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a650c88 ba7578e a650c88 ba7578e a650c88 ba7578e a650c88 ba7578e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import gradio as gr
import subprocess
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
# Install flash-attn if needed
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)
def generate_caption(image):
if image is None:
return ""
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)
)
return parsed_answer["<MORE_DETAILED_CAPTION>"]
# Custom CSS for a "Beautiful UI"
css = """
.container { max-width: 900px; margin: auto; padding-top: 2rem; }
.header { text-align: center; margin-bottom: 2rem; }
.header h1 { font-size: 2.5rem; font-weight: 800; color: #ffffff; margin-bottom: 0.5rem; }
.header p { color: #a0a0a0; font-size: 1.1rem; }
.generate-btn {
background: linear-gradient(90deg, #4776E6 0%, #8E54E9 100%) !important;
border: none !important;
color: white !important;
font-weight: bold !important;
}
.generate-btn:hover { transform: scale(1.02); transition: 0.2s; }
.output-box { border-radius: 10px !important; background-color: #1a1a1a !important; }
"""
with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="violet", secondary_hue="indigo")) as demo:
with gr.Column(elem_classes="container"):
# HTML Header Section
gr.HTML(
"""
<div class="header">
<h1>✨ Image to Prompt Studio</h1>
<p>Upload an image to generate a highly detailed AI prompt using Florence-2.</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=1):
input_img = gr.Image(label="Upload Image", type="pil")
submit_btn = gr.Button("Generate Prompt", variant="primary", elem_classes="generate-btn")
with gr.Column(scale=1):
output_text = gr.Textbox(
label="Generated Prompt",
lines=8,
placeholder="Your prompt will appear here...",
show_copy_button=True,
elem_classes="output-box"
)
# Example images (optional)
gr.Examples(
examples=[], # You can add paths to example images here
inputs=input_img
)
# Logic
submit_btn.click(
fn=generate_caption,
inputs=[input_img],
outputs=[output_text]
)
if __name__ == "__main__":
demo.launch(debug=True) |