Create app.py

app.py

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+## Import
+## ----------------
+import pandas as pd
+import streamlit as st
+from sentence_transformers import SentenceTransformer, util
+
+## Init
+## ----------------
+# set config
+st.set_page_config(layout="wide", page_title="emoji_suggestion 🕵")
+
+# load the sbert model (cache for faster loading)
+@st.cache(allow_output_mutation=True)
+def load_similarity_model(model_name='all-MiniLM-L6-v2'):
+    model = SentenceTransformer(model_name)
+    return model
+
+# list of supported models
+supported_models = ['all-MiniLM-L6-v2', 'paraphrase-albert-small-v2', 'paraphrase-MiniLM-L3-v2', 'all-distilroberta-v1', 'all-mpnet-base-v2']
+
+# read the emoji df and extract the relevant columns
+emoji_df = pd.read_csv('EmojiCharts_unicodeorg.csv')[['name', 'codepoints']]
+
+# function to encode and decode the emoji text
+def encode_emoji(emoji):
+    emoji_text = ""
+    emoji = emoji.replace("U+", "")
+    if len(emoji) == 4:
+        emoji_text = f"\\U0000{emoji}"
+    elif len(emoji) == 5:
+        emoji_text = f"\\U000{emoji}"
+    return emoji_text.encode().decode('unicode-escape')
+
+# find the top similar sentences
+def find_similar_sentences(query, target_sentences, n=5):
+    # compute embeddings
+    embeddings_query = model.encode([query], convert_to_tensor=True)
+    embeddings_target = model.encode(target_sentences, convert_to_tensor=True)
+    cosine_scores = util.pytorch_cos_sim(embeddings_query, embeddings_target)
+    # return the index of top 5 values in a list
+    score_list = cosine_scores.tolist()[0]
+    top_indices = sorted(range(len(score_list)), key=lambda i: score_list[i], reverse=True)[:n]
+    return top_indices
+
+# settings
+selected_model_name = st.sidebar.selectbox('Similarity model', options=supported_models) 
+emoji_count = st.sidebar.slider('Emoji output count', min_value=1, max_value=10, value=5, step=1)
+
+# title and headers
+st.title("emoji_suggestion 🕵")
+st.markdown("""**Semantic Search** for Emojis, allowing you to find emojis for any situation, like when you’re hungry and need an emoji-inspired meal plan for the month. just emojis hungry 🍲 🥫 🍽️ 🍟 🥣 🫘 🍫 🌯 😋 🧑‍🍼 🥮 🌭""")
+query_text = st.text_area("Enter your text here: ", "I am hungry")
+find_button = st.button("Submit")
+
+# load the model
+model = load_similarity_model(selected_model_name)
+
+# callback
+with st.spinner("EmojiFinder is looking for clues to find the best emoji...."):
+    if find_button:
+        # fidn the top N similar sentences
+        top_indices = find_similar_sentences(query_text, emoji_df['name'], emoji_count)
+        # print the emojis
+        for i in top_indices:
+            emoji = emoji_df.iloc[i]
+            # prep the text
+            text = f'{emoji["name"]} - '
+            # add all of the codepoints
+            text += ' '.join([encode_emoji(x) for x in emoji['codepoints'].split(' ')])
+            st.write(text)