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import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
import spaces
from PIL import Image
import io
import subprocess
subprocess.run("pip install flash-attn --no-build-isolation", env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, shell=True)
models = {
"maxiw/Florence-2-ScreenQA-base": AutoModelForCausalLM.from_pretrained("maxiw/Florence-2-ScreenQA-base", trust_remote_code=True).to("cuda").eval(),
}
processors = {
"maxiw/Florence-2-ScreenQA-base": AutoProcessor.from_pretrained("maxiw/Florence-2-ScreenQA-base", trust_remote_code=True),
}
DESCRIPTION = "# [Florence-2-ScreenQA Demo](https://huggingface.co/maxiw/Florence-2-ScreenQA-base)"
@spaces.GPU
def run_example(task_prompt, image, text_input=None, model_id="maxiw/Florence-2-ScreenQA-base"):
model = models[model_id]
processor = processors[model_id]
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(
generated_text,
task=task_prompt,
image_size=(image.width, image.height)
)
if "<SQA>" in parsed_answer:
parsed_answer = parsed_answer["<SQA>"]
return parsed_answer
def process_image(image, task_prompt, text_input=None, model_id="maxiw/Florence-2-ScreenQA-base"):
image = Image.fromarray(image) # Convert NumPy array to PIL Image
if task_prompt == "ScreenQA":
task_prompt = "<SQA>"
results = run_example(task_prompt, image, text_input, model_id=model_id)
return results
else:
print("Unknown task prompt")
return "", None # Return empty string and None for unknown task prompts
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
single_task_list =[
"ScreenQA"
]
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="Florence-2 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="maxiw/Florence-2-ScreenQA-base")
task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="ScreenQA")
text_input = gr.Textbox(label="Question")
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
gr.Examples(
examples=[
["image1.jpg", "ScreenQA", "What is the version of the settings?"],
["image1.jpg", "ScreenQA", "What is the state of use lower resolution images?"],
["image2.jpg", "ScreenQA", "How much is the discount for the product?"]
],
inputs=[input_img, task_prompt, text_input],
outputs=[output_text],
fn=process_image,
cache_examples=True,
label="Try examples"
)
submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text])
demo.launch(debug=True)
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