import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor, CLIPModel, BlipForConditionalGeneration, CLIPProcessor, BlipProcessor
from qwen_vl_utils import process_vision_info
import torch
import base64
from PIL import Image, ImageDraw
from io import BytesIO
import re


models = {
    "Qwen/Qwen2-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"),
    "Qwen/Qwen2-VL-2B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"),
                                                                                  torch_dtype="auto", device_map="auto"),
    "openai/clip-vit-base-patch32": CLIPModel.from_pretrained("openai/clip-vit-base-patch32"),
    "Salesforce/blip-image-captioning-base": BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
}

processors = {
    "Qwen/Qwen2-VL-7B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct"),
    "Qwen/Qwen2-VL-2B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct"),
     "openai/clip-vit-base-patch32": CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"),
    "Salesforce/blip-image-captioning-base": BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
}


def image_to_base64(image):
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
    draw = ImageDraw.Draw(image)
    for box in bounding_boxes:
        xmin, ymin, xmax, ymax = box
        draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
    return image


def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
    x_scale = original_width / scaled_width
    y_scale = original_height / scaled_height
    rescaled_boxes = []
    for box in bounding_boxes:
        xmin, ymin, xmax, ymax = box
        rescaled_box = [
            xmin * x_scale,
            ymin * y_scale,
            xmax * x_scale,
            ymax * y_scale
        ]
        rescaled_boxes.append(rescaled_box)
    return rescaled_boxes


@spaces.GPU
def run_example(image, text_input, system_prompt, model_id="Qwen/Qwen2-VL-2B-Instruct"):
    model = models[model_id].eval()
    processor = processors[model_id]

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
                {"type": "text", "text": system_prompt},
                {"type": "text", "text": text_input},
            ],
        }
    ]

    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    generated_ids = model.generate(**inputs, max_new_tokens=128)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print(output_text)
    pattern = r'\[\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\]'
    matches = re.findall(pattern, str(output_text))
    parsed_boxes = [[int(num) for num in match] for match in matches]
    scaled_boxes = rescale_bounding_boxes(parsed_boxes, image.width, image.height)
    return output_text

css = """
  #output {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""
with gr.Blocks(css=css) as demo:
    gr.Markdown(
    """
    # Qwen2-VL Object Detection Demo
    Use the Qwen2-VL models to detect objects in an image. The 7B variant seems to work much better.
    **Usage**: Use the keyword "detect" and a description of the target (see examples below).
    """)
    with gr.Tab(label="Qwen2-VL Input"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label="Input Image", type="pil")
                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="Qwen/Qwen2-VL-7B-Instruct")
                #system_prompt = gr.Textbox(label="System Prompt", value=default_system_prompt)
                text_input = gr.Textbox(label="User Prompt")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                model_output_text = gr.Textbox(label="Model Output Text")
                #parsed_boxes = gr.Textbox(label="Parsed Boxes")
                #annotated_image = gr.Image(label="Annotated Image")

        gr.Examples(
            examples=[
                ["assets/2024_09_10_10_58_23.png", "Solve the question"],
                ["assets/2024_09_10_10_58_40.png", "Solve the question"],
                ["assets/2024_09_10_11_07_31.png", "Solve the question"],
                ["assets/comics.jpeg", "Describe the scene"],
                ["assets/rescaled_IMG_3644.PNG", "Describe the scene"],
                ["assets/rescaled_IMG_4028.PNG", "Describe the scene"]
            ],
            inputs=[input_img, text_input],
            outputs=[model_output_text],
            fn=run_example,
            cache_examples=True,
            label="Try examples"
        )

        submit_btn.click(run_example, [input_img, text_input, model_selector], [model_output_text])

demo.launch(debug=True)