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import streamlit as st
from PIL import Image
from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel
import torch
vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
device = "cuda" if torch.cuda.is_available() else "cpu"
vitgpt_model.to(device)
def generate_caption(processor, model, image, num_seq, tokenizer=None):
inputs = processor(images=image, return_tensors="pt").to(device)
generated_ids = model.generate(pixel_values=inputs.pixel_values,
max_length=50,
num_beams=5,
do_sample=True,
temperature=2.,
top_k = 20,
no_repeat_ngram_size=5,
num_return_sequences=num_seq)
if tokenizer is not None:
generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
else:
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)
return generated_caption
def generate_captions(image, num_seq):
caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, num_seq, vitgpt_tokenizer)
return caption_vitgpt
st.title('Generate text to your image')
uploaded_file = st.file_uploader("Upload your image")
num_seq = st.slider('Return sequences quantity', 1, 5, 2)
if uploaded_file is not None:
if st.button('Generate!'):
col1, col2 = st.columns(2)
with col1:
image = Image.open(uploaded_file)
st.image(image)
with col2:
generated_caption = generate_caption(vitgpt_processor, vitgpt_model, image, num_seq, vitgpt_tokenizer)
for i in generated_caption:
st.write(i) |