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import gradio as gr
from transformers import AutoModel, AutoProcessor, AutoTokenizer
import torch
from PIL import Image
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
# ImageNet constants
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
# Load model and processor
model_name = 'rinkhanh000/Vintern-ViMemeCap'
model = AutoModel.from_pretrained(
model_name,
torch_dtype=torch.float32, # Use float32 for CPU
low_cpu_mem_usage=True,
trust_remote_code=True
).eval() # No .cuda()
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1)
if i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
# Prediction function
def predict_from_prompt_and_image(prompt, image):
if not prompt or not image:
return {"Error": "Please provide both a prompt and an image"}
try:
generation_config = dict(max_new_tokens=512, do_sample=False, num_beams=3, repetition_penalty=3.5)
question = prompt.strip()
pixel_values = load_image(image, max_num=6).to(torch.float32) # Use float32 for CPU
response = model.chat(tokenizer, pixel_values, question, generation_config)
return {response}
except Exception as e:
return {"Error": f"Failed to process: {str(e)}"}
# Gradio interface
demo = gr.Interface(
fn=predict_from_prompt_and_image,
inputs=[
gr.Textbox(label="Enter Prompt"),
gr.Image(label="Upload Image", type="pil")
],
outputs=gr.Textbox(label="Generated Caption"),
title="ViMemeCap",
allow_flagging="never"
)
# Launch the interface
demo.launch() |