app.py

import gradio as gr
from transformers import AutoModel, AutoTokenizer
from medcrab import MedCrabTranslator
import torch
import os
import sys
import tempfile
import shutil
from PIL import Image, ImageOps
import fitz
import re
import time
from io import StringIO, BytesIO
import spaces

# ==================== CONFIG ====================
OCR_MODEL_NAME = 'deepseek-ai/DeepSeek-OCR'
MODEL_CONFIGS = {
    "Crab": {"base_size": 1024, "image_size": 640, "crop_mode": True},
    "Base": {"base_size": 1024, "image_size": 1024, "crop_mode": False},
}

# ==================== LOAD MODELS ====================
print("🔄 Loading OCR model...")
ocr_tokenizer = AutoTokenizer.from_pretrained(OCR_MODEL_NAME, trust_remote_code=True)
try:
    ocr_model = AutoModel.from_pretrained(
        OCR_MODEL_NAME,
        attn_implementation='flash_attention_2',
        torch_dtype=torch.bfloat16,
        trust_remote_code=True,
        use_safetensors=True
    )
    print("✅ Using Flash Attention 2")
except (ImportError, ValueError):
    print("⚠️ Flash Attention 2 not available, using eager attention")
    ocr_model = AutoModel.from_pretrained(
        OCR_MODEL_NAME,
        attn_implementation='eager',
        torch_dtype=torch.bfloat16,
        trust_remote_code=True,
        use_safetensors=True
    )
ocr_model = ocr_model.eval()

print("🦀 Loading MedCrab translator...")
device = "cuda" if torch.cuda.is_available() else "cpu"
translator = MedCrabTranslator(device=device)
print(f"✅ MedCrab translator loaded on {device}")

# ==================== TEXT CLEANING ====================
def clean_mathrm(text):
    if not text:
        return ""
    def process_math_block(match):
        math_content = match.group(1)
        math_content = re.sub(r'\\mathrm\{([^}]*)\}', r'\1', math_content)
        math_content = re.sub(r'\^\{([^}]+)\}', r'<sup>\1</sup>', math_content)
        math_content = re.sub(r'\^([A-Za-z0-9+\-]+)', r'<sup>\1</sup>', math_content)
        math_content = re.sub(r'_\{([^}]+)\}', r'<sub>\1</sub>', math_content)
        math_content = re.sub(r'_([A-Za-z0-9+\-]+)', r'<sub>\1</sub>', math_content)
        replacements = {
            r'\times': '×', r'\pm': '±', r'\div': '÷', r'\cdot': '·',
            r'\approx': '≈', r'\leq': '≤', r'\geq': '≥', r'\neq': '≠',
            r'\rightarrow': '→', r'\leftarrow': '←',
            r'\Rightarrow': '⇒', r'\Leftarrow': '⇐',
        }
        for latex_cmd, unicode_char in replacements.items():
            math_content = math_content.replace(latex_cmd, unicode_char)
        return math_content

    text = re.sub(r'\\\((.+?)\\\)', process_math_block, text, flags=re.DOTALL)
    def process_bracket_block(m):
        class FakeMatch:
            def __init__(self, content):
                self.content = content
            def group(self, n):
                return self.content
        content = process_math_block(FakeMatch(m.group(1)))
        return '[' + content + ']'
    text = re.sub(r'\\\[(.+?)\\\]', process_bracket_block, text, flags=re.DOTALL)
    text = re.sub(r'\\mathrm\{([^}]*)\}', r'\1', text)
    text = text.replace(r'\%', '%')
    lines = text.split('\n')
    cleaned_lines = [re.sub(r'[ \t]+', ' ', line).strip() for line in lines]
    return '\n'.join(cleaned_lines).strip()

def clean_output(text, include_images=False, remove_labels=False):
    if not text:
        return ""
    pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
    matches = re.findall(pattern, text, re.DOTALL)
    img_num = 0
    for match in matches:
        if '<|ref|>image<|/ref|>' in match[0]:
            if include_images:
                text = text.replace(match[0], f'\n\n**[Figure {img_num + 1}]**\n\n', 1)
                img_num += 1
            else:
                text = text.replace(match[0], '', 1)
        else:
            if remove_labels:
                text = text.replace(match[0], '', 1)
            else:
                text = text.replace(match[0], match[1], 1)
    return clean_mathrm(text).strip()

