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import gradio as gr
import open_clip
import torch
import requests
import numpy as np
from PIL import Image
model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP')
tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP')
def predict(inp):
image = preprocess_val(inp).unsqueeze(0)
# catgs = [
# "Shirts",
# "SetShirtsPants",
# "SetJacketsPants",
# "Pants",
# "Jeans",
# "JacketsCoats",
# "Shoes",
# "Underpants",
# "Socks",
# "Hats",
# "Wallets",
# "Bags",
# "Scarfs",
# "Parasols&Umbrellas",
# "Necklaces",
# "Towels&Robes",
# "WallObjects",
# "Rugs",
# "Glassware",
# "Mugs&Cups",
# "OralCare"
# ]
# text = tokenizer(catgs)
# with torch.no_grad(), torch.cuda.amp.autocast():
# image_features = model.encode_image(image)
# image_features /= image_features.norm(dim=-1, keepdim=True)
# text_features = model.encode_text(text)
# text_features /= text_features.norm(dim=-1, keepdim=True)
# text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
# max_prob_idx = np.argmax(text_probs)
# pred_lbl = catgs[max_prob_idx]
# pred_lbl_prob = text_probs[0, max_prob_idx].item()
pred_lbl = "clothing"
mw = ["men", "women", "boy", "girl"]
catgs = [
mw[0] + "s " + pred_lbl,
mw[1] + "s " + pred_lbl,
mw[2] + "s " + pred_lbl,
mw[3] + "s " + pred_lbl
]
text = tokenizer(catgs)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
max_prob_idx = np.argmax(text_probs)
pred_lbl_f = mw[max_prob_idx]
pred_lbl_prob_f = text_probs[0, max_prob_idx].item()
# tlt = f"{pred_lbl} <{100.0 * pred_lbl_prob:.1f}%> , {pred_lbl_f} <{100.0 * pred_lbl_prob_f:.1f}%>"
tlt = f"{pred_lbl_f} <{100.0 * pred_lbl_prob_f:.1f}%>"
return(tlt)
gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(),
examples=["imgs/cargo.jpg", "imgs/palazzo.jpg",
"imgs/leggings.jpg", "imgs/dresspants.jpg"]).launch(share=True)
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