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| import jax | |
| import jax.numpy as jnp | |
| import numpy as np | |
| import pandas as pd | |
| import requests | |
| import streamlit as st | |
| from PIL import Image | |
| from utils import load_model | |
| def app(model_name): | |
| model, processor = load_model(f"koclip/{model_name}") | |
| st.title("Zero-shot Image Classification") | |
| st.markdown( | |
| """ | |
| This demonstration explores capability of KoCLIP in the field of Zero-Shot Prediction. This demo takes a set of image and captions from, and predicts the most likely label among the different captions given. | |
| KoCLIP is a retraining of OpenAI's CLIP model using 82,783 images from [MSCOCO](https://cocodataset.org/#home) dataset and Korean caption annotations. Korean translation of caption annotations were obtained from [AI Hub](https://aihub.or.kr/keti_data_board/visual_intelligence). Base model `koclip` uses `klue/roberta` as text encoder and `openai/clip-vit-base-patch32` as image encoder. Larger model `koclip-large` uses `klue/roberta` as text encoder and bigger `google/vit-large-patch16-224` as image encoder. | |
| """ | |
| ) | |
| query1 = st.text_input( | |
| "Enter a URL to an image...", | |
| value="http://images.cocodataset.org/val2017/000000039769.jpg" | |
| ) | |
| query2 = st.file_uploader("or upload an image...", type=["jpg", "jpeg", "png"]) | |
| captions = st.text_input( | |
| "Enter candidate captions in comma-separated form.", | |
| value="κ·μ¬μ΄ κ³ μμ΄,λ©μλ κ°μμ§,ν¬λν¬λν νμ€ν°", | |
| ) | |
| if st.button("μ§λ¬Έ (Query)"): | |
| if not any([query1, query2]): | |
| st.error("Please upload an image or paste an image URL.") | |
| else: | |
| image_data = ( | |
| query2 if query2 is not None else requests.get(query1, stream=True).raw | |
| ) | |
| image = Image.open(image_data) | |
| st.image(image) | |
| captions = captions.split(",") | |
| inputs = processor( | |
| text=captions, images=image, return_tensors="jax", padding=True | |
| ) | |
| inputs["pixel_values"] = jnp.transpose( | |
| inputs["pixel_values"], axes=[0, 2, 3, 1] | |
| ) | |
| outputs = model(**inputs) | |
| probs = jax.nn.softmax(outputs.logits_per_image, axis=1) | |
| score_dict = {captions[idx]: prob for idx, prob in enumerate(*probs)} | |
| df = pd.DataFrame(score_dict.values(), index=score_dict.keys()) | |
| st.bar_chart(df) | |
| # for idx, prob in sorted(enumerate(*probs), key=lambda x: x[1], reverse=True): | |
| # st.text(f"Score: `{prob}`, {captions[idx]}") | |