# ==================== OCR HELPERS ====================
@spaces.GPU
def ocr_process_image(image, mode="Crab"):
    if image is None:
        return "Error: Upload image"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    ocr_model.to(device)
    if image.mode in ('RGBA', 'LA', 'P'):
        image = image.convert('RGB')
    image = ImageOps.exif_transpose(image)
    config = MODEL_CONFIGS[mode]
    prompt = "<image>\n<|grounding|>Convert the document to markdown."
    tmp = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
    image.save(tmp.name, 'JPEG', quality=95)
    tmp.close()
    out_dir = tempfile.mkdtemp()
    stdout = sys.stdout
    sys.stdout = StringIO()
    try:
        ocr_model.infer(
            tokenizer=ocr_tokenizer,
            prompt=prompt,
            image_file=tmp.name,
            output_path=out_dir,
            base_size=config["base_size"],
            image_size=config["image_size"],
            crop_mode=config["crop_mode"]
        )
        result = '\n'.join([l for l in sys.stdout.getvalue().split('\n')
                            if not any(s in l for s in ['image:', 'other:', 'PATCHES', '====', 'BASE:', '%|', 'torch.Size'])]).strip()
    finally:
        sys.stdout = stdout
        try:
            os.unlink(tmp.name)
        except:
            pass
        shutil.rmtree(out_dir, ignore_errors=True)
    if not result:
        return "No text detected"
    return clean_output(result, True, True)

def ocr_process_pdf(path, mode, page_num):
    doc = fitz.open(path)
    total_pages = len(doc)
    if page_num < 1 or page_num > total_pages:
        doc.close()
        return f"Invalid page number. PDF has {total_pages} pages."
    page = doc.load_page(page_num - 1)
    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72), alpha=False)
    img = Image.open(BytesIO(pix.tobytes("png")))
    doc.close()
    return ocr_process_image(img, mode)

def ocr_process_file(path, mode, page_num):
    if not path:
        return "Error: Upload file"
    if path.lower().endswith('.pdf'):
        return ocr_process_pdf(path, mode, page_num)
    else:
        return ocr_process_image(Image.open(path), mode)

# ==================== TRANSLATION HELPERS ====================
def split_by_sentences(text: str, max_words: int = 100):
    def count_words(t):
        return len(t.strip().split())
    chunks = []
    lines = text.split('\n')
    i = 0
    while i < len(lines):
        line = lines[i]
        empty_count = 0
        if not line.strip():
            while i < len(lines) and not lines[i].strip():
                empty_count += 1
                i += 1
            if chunks:
                prev_text, prev_newlines = chunks[-1]
                chunks[-1] = (prev_text, prev_newlines + empty_count)
            continue
        line = line.strip()
        is_last_line = (i == len(lines) - 1)
        if count_words(line) <= max_words:
            chunks.append((line, 0 if is_last_line else 1))
            i += 1
            continue
        sentences = re.split(r'(?<=[.!?])\s+', line)
        current_chunk = ""
        current_words = 0
        for sentence in sentences:
            sentence = sentence.strip()
            if not sentence:
                continue
            sentence_words = count_words(sentence)
            if sentence_words > max_words:
                if current_chunk:
                    chunks.append((current_chunk.strip(), 0))
                    current_chunk = ""
                    current_words = 0
                sub_parts = re.split(r',\s*', sentence)
                temp_chunk = ""
                temp_words = 0
                for part in sub_parts:
                    part_words = count_words(part)
                    if temp_words + part_words > max_words and temp_chunk:
                        chunks.append((temp_chunk.strip(), 0))
                        temp_chunk = part
                        temp_words = part_words
                    else:
                        if temp_chunk:
                            temp_chunk += ", " + part
                        else:
                            temp_chunk = part
                        temp_words += part_words
                if temp_chunk.strip():
                    current_chunk = temp_chunk.strip()
                    current_words = temp_words
            elif current_words + sentence_words <= max_words:
                if current_chunk:
                    current_chunk += " " + sentence
                else:
                    current_chunk = sentence
                current_words += sentence_words
            else:
                chunks.append((current_chunk.strip(), 0))
                current_chunk = sentence
                current_words = sentence_words
        if current_chunk.strip():
            chunks.append((current_chunk.strip(), 0 if is_last_line else 1))
        i += 1
    return chunks

