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import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoProcessor
import torch
from PIL import Image
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
models = {
"microsoft/Phi-3.5-vision-instruct": AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()
}
processors = {
"microsoft/Phi-3.5-vision-instruct": AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True)
}
DESCRIPTION = "[Phi-3.5-vision Demo](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)"
kwargs = {}
kwargs['torch_dtype'] = torch.bfloat16
user_prompt = '<|user|>\n'
assistant_prompt = '<|assistant|>\n'
prompt_suffix = "<|end|>\n"
@spaces.GPU
def run_example(image, text_input=None, model_id="microsoft/Phi-3.5-vision-instruct"):
model = models[model_id]
processor = processors[model_id]
prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
image = Image.fromarray(image).convert("RGB")
inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
generate_ids = model.generate(**inputs,
max_new_tokens=1000,
eos_token_id=processor.tokenizer.eos_token_id,
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)[0]
return response
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="Phi-3.5 Input"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="microsoft/Phi-3.5-vision-instruct")
text_input = gr.Textbox(label="Question")
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text])
demo.launch(debug=True, show_error=True) |