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README.txt

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+---
+title: 🔎NLP Image Semantic Search SL🖼️
+emoji: 🔎🖼️
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.2.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+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-large-patch14")
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+    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.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("EXCLUDING ")
+    dot_product = 0
+    k = 0 if corpus == "Unsplash" else 1
+    if len(splitted_query[0]) > 0:
+        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()
+                k2 = 0 if corpus2 == "Unsplash" else 1
+                positive_embeddings = concatenate_embeddings(
+                    positive_embeddings, embeddings[k2][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])
+                )
+        dot_product = embeddings[k] @ positive_embeddings.T
+        dot_product = dot_product - np.median(dot_product, axis=0)
+        dot_product = dot_product / np.max(dot_product, axis=0, keepdims=True)
+        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.median(dot_product2, axis=0)
+        dot_product2 = dot_product2 / np.max(dot_product2, axis=0, keepdims=True)
+        dot_product -= np.max(np.maximum(dot_product2, 0), 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**
+"""
+
+howto = """
+- Click image to find similar images
+- Use "**;**" to combine multiple queries)
+- Use "**EXCLUDING**", to exclude a query
+"""
+
+
+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)
+    with st.sidebar.expander("Advanced use"):
+        st.markdown(howto)
+
+
+    st.sidebar.markdown(f"Try these test prompts: orange, blue, beach, lighthouse, mountain, sunset, parade")
+    st.sidebar.markdown(f"Unsplash has categories that match: backgrounds, photos, nature, iphone, etc")
+    st.sidebar.markdown(f"Unsplash images contain animals, apps, events, feelings, food, travel, nature, people, religion, sports, things, stock")
+    st.sidebar.markdown(f"Unsplash things include flag, tree, clock, money, tattoo, arrow, book, car, fireworks, ghost, health, kiss, dance, balloon, crown, eye, house, music, airplane, lighthouse, typewriter, toys")
+    st.sidebar.markdown(f"unsplash feelings include funny, heart, love, cool, congratulations, love, scary, cute, friendship, inspirational, hug, sad, cursed, beautiful, crazy, respect, transformation, peaceful, happy")
+    st.sidebar.markdown(f"unsplash people contain baby, life, women, family, girls, pregnancy, society, old people, musician, attractive, bohemian")
+    st.sidebar.markdown(f"imagenet queries include: photo of, photo of many, sculpture of, rendering of, graffiti of, tattoo of, embroidered, drawing of, plastic, black and white, painting, video game, doodle, origami, sketch, etc")
+    
+    
+    _, 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="lighthouse")
+    corpus = st.radio("", ["Unsplash"])
+    #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()

embeddings.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:64515f7d3d71137e2944f2c3d72c8df3e684b5d6a6ff7dcebb92370f7326ccfd
+size 76800128

embeddings2.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d730b33e758c2648419a96ac86d39516c59795e613c35700d3a64079e5a9a27
+size 25098368

requirements.txt

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+torch
+transformers
+numpy
+pandas
+st-clickable-images