app.py

import gradio as gr
import torch
from transformers import AutoModel, AutoTokenizer
import spaces
import os
import tempfile
from PIL import Image, ImageDraw
import re # Import thư viện regular expression

# --- 1. Load Model and Tokenizer (Done only once at startup) ---
print("Loading model and tokenizer...")
model_name = "deepseek-ai/DeepSeek-OCR"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# Load the model to CPU first; it will be moved to GPU during processing
model = AutoModel.from_pretrained(
    model_name,
    _attn_implementation="flash_attention_2",
    trust_remote_code=True,
    use_safetensors=True,
)
model = model.eval()
print("✅ Model loaded successfully.")

# --- Helper function to find pre-generated result images ---
def find_result_image(path):
    for filename in os.listdir(path):
        if "grounding" in filename or "result" in filename:
            try:
                image_path = os.path.join(path, filename)
                return Image.open(image_path)
            except Exception as e:
                print(f"Error opening result image {filename}: {e}")
    return None

# --- 2. Main Processing Function (UPDATED for multi-bbox drawing) ---
@spaces.GPU
def process_ocr_task(image, model_size, task_type, ref_text):
    """
    Processes an image with DeepSeek-OCR for all supported tasks.
    Now draws ALL detected bounding boxes for ANY task.
    """
    if image is None:
        return "Please upload an image first.", None

    print("🚀 Moving model to GPU...")
    model_gpu = model.cuda().to(torch.bfloat16)
    print("✅ Model is on GPU.")

    with tempfile.TemporaryDirectory() as output_path:
        # Build the prompt... (same as before)
        if task_type == "📝 Free OCR":
            prompt = "<image>\nFree OCR."
        elif task_type == "📄 Convert to Markdown":
            prompt = "<image>\n<|grounding|>Convert the document to markdown."
        elif task_type == "📈 Parse Figure":
            prompt = "<image>\nParse the figure."
        else:
            prompt = "<image>\nFree OCR."

        temp_image_path = os.path.join(output_path, "temp_image.png")
        image.save(temp_image_path)

        # Configure model size... (same as before)
        size_configs = {
            "Tiny": {"base_size": 512, "image_size": 512, "crop_mode": False},
            "Small": {"base_size": 640, "image_size": 640, "crop_mode": False},
            "Base": {"base_size": 1024, "image_size": 1024, "crop_mode": False},
            "Large": {"base_size": 1280, "image_size": 1280, "crop_mode": False},
            "Gundam (Recommended)": {"base_size": 1024, "image_size": 640, "crop_mode": True},
        }
        config = size_configs.get(model_size, size_configs["Gundam (Recommended)"])

        print(f"🏃 Running inference with prompt: {prompt}")
        text_result = model_gpu.infer(
            tokenizer,
            prompt=prompt,
            image_file=temp_image_path,
            output_path=output_path,
            base_size=config["base_size"],
            image_size=config["image_size"],
            crop_mode=config["crop_mode"],
            save_results=True,
            test_compress=True,
            eval_mode=True,
        )

        print(f"====\n📄 Text Result: {text_result}\n====")
            
        return text_result

# --- 3. Build the Gradio Interface (UPDATED) ---
with gr.Blocks(title="🐳DeepSeek-OCR🐳", theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🐳 Full Demo of DeepSeek-OCR 🐳

        **💡 How to use:**
        1.  **Upload an image** using the upload box.
        2.  Select a **Resolution**. `Gundam` is recommended for most documents.
        3.  Choose a **Task Type**:
            - **📝 Free OCR**: Extracts raw text from the image.
            - **📄 Convert to Markdown**: Converts the document into Markdown, preserving structure.
            - **📈 Parse Figure**: Extracts structured data from charts and figures.
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="pil", label="🖼️ Upload Image", sources=["upload", "clipboard"])
            model_size = gr.Dropdown(choices=["Tiny", "Small", "Base", "Large", "Gundam (Recommended)"], value="Gundam (Recommended)", label="⚙️ Resolution Size")
            task_type = gr.Dropdown(choices=["📝 Free OCR", "📄 Convert to Markdown", "📈 Parse Figure"], value="📄 Convert to Markdown", label="🚀 Task Type")
            submit_btn = gr.Button("Process Image", variant="primary")

        with gr.Column(scale=2):
            output_text = gr.Textbox(label="📄 Text Result", lines=15, show_copy_button=True)
            output_image = gr.Image(label="🖼️ Image Result (if any)", type="pil")

# --- 4. Launch the App ---
if __name__ == "__main__":
    demo.queue(max_size=20).launch(share=True)