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from PIL import Image
import requests
import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
model_id = "Salesforce/blip-image-captioning-base"
model = BlipForConditionalGeneration.from_pretrained(model_id)
processor = BlipProcessor.from_pretrained(model_id)
def parse_input(input):
# this is a weblink
if isinstance(input, str):
image = Image.open(requests.get(input, stream=True).raw).convert('RGB')
# otherwise it's an image
else:
image = Image.open(input).convert("RGB")
return image
def launch(image):
inputs = processor(image, return_tensors="pt")
out = model.generate(**inputs)
return processor.decode(out[0], skip_special_tokens=True)
iface = gr.Interface(launch, inputs="text", outputs="text")
iface.launch() |