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import gradio as gr
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
def infer(image_input):
#img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(image_input, stream=True).raw).convert('RGB')
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
print(caption)
return caption
css="""
#col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
"""
# Image to Story
Upload an image, get a story !
<br/>
<br/>
[](https://huggingface.co/spaces/fffiloni/SplitTrack2MusicGen?duplicate=true) for longer audio, more control and no queue.</p>
"""
)
image_in = gr.Image(label="Image input")
submit_btn = gr.Button('Sumbit')
story = gr.Textbox(label="Generated Story")
submit_btn.click(fn=infer, inputs=[image_in], outputs=[story])
demo.queue().launch()
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