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import streamlit as st
from transformers import pipeline
from PIL import Image
import torch
from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
loc = "ydshieh/vit-gpt2-coco-en"
pipeline = pipeline(model=loc)
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = VisionEncoderDecoderModel.from_pretrained(loc)
model.eval()
def predict(image):
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
with torch.no_grad():
output_ids = model.generate(pixel_values, max_length=1000, num_beams=4, return_dict_in_generate=True).sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
file_name = st.file_uploader("Upload")
if file_name is not None:
col1, col2 = st.columns(2)
image = Image.open(file_name)
col1.image(image, use_column_width = True)
col2.header("Description")
st.write(predict(image)) |