image_search_with_vector_db.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers qdrant_client datasets torch sentence_transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWVN7EXxpJYR",
        "outputId": "3ffe0695-f15e-4318-cdaf-a2942211ff5b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: sentencepiece, urllib3, safetensors, protobuf, portalocker, hyperframe, hpack, h11, grpcio, dill, multiprocess, httpcore, h2, grpcio-tools, huggingface-hub, httpx, tokenizers, datasets, transformers, qdrant_client, sentence_transformers\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.25.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.14.6 dill-0.3.7 grpcio-1.59.2 grpcio-tools-1.59.2 h11-0.14.0 h2-4.1.0 hpack-4.0.0 httpcore-0.18.0 httpx-0.25.0 huggingface-hub-0.17.3 hyperframe-6.0.1 multiprocess-0.70.15 portalocker-2.8.2 protobuf-4.25.0 qdrant_client-1.6.4 safetensors-0.4.0 sentence_transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.1 urllib3-1.26.18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6YyRMY72o-5h"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "from PIL import Image\n",
        "from qdrant_client import QdrantClient\n",
        "from qdrant_client.http import models\n",
        "from datasets import load_dataset\n",
        "import numpy as np\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "kd_nVfcVo-5k"
      },
      "outputs": [],
      "source": [
        "#connect to the vector database --> get those here :https://cloud.qdrant.io/\n",
        "from google.colab import userdata\n",
        "qdrant_url = userdata.get('qdrant_url')\n",
        "qdrant_api_key =  userdata.get('qdrant_api_key')\n",
        "\n",
        "\n",
        "client = QdrantClient(\n",
        "    url=qdrant_url,\n",
        "    api_key=qdrant_api_key\n",
        "     )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0c_gGdSo-5l",
        "outputId": "b1192d5b-9c6d-4f49-f291-36be6fe6d58c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# create collection\n",
        "my_collection = \"image_collection_test\"\n",
        "client.recreate_collection(\n",
        "    collection_name=my_collection,\n",
        "    vectors_config=models.VectorParams(size=512, distance=models.Distance.COSINE)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxxOmDhBo-5n"
      },
      "source": [
        "## 3. Image Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "bda020ec723844a79603e535ec80775f",
            "691c7980661b4d11bd08e7ecacecda52",
            "44556f98b36c4af8869ea7c8af928f3b",
            "d7a82f273fd8427e8c1a83b75648a91a",
            "1d0597e8188e4dc9a78b0f1c68d090f5",
            "891fd3a3c54e4f288104453879572823",
            "d12e881b60dd433ba0a66b71ff941330",
            "7ba0790f2df241b8b024b678e054b631",
            "ab4a8250d76e43628eedaf70ea479234",
            "3ccaa3055a704af0bbd3400fedfc7994",
            "fe4c783c37d84995842f11b5a8db75d9",
            "e17532ae835e4ac7b996b835ef8a8370",
            "7c15f8f4315b426ab1310a1ec1c56e8d",
            "3df9d6b413cd4f4689b04a7c5751572d",
            "ff71675f6e3b4b0086843b12fd68b37e",
            "52473c525e1440d8a2451e31e7c2dad5",
            "1900cfb3f36e4ae18739603b5caec906",
            "ed931e053eaf4563988d163296d203b1",
            "29aaa81f10d84caaba089abe0b49b63d",
            "4d1a275ec25845768c4ba9d28ad02ac1",
            "e1ceaa5a090945c2967e9bdad344096a",
            "212b023f802c402b8d5a6b1571e1f518",
            "618321a91f3749a291cc6283d1494830",
            "341663a47c00430685fd4f1a32376932",
            "2236c7418af8498992ffa033021e4dc0",
            "349dec7efb2a41cf83ef9507f4d8142f",
            "b84b72f0b8de43d1ba9e088d132467e6",
            "6e98dcd6ef14468dab87bc2ccbd07306",
            "56f1e0a39ffe48a5b98ba4390bf7e521",
            "a2a7d7bc93914a858ef9a8050fa03ea2",
            "0a77930df7534d4e8e77b99434ee6ab2",
            "7b07c1ef0e2945b18e529080e24b4a9c",
            "8941242392c8489c96b49633556503cc",
            "6afaa612ff0b4127b8e180a6212be2d0",
            "f9727cba3e6d40039b9e8a50956dfe3f",
            "fe72bc0029504ecdb05ab61f40f625f5",
            "1ce3e25214df49d3b2b2643735ffc309",
            "f820177bdf9b41adac8006c2c7f5d7b3",
            "9120cefcae484e9ab58db9327bd3abd5",
            "ae808e374a0749c6be4a59e37d3d59d9",
            "f737aabd73a04f1b8883c83c5249cb9e",
            "ceb523f9ba7f4a42867bcbd2f80bfea3",
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            "11f041f24b7943608f7609aef70a435c",
            "1620684537e74c7f810a09590435c9b8",
            "c16e3f8dfba74bb2ac97c793a43d9b2b",
            "51206019332543f7a0071aa8da418252",
            "065cab00bbcf4708a81f964149a4af18",
            "aa4483059946486ba1c21a33bc99832b",
            "0ca83873c71e4dc483b06e70f30509a2",
