Spaces:
Running
Running
File size: 2,969 Bytes
88e550b 128a4c1 f7c5373 128a4c1 80200b5 39b8fb7 128a4c1 69dfe66 88e550b 7369efb 69dfe66 9c02573 69dfe66 fe4dd72 69dfe66 9e5321f 92dc785 39b8fb7 92dc785 128a4c1 7369efb 128a4c1 69dfe66 0f27d7b f7c5373 69dfe66 92dc785 128a4c1 7369efb 92dc785 128a4c1 dc1d715 7369efb dc1d715 7369efb dc1d715 128a4c1 80200b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
import streamlit as st
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import torch
from pathlib import Path
import transformers
from transformers import AutoTokenizer
from jax import numpy as jnp
import json
import requests
import zipfile
import io
import natsort
from PIL import Image as PilImage
from torchvision import datasets, transforms
from torchvision.transforms import CenterCrop, Normalize, Resize, ToTensor
from torchvision.transforms.functional import InterpolationMode
from tqdm import tqdm
from modeling_hybrid_clip import FlaxHybridCLIP
import utils
@st.cache(hash_funcs={FlaxHybridCLIP: lambda _: None})
def get_model():
return FlaxHybridCLIP.from_pretrained("clip-italian/clip-italian-final")
@st.cache(hash_funcs={transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None})
def get_tokenizer():
return AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-uncased", cache_dir="./", use_fast=True)
@st.cache
def download_images():
# from sentence_transformers import SentenceTransformer, util
img_folder = "photos/"
if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
os.makedirs(img_folder, exist_ok=True)
photo_filename = "unsplash-25k-photos.zip"
if not os.path.exists(photo_filename): # Download dataset if does not exist
print(f"Downloading {photo_filename}...")
r = requests.get("http://sbert.net/datasets/" + photo_filename, stream=True)
z = zipfile.ZipFile(io.BytesIO(r.content))
print("Extracting the dataset...")
z.extractall(path=img_folder)
print("Done.")
@st.cache()
def get_image_features():
return jnp.load("static/features/features.npy")
"""
# 👋 Ciao!
# CLIP Italian Demo (HF-Flax Community Week)
"""
query = st.text_input("Insert an italian query text here...")
if query:
with st.spinner("Computing in progress..."):
model = get_model()
download_images()
image_features = get_image_features()
model = get_model()
tokenizer = get_tokenizer()
image_size = model.config.vision_config.image_size
val_preprocess = transforms.Compose(
[
Resize([image_size], interpolation=InterpolationMode.BICUBIC),
CenterCrop(image_size),
ToTensor(),
Normalize(
(0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711),
),
]
)
dataset = utils.CustomDataSet("photos/", transform=val_preprocess)
image_paths = utils.find_image(
query, model, dataset, tokenizer, image_features, n=2
)
st.image(image_paths)
def read_markdown_file(markdown_file):
return Path(markdown_file).read_text()
intro_markdown = read_markdown_file("readme.md")
st.markdown(intro_markdown, unsafe_allow_html=True) |