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import streamlit as st
from transformers import CLIPModel, CLIPProcessor, 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 = CLIPProcessor.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
#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
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)
col_l, col_r = st.columns(2)
#image
with col_l:
default_options = ['black', 'white', 'gray', 'red', 'blue', 'silver', 'red', 'brown', 'green', 'orange', 'beige', 'pruple', 'gold', 'yellow']
options = st.text_input(label="Please enter the classes", value=default_options)
#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)
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