{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/lilianweng/emoji-semantic-search/blob/main/server/app.py"
      ],
      "metadata": {
        "id": "c-4HGfHNbNI4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lIYdn1woOS1n"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q transformers sentence-transformers datasets jsonlines"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/lilianweng/emoji-semantic-search/raw/refs/heads/main/server/emoji-embeddings.jsonl.gz"
      ],
      "metadata": {
        "id": "Vg_Yz2KJVaGo",
        "outputId": "9da3682e-e166-43ee-dde5-8e40dfa9af77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-12-08 08:15:22--  https://github.com/lilianweng/emoji-semantic-search/raw/refs/heads/main/server/emoji-embeddings.jsonl.gz\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/lilianweng/emoji-semantic-search/refs/heads/main/server/emoji-embeddings.jsonl.gz [following]\n",
            "--2024-12-08 08:15:22--  https://raw.githubusercontent.com/lilianweng/emoji-semantic-search/refs/heads/main/server/emoji-embeddings.jsonl.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 49086598 (47M) [application/octet-stream]\n",
            "Saving to: ‘emoji-embeddings.jsonl.gz’\n",
            "\n",
            "emoji-embeddings.js 100%[===================>]  46.81M   287MB/s    in 0.2s    \n",
            "\n",
            "2024-12-08 08:15:24 (287 MB/s) - ‘emoji-embeddings.jsonl.gz’ saved [49086598/49086598]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gzip\n",
        "import os\n",
        "import jsonlines\n",
        "import pandas as pd\n",
        "\n",
        "# Filepath to the compressed embeddings file\n",
        "EMBED_FILE = \"./emoji-embeddings.jsonl.gz\"  # Replace with the correct path\n",
        "\n",
        "def load_emoji_embeddings_to_dataframe(embed_file: str) -> pd.DataFrame:\n",
        "    \"\"\"\n",
        "    Loads emoji embeddings and metadata into a pandas DataFrame.\n",
        "\n",
        "    Args:\n",
        "        embed_file (str): Path to the gzip-compressed JSONL file containing emoji embeddings.\n",
        "\n",
        "    Returns:\n",
        "        pd.DataFrame: A DataFrame with columns for emoji, message, and embedding.\n",
        "    \"\"\"\n",
        "    with gzip.GzipFile(fileobj=open(embed_file, \"rb\"), mode=\"rb\") as fin:\n",
        "        emoji_info = list(jsonlines.Reader(fin))\n",
        "\n",
        "    # Convert the data into a DataFrame\n",
        "    df = pd.DataFrame(emoji_info)\n",
        "\n",
        "    # Retain \"embed\" as a list within a single column\n",
        "    df[\"embed\"] = df[\"embed\"].apply(lambda x: list(x))\n",
        "    return df\n",
        "\n",
        "# Usage\n",
        "emoji_df = load_emoji_embeddings_to_dataframe(EMBED_FILE)\n",
        "\n",
        "# Display the structure of the DataFrame (columns: emoji, message, embed)\n",
        "print(emoji_df.head())"
      ],
      "metadata": {
        "id": "u3hDX1RGVmLL",
        "outputId": "82db94e0-3fb6-4b32-8278-f703289bda3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  emoji                   message  \\\n",
            "0     🥇           1st place medal   \n",
            "1     🥈           2nd place medal   \n",
            "2     🥉           3rd place medal   \n",
            "3     🆎    ab button (blood type)   \n",
            "4     🏧  automated teller machine   \n",
            "\n",
            "                                               embed  \n",
            "0  [-0.017666909843683243, -0.0048620556481182575...  \n",
            "1  [-0.02306770719587803, -0.0014851171290501952,...  \n",
            "2  [-0.02444339357316494, -0.0023657570127397776,...  \n",
            "3  [-0.02489371970295906, -0.024508081376552582, ...  \n",
            "4  [-0.02711508981883526, -0.017816530540585518, ...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Requires sentence_transformers>=2.7.0\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sentence_transformers.util import cos_sim, semantic_search\n",
        "\n",
        "sentences = ['That is a happy person', 'That is a very happy person']\n",
        "\n",
        "model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)\n",
        "embeddings = model.encode(sentences)\n",
        "print(cos_sim(embeddings[0], embeddings[1]))"
      ],
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        "id": "1gL6zdczWNdc",
        "outputId": "c6a93064-3a7d-44a8-c0cf-d286775a371f",
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      },
      "execution_count": null,
      "outputs": [
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/Alibaba-NLP/new-impl:\n",
            "- configuration.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/Alibaba-NLP/new-impl:\n",
            "- modeling.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "output_type": "stream",
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          "text": [
            "tensor([[0.9769]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texts = emoji_df.message.tolist()\n",
        "emoji_embeds = model.encode(texts)"
      ],
      "metadata": {
        "id": "P5gji_0cXN8e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emoji_embeds"
      ],
      "metadata": {
        "id": "xKFOPmv-XcNB",
        "outputId": "82ddf06b-1345-4aed-87fe-b2589a43c98f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.70429766,  0.40987685, -0.40623984, ...,  0.34447536,\n",
              "        -1.1676097 ,  0.60690004],\n",
