import spaces
import torch
import re
import gradio as gr
from threading import Thread
from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
from PIL import ImageDraw
from torchvision.transforms.v2 import Resize

import subprocess
# subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

model_id = "vikhyatk/moondream2"
revision = "2024-05-20"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
moondream = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, revision=revision,
    # torch_dtype=torch.bfloat16, device_map={"": "cuda"},
    torch_dtype=torch.float32, device_map="cpu"
    # attn_implementation="flash_attention_2"
)
moondream.eval()

control_vectors = torch.load("control_vectors.pt", map_location="cpu")
control_vectors = [t.to('cpu', dtype=torch.float32) for t in control_vectors]

class LayerWrapper(torch.nn.Module):
    def __init__(self, og_layer, control_vectors, scale=4.2):
        super().__init__()
        self.og_layer = og_layer
        self.control_vectors = control_vectors
        self.scale = scale

    def forward(self, *args, **kwargs):
        layer_outputs = self.og_layer(*args, **kwargs)
        layer_outputs = (layer_outputs[0] + self.scale * self.control_vectors, *layer_outputs[1:])
        return layer_outputs

moondream.text_model.transformer.h = torch.nn.ModuleList([
    LayerWrapper(layer, vector, 4.2)
    for layer, vector in zip(moondream.text_model.transformer.h, control_vectors)
])

@spaces.GPU(duration=10)
def answer_question(img, prompt):
    image_embeds = moondream.encode_image(img)
    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
    thread = Thread(
        target=moondream.answer_question,
        kwargs={
            "image_embeds": image_embeds,
            "question": prompt,
            "tokenizer": tokenizer,
            "streamer": streamer,
            "repetition_penalty": 1.2,
            "temperature": 0.1,
            "do_sample": True,
            "length_penalty": 1.2
        },
    )
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer.strip()

def extract_floats(text):
    # Regular expression to match an array of four floating point numbers
    pattern = r"\[\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*\]"
    match = re.search(pattern, text)
    if match:
        # Extract the numbers and convert them to floats
        return [float(num) for num in match.groups()]
    return None  # Return None if no match is found


def extract_bbox(text):
    bbox = None
    if extract_floats(text) is not None:
        x1, y1, x2, y2 = extract_floats(text)
        bbox = (x1, y1, x2, y2)
    return bbox

def process_answer(img, answer):
    if extract_bbox(answer) is not None:
        x1, y1, x2, y2 = extract_bbox(answer)
        draw_image = Resize(768)(img)
        width, height = draw_image.size
        x1, x2 = int(x1 * width), int(x2 * width)
        y1, y2 = int(y1 * height), int(y2 * height)
        bbox = (x1, y1, x2, y2)
        ImageDraw.Draw(draw_image).rectangle(bbox, outline="red", width=3)
        return gr.update(visible=True, value=draw_image)

    return gr.update(visible=False, value=None)

with gr.Blocks() as demo:
    gr.Markdown(
        """
        # 🌜 Just for Fun to discuss the meaning of life using [activation vectors]
        """
    )
    with gr.Row():
        prompt = gr.Textbox(label="Input", value="Describe this image.", scale=4)
        submit = gr.Button("Submit")
    with gr.Row():
        img = gr.Image(type="pil", label="Upload an Image")
        with gr.Column():
            output = gr.Markdown(label="Response")
            ann = gr.Image(visible=False, label="Annotated Image")

    submit.click(answer_question, [img, prompt], output)
    prompt.submit(answer_question, [img, prompt], output)
    output.change(process_answer, [img, output], ann, show_progress=False)

demo.queue().launch()