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  1. app.py +33 -0
  2. requirements.txt +4 -0
app.py ADDED
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+ from PIL import Image
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+ import gradio as gr
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+ from transformers import BlipProcessor, BlipForConditionalGeneration
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+
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+ class ImageCaption:
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+ def __init__(self):
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+ self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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+ self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
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+
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+ def generate(self,img):
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+ if isinstance(img,str):
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+ img = Image.open(img)
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+
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+ input = self.processor(img,return_tensors='pt')
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+ print(**input)
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+ output = self.model.generate(**input)
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+ caption = self.processor.decode(output[0],skip_special_tokens = True)
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+ return caption
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+
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+
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+ ic = ImageCaption()
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+ app = gr.Interface(
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+ fn = ic.generate,
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+ inputs=gr.Image(type='pil'),
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+ outputs="text",
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+ description="upload image to generate caption"
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+
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+ )
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+
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+ app.launch()
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+
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+
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+ # print(ic.generate(input("Enter the source of image: ")))
requirements.txt ADDED
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+ gradio
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+ torch
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+ transformers
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+ Pillow