File size: 4,766 Bytes
3a77e9a e583785 3a77e9a a5b1435 2a4dd1c a5b1435 2a4dd1c a5b1435 2a4dd1c a5b1435 3a77e9a a5b1435 |
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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import gradio as gr
import subprocess
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
# Attempt to install flash-attn
try:
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, check=True, shell=True)
except subprocess.CalledProcessError as e:
print(f"Error installing flash-attn: {e}")
print("Continuing without flash-attn.")
# Determine the device to use
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load the base model and processor
try:
vision_language_model_base = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
vision_language_processor_base = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
except Exception as e:
print(f"Error loading base model: {e}")
vision_language_model_base = None
vision_language_processor_base = None
# Load the large model and processor
try:
vision_language_model_large = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to(device).eval()
vision_language_processor_large = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True)
except Exception as e:
print(f"Error loading large model: {e}")
vision_language_model_large = None
vision_language_processor_large = None
def describe_image(uploaded_image, model_choice):
"""
Generates a detailed description of the input image using the selected model.
Args:
uploaded_image (PIL.Image.Image): The image to describe.
model_choice (str): The model to use, either "Base" or "Large".
Returns:
str: A detailed textual description of the image or an error message.
"""
if uploaded_image is None:
return "Please upload an image."
if model_choice == "Base":
if vision_language_model_base is None:
return "Base model failed to load."
model = vision_language_model_base
processor = vision_language_processor_base
elif model_choice == "Large":
if vision_language_model_large is None:
return "Large model failed to load."
model = vision_language_model_large
processor = vision_language_processor_large
else:
return "Invalid model choice."
if not isinstance(uploaded_image, Image.Image):
uploaded_image = Image.fromarray(uploaded_image)
inputs = processor(text="<MORE_DETAILED_CAPTION>", images=uploaded_image, return_tensors="pt").to(device)
with torch.no_grad():
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]
processed_description = processor.post_process_generation(
generated_text,
task="<MORE_DETAILED_CAPTION>",
image_size=(uploaded_image.width, uploaded_image.height)
)
image_description = processed_description["<MORE_DETAILED_CAPTION>"]
print("\nImage description generated!:", image_description)
return image_description
# Description for the interface
description = "Select the model to use for generating the image description. 'Base' is smaller and faster, while 'Large' is more accurate but slower."
if device == "cpu":
description += " Note: Running on CPU, which may be slow for large models."
# Define examples
examples = [
["images/1.png", "Base"],
["images/1.png", "Large"]
]
css = """
.submit-btn {
background-color: #4682B4 !important;
color: white !important;
}
.submit-btn:hover {
background-color: #87CEEB !important;
}
"""
# Create the Gradio interface with Blocks
with gr.Blocks(theme="bethecloud/storj_theme") as demo:
gr.Markdown("# **[Florence-2 Models Image Captions](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
gr.Markdown(description)
with gr.Row():
image_input = gr.Image(label="Upload Image", type="pil")
model_choice = gr.Radio(["Base", "Large"], label="Model Choice", value="Base")
output = gr.Textbox(label="Generated Caption", lines=4, show_copy_button=True)
generate_btn = gr.Button("Generate Caption",
elem_classes="submit-btn")
generate_btn.click(fn=describe_image, inputs=[image_input, model_choice], outputs=output)
gr.Examples(examples=examples, inputs=[image_input, model_choice])
# Launch the interface
demo.launch(debug=True) |