Aria / app.py
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import gradio as gr
from transformers import AutoProcessor, AutoModel, TextIteratorStreamer
from threading import Thread
import re
import time
from PIL import Image
import torch
import spaces
#import subprocess
#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
processor = AutoProcessor.from_pretrained(
"rhymes-ai/Aria",
revision="0e2a8319",
torch_dtype=torch.bfloat16,
)
model = AutoModel.from_pretrained("rhymes-ai/Aria",
revision="f7c92bba",
torch_dtype=torch.bfloat16,
device_map="auto",
)
@spaces.GPU
def model_inference(
input_dict, history, decoding_strategy, temperature, max_new_tokens,
repetition_penalty, top_p
):
text = input_dict["text"]
print(input_dict["files"])
if len(input_dict["files"]) > 1:
images = [Image.open(image).convert("RGB") for image in input_dict["files"]]
elif len(input_dict["files"]) == 1:
images = [Image.open(input_dict["files"][0]).convert("RGB")]
else:
images = []
if text == "" and not images:
gr.Error("Please input a query and optionally image(s).")
if text == "" and images:
gr.Error("Please input a text query along the image(s).")
resulting_messages = [
{
"role": "user",
"content": [{"type": "image"} for _ in range(len(images))] + [
{"type": "text", "text": text}
]
}
]
prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=[images], return_tensors="pt")
inputs.to("cuda", dtype=torch.bfloat16)
generation_args = {
"max_new_tokens": max_new_tokens,
"repetition_penalty": repetition_penalty,
}
assert decoding_strategy in [
"Greedy",
"Top P Sampling",
]
if decoding_strategy == "Greedy":
generation_args["do_sample"] = False
elif decoding_strategy == "Top P Sampling":
generation_args["temperature"] = temperature
generation_args["do_sample"] = True
generation_args["top_p"] = top_p
generation_args.update(inputs)
# Generate
streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens= True)
generation_args = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
generated_text = ""
thread = Thread(target=model.generate, kwargs=generation_args)
thread.start()
yield "..."
buffer = ""
for new_text in streamer:
buffer += new_text
generated_text_without_prompt = buffer#[len(ext_buffer):]
time.sleep(0.01)
yield buffer
examples=[
[{"text": "What art era do these artpieces belong to?", "files": ["example_images/rococo.jpg", "example_images/rococo_1.jpg"]}, "Greedy", 0.4, 512, 1.2, 0.8],
[{"text": "I'm planning a visit to this temple, give me travel tips.", "files": ["example_images/examples_wat_arun.jpg"]}, "Greedy", 0.4, 512, 1.2, 0.8],
[{"text": "What is the due date and the invoice date?", "files": ["example_images/examples_invoice.png"]}, "Greedy", 0.4, 512, 1.2, 0.8],
[{"text": "What is this UI about?", "files": ["example_images/s2w_example.png"]}, "Greedy", 0.4, 512, 1.2, 0.8],
[{"text": "Where do the severe droughts happen according to this diagram?", "files": ["example_images/examples_weather_events.png"]}, "Greedy", 0.4, 512, 1.2, 0.8],
]
demo = gr.ChatInterface(fn=model_inference, title="🎼 Aria by Rhymes AI 🎶",
description="Play with [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) in this demo. To get started, upload an image and text or try one of the examples. This checkpoint works best with single turn conversations, so clear the conversation after a single turn.",
examples=examples,
textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
additional_inputs=[gr.Radio(["Top P Sampling",
"Greedy"],
value="Greedy",
label="Decoding strategy",
#interactive=True,
info="Higher values is equivalent to sampling more low-probability tokens.",
), gr.Slider(
minimum=0.0,
maximum=5.0,
value=0.4,
step=0.1,
interactive=True,
label="Sampling temperature",
info="Higher values will produce more diverse outputs.",
),
gr.Slider(
minimum=8,
maximum=1024,
value=512,
step=1,
interactive=True,
label="Maximum number of new tokens to generate",
), gr.Slider(
minimum=0.01,
maximum=5.0,
value=1.2,
step=0.01,
interactive=True,
label="Repetition penalty",
info="1.0 is equivalent to no penalty",
),
gr.Slider(
minimum=0.01,
maximum=0.99,
value=0.8,
step=0.01,
interactive=True,
label="Top P",
info="Higher values is equivalent to sampling more low-probability tokens.",
)],cache_examples=False
)
demo.launch(debug=True)