import gradio as gr
import torch
from transformers import AutoModel, AutoTokenizer
import os
import tempfile
from PIL import Image, ImageDraw
import re

# --- 1. Load Model and Tokenizer (CPU only) ---
print("Loading model and tokenizer on CPU...")
model_name = "deepseek-ai/DeepSeek-OCR"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Load model directly to CPU without flash_attention_2 (GPU-only feature)
model = AutoModel.from_pretrained(
    model_name,
    trust_remote_code=True,
    use_safetensors=True,
    torch_dtype=torch.float32  # Use float32 for CPU
)
model = model.eval()
print("✅ Model loaded successfully on CPU.")

# --- 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 (CPU version) ---
def process_ocr_task(image, model_size, task_type, ref_text):
    """
    Processes an image with DeepSeek-OCR for all supported tasks.
    CPU-only version without GPU decorators.
    """
    if image is None:
        return "Please upload an image first.", None

    print("🚀 Processing on CPU...")

    with tempfile.TemporaryDirectory() as output_path:
        # Build the prompt
        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."
        elif task_type == "🔍 Locate Object by Reference":
            if not ref_text or ref_text.strip() == "":
                raise gr.Error("For the 'Locate' task, you must provide the reference text to find!")
            prompt = f"<image>\nLocate <|ref|>{ref_text.strip()}<|/ref|> in the image."
        else:
            prompt = "<image>\nFree OCR."

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

        # Configure model size
        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}")
        
        # Run inference on CPU (model is already on CPU)
        text_result = model.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====")

        # Try to find and draw all bounding boxes
        result_image_pil = None
        
        # Pattern to find coordinates like [[280, 15, 696, 997]]
        pattern = re.compile(r"<\|det\|>\[\[(\d+),\s*(\d+),\s*(\d+),\s*(\d+)\]\]<\|/det\|>")
        matches = list(pattern.finditer(text_result))

        if matches:
            print(f"✅ Found {len(matches)} bounding box(es). Drawing on the original image.")
            
            # Create a copy of the original image to draw on
            image_with_bboxes = image.copy()
            draw = ImageDraw.Draw(image_with_bboxes)
            w, h = image.size

            for match in matches:
                # Extract coordinates as integers
                coords_norm = [int(c) for c in match.groups()]
                x1_norm, y1_norm, x2_norm, y2_norm = coords_norm
                
                # Scale normalized coordinates to actual image size
                x1 = int(x1_norm / 1000 * w)
                y1 = int(y1_norm / 1000 * h)
                x2 = int(x2_norm / 1000 * w)
                y2 = int(y2_norm / 1000 * h)
                
                # Draw rectangle with red outline
                draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
            
            result_image_pil = image_with_bboxes
        else:
            print("⚠️ No bounding box coordinates found. Falling back to search for result image file.")
            result_image_pil = find_result_image(output_path)
            
        return text_result, result_image_pil


# --- 3. Build the Gradio Interface ---
with gr.Blocks(title="🐳DeepSeek-OCR (CPU)🐳", theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🐳 DeepSeek-OCR (CPU Version) 🐳
        **⚠️ Note: Running on CPU - processing will be slower than GPU version**
        
        **💡 How to use:**
        1.  **Upload an image** using the upload box.
        2.  Select a **Resolution**. Start with `Tiny` or `Small` for faster CPU processing.
        3.  Choose a **Task Type**:
            - **📝 Free OCR**: Extracts raw text from the image.
            - **📄 Convert to Markdown**: Converts the document into Markdown.
            - **📈 Parse Figure**: Extracts structured data from charts.
            - **🔍 Locate Object by Reference**: Finds a specific object/text.
        4. If this helpful, please give it a like! 🙏 ❤️
        """
    )

    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="Small",  # Default to Small for faster CPU processing
                label="⚙️ Resolution Size"
            )
            task_type = gr.Dropdown(
                choices=["📝 Free OCR", "📄 Convert to Markdown", "📈 Parse Figure", "🔍 Locate Object by Reference"], 
                value="📄 Convert to Markdown", 
                label="🚀 Task Type"
            )
            ref_text_input = gr.Textbox(
                label="📝 Reference Text (for Locate task)", 
                placeholder="e.g., the teacher, 20-10, a red car...", 
                visible=False
            )
            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")

    # UI Interaction Logic
    def toggle_ref_text_visibility(task):
        return gr.Textbox(visible=True) if task == "🔍 Locate Object by Reference" else gr.Textbox(visible=False)

    task_type.change(fn=toggle_ref_text_visibility, inputs=task_type, outputs=ref_text_input)
    submit_btn.click(
        fn=process_ocr_task, 
        inputs=[image_input, model_size, task_type, ref_text_input], 
        outputs=[output_text, output_image]
    )

    # Examples
    gr.Examples(
        examples=[
            ["doc_markdown.png", "Small", "📄 Convert to Markdown", ""],
            ["chart.png", "Small", "📈 Parse Figure", ""],
            ["teacher.jpg", "Tiny", "🔍 Locate Object by Reference", "the teacher"],
            ["math_locate.jpg", "Tiny", "🔍 Locate Object by Reference", "20-10"],
            ["receipt.jpg", "Small", "📝 Free OCR", ""],
        ],
        inputs=[image_input, model_size, task_type, ref_text_input],
        outputs=[output_text, output_image],
        fn=process_ocr_task,
        cache_examples=False,
    )

# --- 4. Launch the App ---
if __name__ == "__main__":
    if not os.path.exists("examples"):
        os.makedirs("examples")
    
    demo.queue(max_size=5).launch(share=True)  # Reduced queue size for CPU