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import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
import re
from PIL import Image
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
model = AutoModelForCausalLM.from_pretrained('vumichien/Florence-2-FT-Caption', trust_remote_code=True).to("cpu").eval()
processor = AutoProcessor.from_pretrained('vumichien/Florence-2-FT-Caption', trust_remote_code=True)
TITLE = "# [Florence-2 Captioner](https://huggingface.co/vumichien/Florence-2-FT-Caption)"
def modify_caption(caption: str) -> str:
"""
Removes specific prefixes from captions if present, otherwise returns the original caption.
Args:
caption (str): A string containing a caption.
Returns:
str: The caption with the prefix removed if it was present, or the original caption.
"""
# Define the prefixes to remove
prefix_substrings = [
('captured from ', ''),
('captured at ', '')
]
# Create a regex pattern to match any of the prefixes
pattern = '|'.join([re.escape(opening) for opening, _ in prefix_substrings])
replacers = {opening.lower(): replacer for opening, replacer in prefix_substrings}
# Function to replace matched prefix with its corresponding replacement
def replace_fn(match):
return replacers[match.group(0).lower()]
# Apply the regex to the caption
modified_caption = re.sub(pattern, replace_fn, caption, count=1, flags=re.IGNORECASE)
# If the caption was modified, return the modified version; otherwise, return the original
return modified_caption if modified_caption != caption else caption
#@spaces.GPU
def run_example(image):
image = Image.fromarray(image)
task_prompt = "<CAPTION>"
prompt = task_prompt
# Ensure the image is in RGB mode
if image.mode != "RGB":
image = image.convert("RGB")
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cpu")
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
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))
return modify_caption(parsed_answer["<CAPTION>"])
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
examples = ["240617143250078.JPG", "240617144124216.JPG", "240617144154631.JPG", "240617143227939.JPG"]
with gr.Blocks(css=css) as demo:
gr.Markdown(TITLE)
with gr.Tab(label="Florence-2 SD3 Prompts"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture",height=400)
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
submit_btn.click(run_example, [input_img], [output_text])
examples = gr.Examples(
examples,
fn=run_example,
inputs=[input_img],
outputs=output_text,
cache_examples=True,
)
demo.launch(debug=True) |