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import gradio as gr
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
def inference(input_img, captions):
captions_list = captions.split(",")
inputs = processor(text=captions_list, images=input_img, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)
probabilities_percentages = ', '.join(['{:.2f}%'.format(prob.item() * 100) for prob in probs[0]])
return probabilities_percentages
title = "TSAI S18 Assignment: Use a pretrained CLIP model and give a demo on its workig"
description = "A simple Gradio interface that accepts an image and some captions, and gives a score as to how much the caption describes the image "
examples = [["cats.jpg","a photo of a cat, a photo of a dog"],
["personBicycle.jpg","person riding bicycle, person driving car, photo of a dog"]
]
demo = gr.Interface(
inference,
inputs = [gr.Image(shape=(416, 416), label="Input Image"), gr.Textbox(placeholder="Enter different captions for image, separated by comma")],
outputs = [gr.Textbox(label="Probability score of captions")],
title = title,
description = description,
examples = examples,
)
demo.launch()
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