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import streamlit as st
from PIL import Image
from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel,RobertaTokenizerFast
#import torch
#from transformers import BlipProcessor, BlipForConditionalGeneration
def set_page_config():
st.set_page_config(
page_title='Caption an Image',
page_icon=':camera:',
layout='wide',
)
#def initialize_model():
# hf_model = "Salesforce/blip-image-captioning-large"
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# processor = BlipProcessor.from_pretrained(hf_model)
# model = BlipForConditionalGeneration.from_pretrained(hf_model).to(device) # type: ignore
# return processor, model, device
def upload_image():
return st.sidebar.file_uploader("Upload an image (we aren't storing anything)", type=["jpg", "jpeg", "png"])
def resize_image(image, max_width):
width, height = image.size
if width > max_width:
ratio = max_width / width
height = int(height * ratio)
image = image.resize((max_width, height))
return image
def generate_caption(processor, model, device, image):
#inputs = processor(image, return_tensors='pt').to(device)
#out = model.generate(**inputs, max_new_tokens=20)
#caption = processor.decode(out[0], skip_special_tokens=True)
caption="im here "
return caption
def main():
set_page_config()
st.header("Caption an Image :camera:")
uploaded_image = upload_image()
if uploaded_image is not None:
image = Image.open(uploaded_image)
image = resize_image(image, max_width=300)
st.image(image, caption='Your image')
with st.sidebar:
st.divider()
if st.sidebar.button('Generate Caption'):
with st.spinner('Generating caption...'):
#processor, model, device = initialize_model()
#caption = generate_caption(processor, model, device, image)
caption="im here man"
st.header("Caption:")
st.markdown(f'**{caption}**')
if __name__ == '__main__':
main()
st.markdown("""
---
You are looking at Finetuned image Caption model """)
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