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from __future__ import annotations
import gradio as gr
import PIL.Image
import spaces
import torch
from transformers import AutoProcessor, BlipForConditionalGeneration
DESCRIPTION = "# Image Captioning with BLIP"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model_id = "Salesforce/blip-image-captioning-large"
processor = AutoProcessor.from_pretrained(model_id)
model = BlipForConditionalGeneration.from_pretrained(model_id).to(device)
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_caption(image_paths):
images = []
for image_path in image_paths:
image = Image.open(image_path)
if image.mode != "RGB":
image = image.convert(mode="RGB")
images.append(image)
pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
return preds
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