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import sys
from threading import Thread
import gradio as gr
import torch
from transformers import AutoModel, AutoProcessor
from transformers import StoppingCriteria, TextIteratorStreamer, StoppingCriteriaList
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True)
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [151645]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
@torch.no_grad()
def response(message, history, image):
stop = StopOnTokens()
messages = [{"role": "system", "content": "You are a helpful assistant."}]
for user_msg, assistant_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
if len(messages) == 1:
message = f" <image>{message}"
messages.append({"role": "user", "content": message})
model_inputs = processor.tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
)
image = (
processor.feature_extractor(image)
.unsqueeze(0)
)
attention_mask = torch.ones(
1, model_inputs.shape[1] + processor.num_image_latents - 1
)
model_inputs = {
"input_ids": model_inputs,
"images": image,
"attention_mask": attention_mask
}
model_inputs = {k: v.to(device) for k, v in model_inputs.items()}
streamer = TextIteratorStreamer(processor.tokenizer, timeout=30., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
history.append([message, ""])
partial_response = ""
for new_token in streamer:
partial_response += new_token
history[-1][1] = partial_response
yield history, gr.Button(visible=False), gr.Button(visible=True, interactive=True)
with gr.Blocks() as demo:
with gr.Row():
image = gr.Image(type="pil")
with gr.Column():
chat = gr.Chatbot(show_label=False)
message = gr.Textbox(interactive=True, show_label=False, container=False)
with gr.Row():
gr.ClearButton([chat, message])
stop = gr.Button(value="Stop", variant="stop", visible=False)
submit = gr.Button(value="Submit", variant="primary")
with gr.Row():
gr.Examples(
[
["images/interior.jpg", "Describe the image accurately."],
["images/cat.jpg", "Describe the image in three sentences."],
["images/child.jpg", "Describe the image in one sentence."],
],
[image, message],
label="Captioning"
)
gr.Examples(
[
["images/scream.jpg", "What is the main emotion of this image?"],
["images/louvre.jpg", "Where is this landmark located?"],
["images/three_people.jpg", "What are these people doing?"]
],
[image, message],
label="VQA"
)
response_handler = (
response,
[message, chat, image],
[chat, submit, stop]
)
postresponse_handler = (
lambda: (gr.Button(visible=False), gr.Button(visible=True)),
None,
[stop, submit]
)
event1 = message.submit(*response_handler)
event1.then(*postresponse_handler)
event2 = submit.click(*response_handler)
event2.then(*postresponse_handler)
stop.click(None, None, None, cancels=[event1, event2])
demo.queue()
demo.launch() |