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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)) | |