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import streamlit as st
from transformers import VisionEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer
import requests
from PIL import Image
import torch
CHECKPOINT = "g8a9/vit-geppetto-captioning"
model = VisionEncoderDecoderModel.from_pretrained(CHECKPOINT)
feature_extractor = AutoFeatureExtractor.from_pretrained(CHECKPOINT)
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
model.eval()
st.title("Image Captioning with ViT & GePpeTto 🇮🇹")
st.sidebar.markdown("## Generation parameters")
max_length = st.sidebar.number_input("Max length", value=20, min_value=1)
num_beams = st.sidebar.number_input("Beam size", value=5, min_value=1)
early_stopping = st.sidebar.checkbox("Early stopping", value=True)
no_repeat_ngram_size= st.sidebar.number_input("no repeat ngrams size", value=2, min_value=1)
num_return_sequences = st.sidebar.number_input("Generated sequences", value=3, min_value=1)
def generate_caption(url):
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
inputs = feature_extractor(image, return_tensors="pt")
generated_ids = model.generate(
inputs["pixel_values"],
max_length=20,
num_beams=5,
early_stopping=True,
no_repeat_ngram_size=2,
num_return_sequences=3,
)
captions = tokenizer.batch_decode(
generated_ids,
skip_special_tokens=True,
)
return captions[0]
url = st.text_input(
"Insert your URL", "https://iheartcats.com/wp-content/uploads/2015/08/c84.jpg"
)
st.image(url)
if st.button("Run captioning"):
with st.spinner("Processing image..."):
caption = generate_caption(url)
st.text(caption)
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