# import altair as alt # import numpy as np # import pandas as pd # import streamlit as st # """ # # Welcome to Streamlit! # Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:. # If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community # forums](https://discuss.streamlit.io). # In the meantime, below is an example of what you can do with just a few lines of code: # """ # num_points = st.slider("Number of points in spiral", 1, 10000, 1100) # num_turns = st.slider("Number of turns in spiral", 1, 300, 31) # indices = np.linspace(0, 1, num_points) # theta = 2 * np.pi * num_turns * indices # radius = indices # x = radius * np.cos(theta) # y = radius * np.sin(theta) # df = pd.DataFrame({ # "x": x, # "y": y, # "idx": indices, # "rand": np.random.randn(num_points), # }) # st.altair_chart(alt.Chart(df, height=700, width=700) # .mark_point(filled=True) # .encode( # x=alt.X("x", axis=None), # y=alt.Y("y", axis=None), # color=alt.Color("idx", legend=None, scale=alt.Scale()), # size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])), # )) import streamlit as st import numpy as np import pickle from typing import Dict, List, Any import random from sentence_transformers import SentenceTransformer from qdrant_client import models, QdrantClient import emoji as em import warnings warnings.filterwarnings('ignore') # A function to load the emoji dictionary @st.cache_data(show_spinner=False) def load_dictionary(file_path: str) -> Dict[str, Dict[str, Any]]: """Load the emoji dictionary from a pickle file.""" with open(file_path, 'rb') as file: emoji_dict = pickle.load(file) return emoji_dict # A function to load the sentence encoder model @st.cache_resource(show_spinner=False) def load_encoder(model_name: str) -> SentenceTransformer: """Load a sentence encoder model from Hugging Face Hub.""" sentence_encoder = SentenceTransformer(model_name) #st.session_state.sentence_encoder = sentence_encoder return sentence_encoder # A function to load the Qdrant vector DB client @st.cache_resource(show_spinner=False) def load_qdrant_client(emoji_dict: Dict[str, Dict[str, Any]]) -> QdrantClient: """ Load a Qdrant client and populate the database with embeddings. """ # Setup the Qdrant client and populate the database vector_DB_client = QdrantClient(":memory:") embedding_dict = { emoji: np.array(metadata['embedding']) for emoji, metadata in emoji_dict.items() } # Remove the embeddings from the dictionary so it can be used # as payload in Qdrant for emoji in list(emoji_dict): del emoji_dict[emoji]['embedding'] embedding_dim = next(iter(embedding_dict.values())).shape[0] # Create collection in Qdrant vector_DB_client.create_collection( collection_name="EMOJIS", vectors_config=models.VectorParams( size=embedding_dim, distance=models.Distance.COSINE ), ) # Upload points to the collection vector_DB_client.upload_points( collection_name="EMOJIS", points=[ models.PointStruct( id=idx, vector=embedding_dict[emoji].tolist(), payload=emoji_dict[emoji] ) for idx, emoji in enumerate(emoji_dict) ], ) #st.session_state.vector_DB_client = vector_DB_client return vector_DB_client # for the offline version this code was faster, but resulted in a resource # limits error from online streamlit app # it seems that each user has its own session, thus caching does not help # much here, and the resources are loaded for each user # def load_resources(): # if ('vector_DB_client' not in st.session_state # or 'sentence_encoder' not in st.session_state): # # Load emoji dictionary # with open('emoji_embeddings_dict.pkl', 'rb') as file: # emoji_dict = pickle.load(file) # # Load sentence encoder # embedding_model = 'paraphrase-multilingual-MiniLM-L12-v2' # sentence_encoder = SentenceTransformer(embedding_model) # st.session_state.sentence_encoder = sentence_encoder # # Setup the Qdrant client and populate the database # vector_DB_client = QdrantClient(":memory:") # embedding_dict = { # emoji: np.array(data['embedding']) # for emoji, data in emoji_dict.items() # } # for emoji in list(emoji_dict): # del emoji_dict[emoji]['embedding'] # embedding_dim = next(iter(embedding_dict.values())).shape[0] # # Create collection in Qdrant # vector_DB_client.create_collection( # collection_name="EMOJIS", # vectors_config=models.VectorParams( # size=embedding_dim, # distance=models.Distance.COSINE # ), # ) # # Upload points to the collection # vector_DB_client.upload_points( # collection_name="EMOJIS", # points=[ # models.PointStruct( # id=idx, # vector=embedding_dict[emoji].tolist(), # payload=emoji_dict[emoji] # ) # for idx, emoji in enumerate(emoji_dict) # ], # ) # st.session_state.vector_DB_client = vector_DB_client def retrieve_relevant_emojis( embedding_model: SentenceTransformer, vector_DB_client: QdrantClient, query: str) -> List[str]: """ Return similar emojis to the query using the sentence encoder and Qdrant. """ # Embed the query query_vector = embedding_model.encode(query).tolist() hits = vector_DB_client.search( collection_name="EMOJIS", query_vector=query_vector, limit=50, ) search_emojis = [] # only add to list if it is not already an item in the list for hit in hits: if hit.payload['Emoji'] not in search_emojis: search_emojis.append(hit.payload['Emoji']) return search_emojis def render_results( embedding_model: SentenceTransformer, vector_DB_client: QdrantClient, query: str, emojis_to_render: List[str] = None,) -> None: """ Render the search results in the Streamlit app. """ # Retrieve relevant emojis if emojis_to_render is None: emojis_to_render = retrieve_relevant_emojis( embedding_model, vector_DB_client, query ) #with st.empty(): # Display results as HTML #placeholder = st.empty() if emojis_to_render: st.markdown( '

' + '\t'.join(emojis_to_render) + '

', unsafe_allow_html=True ) else: st.error("No results found.") def main(): # Examples queries to show example_queries = [ "Extraterrestrial form", "Exploration & discovery", "Happy birthday", "Love and peace", "Beyond the stars", "Great ambition", "Career growth", "Flightless bird", "Tropical vibes", "Gift of nature", "In the ocean ", "Spring awakening", "Autumn vibes", "In the garden", "In the desert", "Heart gesture", "Love is in the air", "In the mountains", "Extinct species", "Wonderful world", "Cool vibes", "Warm feelings", "Academic excellence", "Artistic expression", "Urban life", "Rural life", "Sign language", "Global communication", "International cooperation", "Worldwide connection", "Digital transformation", "AI-powered solutions", "New beginnings", "Innovation & creativity", "Scientific discovery", "Space exploration", "Sustainable development", "Climate change", "Environmental protection", "Healthy lifestyle", "Mental health", "Healthy food", "Healthy habits", "Fitness & wellness", "Mindfulness & meditation", "Emotional intelligence", "Personal growth", "Financial freedom", "Investment opportunities", "Economic growth", "Traditional crafts", "Folk music", "Cultural shock", "Illuminating thoughts", ] # Load the sentence encoder model #if 'sentence_encoder' not in st.session_state: model_name = 'paraphrase-multilingual-MiniLM-L12-v2' #model_name = 'paraphrase-multilingual-mpnet-base-v2' sentence_encoder = load_encoder(model_name) # Load metadata dictionary embedding_dict = load_dictionary('/home/user/app/src/emoji_embeddings_dict.pkl') # git a list of emojis emojis = list(embedding_dict.keys()) # Load the Qdrant client #if 'vector_DB_client' not in st.session_state: vector_DB_clinet = load_qdrant_client(embedding_dict) st.title("Emojeez πŸ’Ž ") # ({languages_link}) languages_link = "https://github.com/badrex/emojeez/blob/main/LANGUAGES" app_description = f""" AI-powered, multilingual semantic search for emojis in 50+ languages 🌐 """ app_example = """ ⌨️ For example, type β€œ hit the target ” or β€œ illuminating ” below """ st.text(app_description) #query = st.text_input("Enter your search query", "") # Using columns to layout the input and button next to each other with st.container(border=True): random_query = random.sample(example_queries, 1)[0] if 'input_text' not in st.session_state: st.session_state.input_text = random_query #"" instr = f'Enter your text query here ...' # For example `illuminating thoughtsΒ΄ st.caption(app_example) #col1, col2, col3 = st.columns([3.5, 1.3, 1.3]) col1, col2 = st.columns([3.5, 1]) with col1: query = st.text_input( instr, value="", #st.session_state.input_text, placeholder=instr, label_visibility='collapsed', #label_visibility='visible', help="exploration discovery", on_change=lambda: st.session_state.update({ 'enter_clicked': True } ) #key="query_input" ) #Enter your search query with col2: trigger_search = st.button( label="✨ Find emojis!", use_container_width=True ) # with col3: # trigger_explore = st.button( # label="Randomize 🎲", # use_container_width=True # ) # create an empty container to show the resukts #placeholder = st.empty() #with st.empty(): # Trigger search if the search button is clicked or user clicked Ebitnter if trigger_search or (st.session_state.get('enter_clicked') and query): if query: render_results( sentence_encoder, vector_DB_clinet, query ) #st.session_state['enter_clicked'] = False else: st.error("Please enter a query of a few keywords to search!") # # Trigger explore if the Explore button is clicked # if trigger_explore: # # get a list of 50 random emojis # random_emojis = random.sample(emojis, 50) # render_results( # sentence_encoder, # vector_DB_clinet, # "", # emojis_to_render=random_emojis # ) # Footer footer = """
Developed with πŸ’š by Badr Alabsi
πŸ“   Blog Post   |   🌐   Supported Languages   |   πŸš€   Code on GitHub
""" # Use columns to visually separate the footer from the form content footer_column = st.columns(1) # Creates a full-width column with footer_column[0]: st.markdown(footer, unsafe_allow_html=True) if __name__ == "__main__": main()