dd/mm/yyyy.
   - que_quan: lấy giá trị ngay sau nhãn "Quê quán / Place of origin".
   - noi_thuong_tru: lấy giá trị ngay sau nhãn "Nơi thường trú / Place of residence".
4) Nếu trường nào không đọc được rõ/chắc chắn: đặt null. Không được suy đoán.
5) Chuẩn hoá: trim khoảng trắng đầu/cuối; giữ nguyên dấu tiếng Việt và chữ hoa/thường như trong ảnh.

CHỈ TRẢ VỀ THEO MẪU JSON NÀY:
{
  "so_no": "... hoặc null",
  "ho_va_ten": "... hoặc null",
  "ngay_sinh": "... hoặc null",
  "que_quan": "... hoặc null",
  "noi_thuong_tru": "... hoặc null"
}
"""


def parse_response_to_json(response_text: str):
    if not response_text:
        return None

    s = response_text.strip()

    if s.startswith('"') and s.endswith('"'):
        s = s[1:-1].replace('\\"', '"')

    try:
        obj = json.loads(s)
        if isinstance(obj, dict):
            return obj
    except json.JSONDecodeError:
        pass

    try:
        obj = ast.literal_eval(s)
        if isinstance(obj, dict):
            return obj
    except (ValueError, SyntaxError):
        pass

    json_pattern = r"\{[\s\S]*\}"
    m = re.search(json_pattern, s)
    if m:
        chunk = m.group(0).strip()
        try:
            obj = ast.literal_eval(chunk)
            if isinstance(obj, dict):
                return obj
        except Exception:
            pass
        try:
            chunk2 = chunk.replace("'", '"')
            obj = json.loads(chunk2)
            if isinstance(obj, dict):
                return obj
        except Exception:
            pass

    return {"text": response_text}


def normalize_base64(image_base64: str) -> str:
    if not image_base64:
        return image_base64
    image_base64 = image_base64.strip()
    if image_base64.startswith("data:"):
        parts = image_base64.split(",", 1)
        if len(parts) == 2:
            return parts[1]
    return image_base64


def ocr_by_llm(image_base64: str, prompt: str) -> str:
    pixel_values = load_image_from_base64(image_base64, max_num=6)
    pixel_values = pixel_values.to(dtype=torch.float32, device=DEVICE)
    with torch.no_grad():
        response_message = model.chat(
            tokenizer,
            pixel_values,
            prompt,
            generation_config,
        )
    del pixel_values
    return response_message


class OCRRequest(BaseModel):
    image_base64: str


@app.post("/ocr")
def ocr_endpoint(req: OCRRequest):
    image_base64 = normalize_base64(req.image_base64)
    if not image_base64:
        raise HTTPException(status_code=400, detail="image_base64 is required")

    try:
        response_message = ocr_by_llm(image_base64, PROMPT)
        parsed = parse_response_to_json(response_message)
        return {"response_message": parsed}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.on_event("startup")
async def startup_log():
    """
    Log basic information about available endpoints when the app starts.
    """
    print("============================================")
    print("CCCD OCR API is running")
    print("Main endpoint:  POST /ocr")
    print("Docs (Swagger): GET  /docs")
    print("Redoc:          GET  /redoc")
    print("============================================")