            "d4d5f70dac134e66b5494875f96c3b14",
            "4a93bdfc98c145dcb7431388bad8d15b",
            "5701fc589f744639b18bd8b32651a915",
            "8c6d0d6de9fc48069a9930037c3a2d2c",
            "bc8e94abe18c4a1db0cb55ec39374f74",
            "5b18fcea653f4c0d8dae07cb631c63b7",
            "cb3e6bf481a94369b4c03eef134b36b3",
            "bb14387d4d0b41a9a4df970ef0142dc5",
            "fab1453807c24c05a13abcdf15124074",
            "864bc7b43ad0452cb715aee2e5689913",
            "86497f15348042008c6a7489c2620a9f",
            "b5c45111b78c4c40a3a9906c4b11df3b",
            "fe7ce3cf87eb46acb3b2f2139cc35888",
            "a8ae21cd9ba74f36be0658ee35db36b0",
            "b1fd9e662d9445b78fc954978f301c4d"
          ]
        },
        "id": "5GM14u1eo-5p",
        "outputId": "def96a94-2b2e-4ac1-aead-7cd30676b236"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/867 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bda020ec723844a79603e535ec80775f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e17532ae835e4ac7b996b835ef8a8370"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/136M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "618321a91f3749a291cc6283d1494830"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/135M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6afaa612ff0b4127b8e180a6212be2d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11f041f24b7943608f7609aef70a435c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/44072 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc8e94abe18c4a1db0cb55ec39374f74"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# get the data\n",
        "dataset = load_dataset(\n",
        "     'ashraq/fashion-product-images-small', split='train[:20000]'\n",
        ")\n",
        "\n",
        "values= list(range(len(dataset)))\n",
        "dataset=dataset.add_column('image_id',values)\n",
        "\n",
        "dataset_test = load_dataset(\n",
        "     'ashraq/fashion-product-images-small', split='train[20000:30000]'\n",
        ")\n",
        "\n",
        "values= list(range(len(dataset_test)))\n",
        "dataset_test=dataset_test.add_column('image_id',values)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[0]['image']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "SyhJpjv20a2T",
        "outputId": "c0e71e72-f3e8-4bb0-fc6a-b9f43b801589"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=60x80>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#use this if image is in url format.\n",
        "def download_image(url):\n",
        "    response = requests.get(url, stream=True)\n",
        "    response.raise_for_status()\n",
        "    image = PILImage.open(response.raw)\n",
        "    return image\n",
        "\n",
        "def to_pillow(examples):\n",
        "    urls = examples['Photo']\n",
        "    images = [download_image(url) for url in urls]\n",
        "    examples['image'] = images\n",
        "    return examples\n",
        "#dataset_train = dataset_train.map(to_pillow, batched=True)\n",
        "#dataset_test = dataset_test.map(to_pillow, batched=True)"
      ],
      "metadata": {
        "id": "gRSAIEmTmizB"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#embedding model\n",
        "model = SentenceTransformer(\"clip-ViT-B-32\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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        "id": "a1KgP3A_mkY3",
        "outputId": "bc46b642-f34f-48ae-d0ab-30d4b7102671"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)d52eb/.gitattributes:   0%|          | 0.00/690 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/604 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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        {
          "output_type": "display_data",
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        {
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        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "# Define a function to encode images and add embeddings to the dataset\n",
        "def add_embeddings(example):\n",
        "    image = example['image']\n",
        "    image_embedding = model.encode(image).tolist()\n",
        "    example['embeddings'] = image_embedding\n",
        "    return example\n",
        "\n",
        "# Apply the function to each example in the dataset\n",
        "dataset = dataset.map(add_embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "-lhINeHHK9JT",
        "outputId": "cb280ed0-d7a6-49bd-ddf2-77f19d60fcea"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/20000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZeYAqTno-5z",
        "outputId": "bf925d24-5a89-4ca5-dea4-e8b254d1157e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 1855,\n",
              " 'gender': 'Men',\n",
