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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()
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