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CLIP / app.py

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Update app.py

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	import streamlit as st
	#from transformers import CLIPModel, pipeline, CLIPImageProcessor
	from transformers import pipeline
	import torch
	from PIL import Image

	#################################
	#### FUNCTIONS

	def load_clip(model_size='large'):
	if model_size == 'base':
	MODEL_name = 'openai/clip-vit-base-patch32'
	#elif model_size == 'large':
	# MODEL_name = 'openai/clip-vit-large-patch14'

	model = CLIPModel.from_pretrained(MODEL_name)
	processor = CLIPImageProcessor.from_pretrained(MODEL_name)

	return processor, model

	def inference_clip(options, image, processor, model):

	inputs = processor(text= options, images=image, return_tensors="pt", padding=True)
	with torch.no_grad():
	outputs = model(**inputs)

	#logits_per_text = outputs.logits_per_text
	logits_per_image = outputs.logits_per_image # this is the image-text similarity score
	probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities

	max_prob_idx = torch.argmax(probs)
	max_prob_option = options[max_prob_idx]
	max_prob = probs[max_prob_idx].item()
	return max_prob_option

	#################################
	#### LAYOUT
	col_l, col_r = st.columns(2)

	#CLIP_large = load_clip(model_size='large')
	model_name = "openai/clip-vit-large-patch14-336"
	classifier = pipeline("zero-shot-image-classification", model = model_name)



	#### Loading picture
	with col_l:
	picture_file = st.file_uploader("Picture :", type=["jpg", "jpeg", "png"])

	if picture_file is not None:
	image = Image.open(picture_file)
	st.image(image, caption='Please upload an image of the damage') #use_column_width=True

	#image
	with col_l:
	default_options = 'There is a car, There is no car'
	options = st.text_input(label="Please enter the classes", value=default_options).split(',')

	#options = list(options)

	# button to launch compute
	if st.button("Compute"):
	#clip_processor, clip_model = load_clip(model_size='large')
	#result = inference_clip(options = options, image = image, processor=clip_processor, model=clip_model)
	scores = classifier(image,
	candidate_labels = options)

	with col_r:
	#st.write(result)
	st.dataframe(scores)