              "       [-0.8834242 ,  0.41458154, -0.52103883, ...,  0.6835418 ,\n",
              "        -0.8694557 ,  0.26585358],\n",
              "       [-0.8940514 ,  0.4233047 , -1.1000963 , ..., -0.2655996 ,\n",
              "        -0.5408825 ,  0.51726294],\n",
              "       ...,\n",
              "       [-1.0322247 ,  0.31219995, -0.61489904, ...,  0.3374904 ,\n",
              "        -0.7023771 , -1.6981932 ],\n",
              "       [-0.16666836,  0.6253868 , -0.15234776, ...,  1.9760971 ,\n",
              "        -0.6701496 , -0.44981623],\n",
              "       [-0.5770006 , -0.17587984,  0.08439391, ...,  1.3222466 ,\n",
              "         0.20345928,  0.0735295 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.util import cos_sim, semantic_search\n",
        "query_text = \"urgent exclamation\"\n",
        "query_embed = model.encode(query_text)\n",
        "hits = semantic_search(query_embed, emoji_embeds, top_k=5)"
      ],
      "metadata": {
        "id": "9sgbZdMdX21h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hits"
      ],
      "metadata": {
        "id": "rgBO_YU-Yjd2",
        "outputId": "88bea4ec-fbad-4367-fb4f-b01106c58322",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'corpus_id': 3153, 'score': 0.7861719727516174},\n",
              "  {'corpus_id': 877, 'score': 0.7329143285751343},\n",
              "  {'corpus_id': 1031, 'score': 0.7237887382507324},\n",
              "  {'corpus_id': 3154, 'score': 0.721806526184082},\n",
              "  {'corpus_id': 2749, 'score': 0.70970618724823}]]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emoji_df.loc[2749]"
      ],
      "metadata": {
        "id": "PbYHZ0YcYkav",
        "outputId": "ee778775-c9b3-4cad-ed5d-097f3b4d68d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emoji                                                      ❗\n",
              "message                        heavy exclamation mark symbol\n",
              "embed      [-0.011823254637420177, -0.02413378655910492, ...\n",
              "Name: 2749, dtype: object"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2749</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>emoji</th>\n",
              "      <td>❗</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>message</th>\n",
              "      <td>heavy exclamation mark symbol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>embed</th>\n",
              "      <td>[-0.011823254637420177, -0.02413378655910492, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zczg1uW6YyVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "eK3s3_TWbGzS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Function to perform semantic search\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sentence_transformers.util import cos_sim, semantic_search\n",
        "\n",
        "def get_top_emojis(\n",
        "    query: str,\n",
        "    emoji_df: pd.DataFrame,\n",
        "    model,\n",
        "    top_k: int = 5,\n",
        "    num_digits: int = 4,\n",
        ") -> pd.DataFrame:\n",
        "    \"\"\"\n",
        "    Performs semantic search to find the most relevant emojis for a given query.\n",
        "\n",
        "    Args:\n",
        "        query (str): The search query.\n",
        "        emoji_df (pd.DataFrame): DataFrame containing emoji metadata and embeddings.\n",
        "        model (SentenceTransformer): The sentence transformer model for encoding.\n",
        "        top_k (int): Number of top results to return.\n",
        "\n",
        "    Returns:\n",
        "        pd.DataFrame: A DataFrame containing the top emojis with scores.\n",
        "    \"\"\"\n",
        "    # Encode the query text\n",
        "    query_embed = model.encode(query)\n",
        "\n",
        "    embeddings_array = np.vstack(emoji_df.embed.values, dtype=np.float32)\n",
        "\n",
        "    # Perform semantic search\n",
        "    hits = semantic_search(query_embed, embeddings_array, top_k=top_k)[0]\n",
        "\n",
        "    # Extract the top hits and their metadata\n",
        "    results = [\n",
        "        {\n",
        "            \"emoji\": emoji_df.loc[hit[\"corpus_id\"], \"emoji\"],\n",
        "            \"message\": emoji_df.loc[hit[\"corpus_id\"], \"message\"],\n",
        "            \"score\": round(hit[\"score\"], num_digits),\n",
        "        }\n",
        "        for hit in hits\n",
        "    ]\n",
        "    return results"
      ],
      "metadata": {
        "id": "k_1BJFlWaMGi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pprint as pp\n",
        "\n",
        "# Example usage\n",
        "query_text = \"urgent exclamation\"\n",
        "top_emojis = get_top_emojis(query_text, emoji_df, model, top_k=5)\n",
        "\n",
        "# Display the results\n",
        "pp.pprint(top_emojis, indent=2)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Cr6-6gyaMwT",
        "outputId": "17e1560d-d204-462c-d182-f903c5bee178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ {'emoji': '⚠️', 'message': 'warning', 'score': 0.7862},\n",
            "  {'emoji': '‼', 'message': 'double exclamation mark', 'score': 0.7329},\n",
            "  {'emoji': '🔥', 'message': 'fire', 'score': 0.7238},\n",
            "  {'emoji': '⚠', 'message': 'warning sign', 'score': 0.7218},\n",
            "  {'emoji': '❗', 'message': 'heavy exclamation mark symbol', 'score': 0.7097}]\n"
          ]
        }
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