@spaces.GPU
def translate_chunk(chunk_text):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    if hasattr(translator, 'model') and hasattr(translator.model, 'to'):
        translator.model.to(device)
    return translator.translate(chunk_text, max_new_tokens=2048).strip()

def streaming_translate(text: str):
    if not text or not text.strip():
        yield '<div style="padding:20px; color:#ff6b6b;">⚠️ Vui lòng nhập văn bản tiếng Anh để dịch.</div>'
        return
    chunks = split_by_sentences(text, max_words=100)
    accumulated = ""
    for i, (chunk_text, newline_count) in enumerate(chunks):
        try:
            translated = translate_chunk(chunk_text)
            if accumulated and not accumulated.endswith('\n'):
                accumulated += " " + translated
            else:
                accumulated += translated
            chunk_start = len(accumulated) - len(translated)
            for j in range(len(translated)):
                current_display = accumulated[:chunk_start + j + 1]
                html_output = f'<div style="padding:20px; line-height:1.8; font-size:15px; white-space:pre-wrap; font-family:Arial,sans-serif;">{current_display}</div>'
                yield html_output
                time.sleep(0.015)
            if newline_count > 0:
                actual_newlines = min(newline_count, 2)
                accumulated += "\n" * actual_newlines
                html_output = f'<div style="padding:20px; line-height:1.8; font-size:15px; white-space:pre-wrap; font-family:Arial,sans-serif;">{accumulated}</div>'
                yield html_output
        except Exception as e:
            yield f'<div style="padding:20px; color:#ff6b6b;">❌ Lỗi dịch chunk {i+1}: {str(e)}</div>'
            return

# ==================== UI HELPERS ====================
def load_image(file_path, page_num_str="1"):
    if not file_path:
        return None
    try:
        try:
            page_num = int(page_num_str)
        except (ValueError, TypeError):
            page_num = 1
        if file_path.lower().endswith('.pdf'):
            doc = fitz.open(file_path)
            page_idx = max(0, min(page_num - 1, len(doc) - 1))
            page = doc.load_page(page_idx)
            pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72), alpha=False)
            img = Image.open(BytesIO(pix.tobytes("png")))
            doc.close()
            return img
        else:
            return Image.open(file_path)
    except Exception as e:
        print(f"Error loading image: {e}")
        return None

def get_pdf_page_count(file_path):
    if not file_path or not file_path.lower().endswith('.pdf'):
        return 1
    try:
        doc = fitz.open(file_path)
        count = len(doc)
        doc.close()
        return count
    except Exception as e:
        print(f"Error reading PDF page count: {e}")
        return 1

def update_page_info(file_path):
    if not file_path:
        return gr.update(label="Số trang (chỉ dùng cho PDF, mặc định: 1)")
    if file_path.lower().endswith('.pdf'):
        page_count = get_pdf_page_count(file_path)
        return gr.update(
            label=f"Số trang (PDF có {page_count} trang, nhập 1-{page_count})",
            value="1"
        )
    return gr.update(
        label="Số trang (chỉ dùng cho PDF, mặc định: 1)",
        value="1"
    )

# ==================== COMBINED OCR + TRANSLATION ====================
def ocr_and_translate_streaming(file_path, mode, page_num_str):
    if not file_path:
        yield '<div style="padding:20px; color:#ff6b6b;">⚠️ Vui lòng tải file lên trước!</div>'
        return
    yield '<div style="padding:20px; color:#4CAF50;">🔍 Đang quét OCR...</div>'
    try:
        try:
            page_num = int(page_num_str)
        except (ValueError, TypeError):
            page_num = 1
        markdown = ocr_process_file(file_path, mode, page_num)
        if not markdown or markdown.startswith("Error") or markdown.startswith("Invalid"):
            yield f'<div style="padding:20px; color:#ff6b6b;">❌ Lỗi OCR: {markdown}</div>'
            return
    except Exception as e:
        yield f'<div style="padding:20px; color:#ff6b6b;">❌ Lỗi OCR: {str(e)}</div>'
        return
    yield '<div style="padding:20px; color:#2196F3;">🦀 Đang dịch...</div>'
    time.sleep(0.5)
    try:
        yield from streaming_translate(markdown)
    except Exception as e:
        yield f'<div style="padding:20px; color:#ff6b6b;">❌ Lỗi dịch: {str(e)}</div>'

# ==================== GRADIO INTERFACE ====================
def load_default_example():
    src = "images/example1.png"
    if not os.path.exists(src):
        # fallback: return empty values
        return None, None
    tmp_path = "/tmp/example1.png"
    try:
        shutil.copy(src, tmp_path)
    except Exception:
        # if copy fails, try to use src directly
        tmp_path = src
    img = Image.open(tmp_path)
    return tmp_path, img

with gr.Blocks(theme=gr.themes.Soft(), title="MedCrab Translation") as demo:
    gr.Markdown("""
    <div style="text-align: center;">
    <h1>🦀 MedCrab Translation</h1>
    <p style="font-size: 18px;"><b>Quét PDF Y khoa → Dịch trực tiếp sang tiếng Việt (Streaming)</b></p>
    
