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from PIL import Image
import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
class ImageCaption:
def __init__(self):
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
def generate(self,img):
if isinstance(img,str):
img = Image.open(img)
# text = "Explain this image,that what she is doing and what kind of expression is it"
input = self.processor(img,return_tensors='pt')
# print(**input)
output = self.model.generate(**input)
caption = self.processor.decode(output[0],skip_special_tokens = True)
return caption
ic = ImageCaption()
app = gr.Interface(
fn = ic.generate,
inputs=gr.Image(type='pil'),
outputs="text",
description="upload image to generate caption"
)
app.launch()
# print(ic.generate(input("Enter the source of image: "))) |