app.py

from html import escape
import re
import streamlit as st
import pandas as pd, numpy as np
from transformers import CLIPProcessor, CLIPModel
from st_clickable_images import clickable_images


@st.cache(
    show_spinner=False,
    hash_funcs={
        CLIPModel: lambda _: None,
        CLIPProcessor: lambda _: None,
        dict: lambda _: None,
    },
)
def load():
    model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
    df = {0: pd.read_csv("data.csv"), 1: pd.read_csv("data2.csv")}
    embeddings = {0: np.load("embeddings.npy"), 1: np.load("embeddings2.npy")}
    for k in [0, 1]:
        embeddings[k] = embeddings[k] - np.mean(embeddings[k], axis=0)
        embeddings[k] = embeddings[k] / np.linalg.norm(
            embeddings[k], axis=1, keepdims=True
        )
    return model, processor, df, embeddings


model, processor, df, embeddings = load()
source = {0: "\nSource: Unsplash", 1: "\nSource: The Movie Database (TMDB)"}


def compute_text_embeddings(list_of_strings):
    inputs = processor(text=list_of_strings, return_tensors="pt", padding=True)
    result = model.get_text_features(**inputs).detach().numpy()
    return result / np.linalg.norm(result, axis=1, keepdims=True)


def image_search(query, corpus, n_results=24):
    positive_embeddings = None

    def concatenate_embeddings(e1, e2):
        if e1 is None:
            return e2
        else:
            return np.concatenate((e1, e2), axis=0)

    splitted_query = query.split("/")

    positive_queries = splitted_query[0].split(";")
    for positive_query in positive_queries:
        match = re.match(r"\[(Movies|Unsplash):(\d{1,5})\](.*)", positive_query)
        if match:
            corpus2, idx, remainder = match.groups()
            idx, remainder = int(idx), remainder.strip()
            k = 0 if corpus2 == "Unsplash" else 1
            positive_embeddings = concatenate_embeddings(
                positive_embeddings, embeddings[k][idx : idx + 1, :]
            )
            if len(remainder) > 0:
                positive_embeddings = concatenate_embeddings(
                    positive_embeddings, compute_text_embeddings([remainder])
                )
        else:
            positive_embeddings = concatenate_embeddings(
                positive_embeddings, compute_text_embeddings([positive_query])
            )
    k = 0 if corpus == "Unsplash" else 1
    dot_product = embeddings[k] @ positive_embeddings.T
    dot_product = dot_product - np.mean(dot_product, axis=0)
    dot_product = dot_product / np.linalg.norm(dot_product, axis=0)
    dot_product = np.min(dot_product, axis=1)

    if len(splitted_query) > 1:
        negative_queries = (" ".join(splitted_query[1:])).split(";")
        negative_embeddings = compute_text_embeddings(negative_queries)
        dot_product2 = embeddings[k] @ negative_embeddings.T
        dot_product2 = dot_product2 - np.mean(dot_product2, axis=0)
        dot_product2 = dot_product2 / np.linalg.norm(dot_product2, axis=0)
        dot_product -= np.max(dot_product2, axis=1)

    results = np.argsort(dot_product)[-1 : -n_results - 1 : -1]
    return [
        (
            df[k].iloc[i]["path"],
            df[k].iloc[i]["tooltip"] + source[k],
            i,
        )
        for i in results
    ]


description = """
# Semantic image search

**Enter your query and hit enter**

*Built with OpenAI's [CLIP](https://openai.com/blog/clip/) model, 🤗 Hugging Face's [transformers library](https://huggingface.co/transformers/), [Streamlit](https://streamlit.io/), 25k images from [Unsplash](https://unsplash.com/) and 8k images from [The Movie Database (TMDB)](https://www.themoviedb.org/)*

*Inspired by [Unsplash Image Search](https://github.com/haltakov/natural-language-image-search) from Vladimir Haltakov and [Alph, The Sacred River](https://github.com/thoppe/alph-the-sacred-river) from Travis Hoppe*
"""


def main():
    st.markdown(
        """
              <style>
              .block-container{
                max-width: 1200px;
              }
              div.row-widget.stRadio > div{
                flex-direction:row;
                display: flex;
                justify-content: center;
              }
              div.row-widget.stRadio > div > label{
                margin-left: 5px;
                margin-right: 5px;
              }
              section.main>div:first-child {
                padding-top: 0px;
              }
              section:not(.main)>div:first-child {
                padding-top: 30px;
              }
              div.reportview-container > section:first-child{
                max-width: 320px;
              }
              #MainMenu {
                visibility: hidden;
              }
              footer {
                visibility: hidden;
              }
              </style>""",
        unsafe_allow_html=True,
    )
    st.sidebar.markdown(description)
    _, c, _ = st.columns((1, 3, 1))
    if "query" in st.session_state:
        query = c.text_input("", value=st.session_state["query"])
    else:
        query = c.text_input("", value="clouds at sunset")
    corpus = st.radio("", ["Unsplash", "Movies"])
    if len(query) > 0:
        results = image_search(query, corpus)
        clicked = clickable_images(
            [result[0] for result in results],
            titles=[result[1] for result in results],
            div_style={
                "display": "flex",
                "justify-content": "center",
                "flex-wrap": "wrap",
            },
            img_style={"margin": "2px", "height": "200px"},
        )
        if clicked >= 0:
            change_query = False
            if "last_clicked" not in st.session_state:
                change_query = True
            else:
                if clicked != st.session_state["last_clicked"]:
                    change_query = True
            if change_query:
                st.session_state["query"] = f"[{corpus}:{results[clicked][2]}]"
                st.experimental_rerun()


if __name__ == "__main__":
    main()