    <p style="font-size: 15px;">
    <b>Model:</b> 
    <a href="https://huggingface.co/pnnbao-ump/MedCrab-1.5B" target="_blank">
    https://huggingface.co/pnnbao-ump/MedCrab-1.5B
    </a>
    </p>
    
    <p style="font-size: 15px;">
    <b>Dataset:</b> 
    <a href="https://huggingface.co/datasets/pnnbao-ump/MedCrab" target="_blank">
    https://huggingface.co/datasets/pnnbao-ump/MedCrab
    </a>
    </p>
    
    <p style="font-size: 15px;">
    <b>GitHub Repository:</b> 
    <a href="https://github.com/pnnbao97/MedCrab" target="_blank">
    https://github.com/pnnbao97/MedCrab
    </a>
    </p>
    
    <p style="font-size: 15px;">
    <b>Tác giả:</b> Phạm Nguyễn Ngọc Bảo
    </p>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 📤 Tải file lên")
            file_in = gr.File(label="PDF hoặc Hình ảnh", file_types=["image", ".pdf"], type="filepath")
            input_img = gr.Image(label="Xem trước", type="pil", height=300)
            page_input = gr.Textbox(label="Số trang (chỉ dùng cho PDF, mặc định: 1)", value="1", placeholder="Nhập số trang...")
            mode = gr.Dropdown(list(MODEL_CONFIGS.keys()), value="Crab", label="Chế độ OCR")
            gr.Markdown("### 🦀 Quét và Dịch")
            process_btn = gr.Button("🚀 Quét OCR + Dịch tiếng Việt", variant="primary", size="lg")
        with gr.Column(scale=2):
            gr.Markdown("### 📄 Kết quả dịch tiếng Việt (Streaming)")
            translation_output = gr.HTML(label="", value="")

    with gr.Accordion("📚 Ví dụ mẫu", open=True):
        gr.Markdown("**Thử ngay với các ví dụ có sẵn:**")
        gr.Examples(
            examples=[
                ["images/example1.png", "Crab", "1"],
                ["images/example2.png", "Crab", "1"],
            ],
            inputs=[file_in, mode, page_input],
            outputs=[translation_output],
            fn=ocr_and_translate_streaming,
            cache_examples=False,
            label="Nhấp vào ví dụ để thử"
        )

    with gr.Accordion("⚖️ Giấy phép & Liên hệ", open=False): 
        gr.Markdown("""
        ### ⚖️ Giấy phép sử dụng
        
        MedCrab được phát hành theo giấy phép:  
        **Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0)**
        
        ### ✅ Được phép
        - Sử dụng cho mục đích cá nhân  
        - Nghiên cứu học thuật  
        - Giảng dạy, học tập, minh họa  
        - Thử nghiệm nội bộ (không phục vụ vận hành thực tế)
        
        ### ❌ Không được phép
        - Sử dụng cho mục đích thương mại dưới mọi hình thức  
        - Tích hợp vào hệ thống sản xuất (production system)  
        - Triển khai tại bệnh viện, phòng khám, cơ sở y tế  
        - Cung cấp như một dịch vụ trả phí / SaaS  
        - Bán lại, cho thuê, nhượng quyền phần mềm
        
        ### 💼 Nhu cầu sử dụng thương mại
        Nếu bạn đại diện cho:
        - Bệnh viện / phòng khám  
        - Công ty công nghệ y tế  
        - Viện nghiên cứu có hoạt động thương mại hóa  
        - Startup, doanh nghiệp triển khai sản phẩm y tế  
        
        Vui lòng liên hệ trực tiếp tác giả để trao đổi về **giấy phép thương mại**:
        
        👤 **Phạm Nguyễn Ngọc Bảo**  
        📧 Facebook: https://www.facebook.com/bao.phamnguyenngoc.5/
        
        ---
        
        ⚠️ **Lưu ý pháp lý:**  
        Phần mềm này chỉ phục vụ cho mục đích nghiên cứu và tham khảo,  
        **không thay thế cho chẩn đoán hoặc quyết định y khoa.**
        """)
    
    # Events
    file_in.change(load_image, [file_in, page_input], [input_img])
    file_in.change(update_page_info, [file_in], [page_input])
    page_input.change(load_image, [file_in, page_input], [input_img])
    process_btn.click(ocr_and_translate_streaming, [file_in, mode, page_input], [translation_output])

    # Load default example into both file_in (filepath) and input_img (PIL) when UI starts
    demo.load(
        load_default_example,
        inputs=None,
        outputs=[file_in, input_img]
    )

if __name__ == "__main__":
    print("🚀 Starting MedCrab Translation on Hugging Face Spaces...")
    demo.queue(max_size=20).launch()