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import gradio as gr
# from PIL import Image
from transformers.utils import logging
from transformers import BlipForConditionalGeneration, AutoProcessor
from transformers import pipeline
pipe = pipeline("image-to-text",
model="Salesforce/blip-image-captioning-base")
def launch(input):
out = pipe(input)
return out[0]['generated_text']
iface = gr.Interface(launch,
inputs=gr.Image(type='pil'),
outputs="text")
iface.launch()
# 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
# iface = gr.Interface(fn=caption_image, inputs=["image"], outputs="textbox")
# iface.launch()
# 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("Salesforce/blip-image-captioning-base")
# processor = AutoProcessor.from_pretrained("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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