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import streamlit as st
import requests
import pickle
from PIL import Image
from transformers import BlipProcessor
with st.sidebar:
st.subheader('Image Captioning App using BLIP')
st.write('This app uses the BLIP model to generate captions for images.Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone).')
image = Image.open('details.png')
st.image(image, caption='BLIP Model')
st.code('App Built by Ambuj Raj', language='python')
st.title('Image Captioning App using BLIP')
flag_image=0
flag_url=0
tab1, tab2 = st.tabs(["Upload Image", "Use URL"])
with tab1:
flag_image=1
uploaded_file = st.file_uploader("Choose a image",type=['png','jpeg','jpg'])
if uploaded_file is not None:
st.image(uploaded_file, width=300)
raw_image = Image.open(uploaded_file).convert('RGB')
with tab2:
flag_url=1
img_url = st.text_input('Enter URL of image')
if img_url:
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
st.image(raw_image, width=300)
if st.button('Generate Caption'):
if(flag_image==1):
flag_image=1
with st.spinner('Generating Caption...'):
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
filename = 'blip.sav'
loaded_model = pickle.load(open(filename, 'rb'))
inputs = processor(raw_image, return_tensors="pt")
out = loaded_model.generate(**inputs)
st.success('Caption Generated!')
st.write('Generated Caption is: ',processor.decode(out[0], skip_special_tokens=True))
elif(flag_url==1):
flag_url=0
with st.spinner('Generating Caption...'):
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
filename = 'blip.sav'
loaded_model = pickle.load(open(filename, 'rb'))
inputs = processor(raw_image, return_tensors="pt")
out = loaded_model.generate(**inputs)
st.success('Caption Generated!')
st.write('Generated Caption is: ',processor.decode(out[0], skip_special_tokens=True))
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