import svetoviz_webgpu as sv

tokenizer = GPT2Tokenizer.from_pretrained('gpt2', device_map="cpu")
model = GPT2LMHeadModel.from_pretrained('gpt2', device_map="cpu")

def terminal_callback(buffer, message, images, files):
    text = message  # "Replace me by any text you'd like."
    buffer.send_user_message(text)
    inputs = tokenizer(text, return_tensors="pt")

    # Generate continuation
    outputs = model.generate(**inputs, max_new_tokens=50)

    # Decode to string
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(decoded)
    buffer.send_system_message(decoded)

sv.model(
    model=model,
    tokenizer=tokenizer,
    terminal_callback=terminal_callback
)

import svetoviz_webgpu as sv

image_processor = DetrImageProcessor.from_pretrained(
    "facebook/detr-resnet-50",
    revision="no_timm",
    device_map="cpu")
model = DetrForObjectDetection.from_pretrained(
    "facebook/detr-resnet-50",
    revision="no_timm",
    device_map="cpu")

def terminal_callback(buffer, message, images, files):
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    inputs = image_processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    buffer.send_system_message("processed")

sv.model(
    model=model,
    image_processor=image_processor,
    terminal_callback=terminal_callback
)

import svetoviz_webgpu as sv

model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny', device_map="cpu")
image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny", device_map="cpu")

def terminal_callback(buffer, message, images, files):
    image = Image.open("sample_image.jpg").convert("RGB")
    inputs = image_processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    buffer.send_system_message("processed")

sv.model(
    model=model,
    image_processor=image_processor,
    terminal_callback=terminal_callback
)