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import gradio as gr
import subprocess
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
# import os
# import random
# from gradio_client import Client
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
# Initialize Florence model
device = "cuda" if torch.cuda.is_available() else "cpu"
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
# api_key = os.getenv("HF_READ_TOKEN")
article_text = """
<div style="text-align: center;">
<p>Enjoying the tool? Buy me a coffee and get exclusive prompt guides!</p>
<p><i>Instantly unlock helpful tips for creating better prompts!</i></p>
<div style="display: flex; justify-content: center;">
<a href="https://piczify.lemonsqueezy.com/buy/0f5206fa-68e8-42f6-9ca8-4f80c587c83e">
<img src="https://www.buymeacoffee.com/assets/img/custom_images/yellow_img.png"
alt="Buy Me a Coffee"
style="height: 40px; width: auto; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); border-radius: 10px;">
</a>
</div>
</div>
"""
def generate_caption(image):
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
generated_ids = florence_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 = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = florence_processor.post_process_generation(
generated_text,
task="<MORE_DETAILED_CAPTION>",
image_size=(image.width, image.height)
)
prompt = parsed_answer["<MORE_DETAILED_CAPTION>"]
print("\n\nGeneration completed!:"+ prompt)
return prompt
# yield prompt, None
# image_path = generate_image(prompt,random.randint(0, 4294967296))
# yield prompt, image_path
# def generate_image(prompt, seed=42, width=1024, height=1024):
# try:
# result = Client("KingNish/Realtime-FLUX", hf_token=api_key).predict(
# prompt=prompt,
# seed=seed,
# width=width,
# height=height,
# api_name="/generate_image"
# )
# # Extract the image path from the result tuple
# image_path = result[0]
# return image_path
# except Exception as e:
# raise Exception(f"Error generating image: {str(e)}")
io = gr.Interface(generate_caption,
inputs=[gr.Image(label="Input Image")],
outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
# gr.Image(label="Output Image")
],
#article = article_text
)
io.launch(debug=True) |