app.py

import traceback
import gradio as gr
from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel, BitsAndBytesConfig
import torch
import open_clip
from PIL import Image
import requests

from huggingface_hub import hf_hub_download

# Load the Blip base model
preprocessor_blip_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model_blip_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

# Load the Blip large model
preprocessor_blip_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model_blip_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")

# Load the GIT coco base model
preprocessor_git_base_coco = AutoProcessor.from_pretrained("microsoft/git-base-coco")
model_git_base_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")

# Load the GIT coco large model
preprocessor_git_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco")
model_git_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")

# Load the CLIP model
model_oc_coca, _, transform_oc_coca = open_clip.create_model_and_transforms(
	model_name="coca_ViT-L-14",
	pretrained="mscoco_finetuned_laion2B-s13B-b90k"
)

device = "cuda" if torch.cuda.is_available() else "cpu"
# Transfer the models to the device
model_blip_base.to(device)
model_blip_large.to(device)
model_git_base_coco.to(device)
model_git_large_coco.to(device)
model_oc_coca.to(device)


def generate_caption(
	preprocessor,
	model,
	image,
	tokenizer=None,
):
	"""
	Generate captions for the given image.

	-----
	Parameters
	preprocessor: AutoProcessor
		The preprocessor for the model.
	model: BlipForConditionalGeneration
		The model to use.
	image: PIL.Image
		The image to generate captions for.
	tokenizer: AutoTokenizer
		The tokenizer to use. If None, the default tokenizer for the model will be used.
	use_float_16: bool
		Whether to use float16 precision. This can speed up inference, but may lead to worse results.

	-----
	Returns
	str
		The generated caption.
	"""
	pixel_values = preprocessor(images=image, return_tensors="pt").pixel_values.to(device)

	generated_ids = model.generate(
		pixel_values=pixel_values,
		max_length=50,
	)

	if tokenizer is None:
		generated_caption = preprocessor.batch_decode(generated_ids, skip_special_tokens=True)[0]
	else:
		generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

	# generated_ids = model.generate(**inputs, max_new_tokens=32)
	# generated_text = preprocessor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()	
		
	return generated_caption


def generate_captions_clip(
	model,
	transform,
	image
):
	"""
	Generate captions for the given image using CLIP.

	-----
	Parameters
	model: VisionEncoderDecoderModel
		The CLIP model to use.
	transform: Callable
		The transform to apply to the image before passing it to the model.
	image: PIL.Image
		The image to generate captions for.

	-----
	Returns
	str
		The generated caption.
	"""
	im = transform(image).unsqueeze(0).to(device)
	with torch.no_grad(), torch.cuda.amp.autocast():
		generated = model.generate(im, seq_len=20)
	generated_caption = open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
	return generated_caption


def generate_captions(
	image,
	max_length,
	temperature,
):
	"""
	Generate captions for the given image.

	-----
	Parameters
	image: PIL.Image
		The image to generate captions for.

	-----
	Returns
	str
		The generated caption.
	"""
	caption_blip_base = ""
	caption_blip_large = ""
	caption_git_large_coco = ""
	caption_oc_coca = ""

	# Generate captions for the image using the Blip base model
	try:
		caption_blip_base = generate_caption(preprocessor_blip_base, model_blip_base, image).strip()
	except Exception as e:
		print(e)

	# Generate captions for the image using the Blip large model
	try:
		caption_blip_large = generate_caption(preprocessor_blip_large, model_blip_large, image).strip()
	except Exception as e:
		print(e)

	# Generate captions for the image using the GIT coco base model
	try:
		caption_git_base_coco = generate_caption(preprocessor_git_base_coco, model_git_base_coco, image).strip()
	except Exception as e:
		print(e)
	
	# Generate captions for the image using the GIT coco large model
	try:
		caption_git_large_coco = generate_caption(preprocessor_git_large_coco, model_git_large_coco, image).strip()
	except Exception as e:
		print(e)
	
	# Generate captions for the image using the CLIP model
	try:
		caption_oc_coca = generate_captions_clip(model_oc_coca, transform_oc_coca, image).strip()
	except Exception as e:
		print(e)

	return caption_blip_base, caption_blip_large, caption_git_base_coco, caption_git_large_coco, caption_oc_coca


# Create the interface
iface = gr.Interface(
	fn=generate_captions,
	# Define the inputs: Image, Slider for Max Length, Slider for Temperature
	inputs=[
		gr.inputs.Image(type="pil", label="Image"),
		gr.inputs.Slider(minimum=16, maximum=64, step=2, default=32, label="Max Length"),
		gr.inputs.Slider(minimum=0.5, maximum=1.5, step=0.1, default=1.0, label="Temperature"),
	],
	# Define the outputs
	outputs=[
		gr.outputs.Textbox(label="Blip base"),
		gr.outputs.Textbox(label="Blip large"),
		gr.outputs.Textbox(label="GIT base coco"),
		gr.outputs.Textbox(label="GIT large coco"),
		gr.outputs.Textbox(label="CLIP"),
	],
	title="Image Captioning",
	description="Generate captions for images using the Blip2 model, the Blip base model, the Blip large model, the GIT large coco model, and the CLIP model.",
	enable_queue=True,
)

# Launch the interface
iface.launch(debug=True)