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# import gradio as gr
# # from PIL import Image
# from transformers.utils import logging
# from transformers import BlipForConditionalGeneration, AutoProcessor
# logging.set_verbosity_error()
# model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
# def caption_image(image):
# inputs = processor(image, return_tensors="pt")
# out = model.generate(**inputs)
# caption = processor.decode(out[0], skip_special_tokens=True)
# return caption
# gr.Interface(caption_image, gr.inputs.Image(), "text").launch()
# # gr.Interface(caption_image, image_input, caption_output).launch()
import streamlit as st
# from PIL import Image
from transformers.utils import logging
from transformers import BlipForConditionalGeneration, AutoProcessor
import torch
logging.set_verbosity_error()
model = BlipForConditionalGeneration.from_pretrained("./models/Salesforce/blip-image-captioning-base")
processor = AutoProcessor.from_pretrained("./models/Salesforce/blip-image-captioning-base")
st.title("Image Captioning")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
st.write("")
st.write("Generating caption...")
inputs = processor(image, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
st.write("Caption:", caption)
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