              " 'masterCategory': 'Apparel',\n",
              " 'subCategory': 'Topwear',\n",
              " 'articleType': 'Tshirts',\n",
              " 'baseColour': 'Grey',\n",
              " 'season': 'Summer',\n",
              " 'year': 2011.0,\n",
              " 'usage': 'Casual',\n",
              " 'productDisplayName': 'Inkfruit Mens Chain Reaction T-shirt',\n",
              " 'image_id': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "payload = dataset.select_columns(['id','gender','masterCategory', 'subCategory',\n",
        "                                  'articleType', 'baseColour', 'season', 'year',\n",
        "                                  'usage', 'productDisplayName', 'image_id'\n",
        "]).to_pandas().fillna({\"age\": 0}).to_dict(orient=\"records\")\n",
        "\n",
        "payload[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "8Rwj3gnSwoiI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "AFe4XtnBo-5z"
      },
      "outputs": [],
      "source": [
        "ids = list(range(dataset.num_rows))\n",
        "embeddings =np.array(dataset['embeddings']).tolist()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings[111]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vx4i3ZMMzrlF",
        "outputId": "18f5c575-f40b-46c4-e370-0d3d08c9d1ee"
      },
      "execution_count": 24,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_FcqK-lo-50"
      },
      "source": [
        "We are now ready to upsert the combination of ids, vectors and payload to our collection, and we'll do so in batches of 1000."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "7ea6_pLio-50"
      },
      "outputs": [],
      "source": [
        "batch_size = 1000\n",
        "\n",
        "for i in range(0, dataset.num_rows, batch_size):\n",
        "\n",
        "    low_idx = min(i+batch_size, dataset.num_rows)\n",
        "\n",
        "    batch_of_ids = ids[i: low_idx]\n",
        "    batch_of_embs = embeddings[i: low_idx]\n",
        "    batch_of_payloads = payload[i: low_idx]\n",
        "\n",
        "    client.upsert(\n",
        "        collection_name=my_collection,\n",
        "        points=models.Batch(\n",
        "            ids=batch_of_ids,\n",
        "            vectors=batch_of_embs,\n",
        "            payloads=batch_of_payloads\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoY_kiEeo-50"
      },
      "source": [
        "We can make sure our vectors were uploaded successfully by counting them with the `client.count()` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7NIJiTJo-51",
        "outputId": "6644aa8b-fd8b-4b94-f67c-20dfb7ede7b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CountResult(count=20000)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "client.count(\n",
        "    collection_name=my_collection,\n",
        "    exact=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nL6nLTrHo-51"
      },
      "source": [
        "To visually inspect the collection we just created, we can scroll through our vectors with the `client.scroll()` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0I0DfJwo-51",
        "outputId": "396575d5-2726-4523-f2d7-1a30bb52ec5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('status', <CollectionStatus.YELLOW: 'yellow'>),\n",
              " ('optimizer_status', <OptimizersStatusOneOf.OK: 'ok'>),\n",
              " ('vectors_count', 22000),\n",
              " ('indexed_vectors_count', 10000),\n",
              " ('points_count', 22000),\n",
              " ('segments_count', 3),\n",
              " ('config',\n",
              "  CollectionConfig(params=CollectionParams(vectors=VectorParams(size=512, distance=<Distance.COSINE: 'Cosine'>, hnsw_config=None, quantization_config=None, on_disk=None), shard_number=1, replication_factor=1, write_consistency_factor=1, read_fan_out_factor=None, on_disk_payload=True), hnsw_config=HnswConfig(m=16, ef_construct=100, full_scan_threshold=10000, max_indexing_threads=0, on_disk=False, payload_m=None), optimizer_config=OptimizersConfig(deleted_threshold=0.2, vacuum_min_vector_number=1000, default_segment_number=0, max_segment_size=None, memmap_threshold=None, indexing_threshold=20000, flush_interval_sec=5, max_optimization_threads=1), wal_config=WalConfig(wal_capacity_mb=32, wal_segments_ahead=0), quantization_config=None)),\n",
              " ('payload_schema', {})]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "collection_info = client.get_collection(collection_name='image_collection_test')\n",
        "list(collection_info)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = dataset_test[397]['image']\n",
        "image_embedding = model.encode(image)\n",
        "payload_dict = dataset_test[397]\n",
        "print(payload_dict)\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "x_-4M1_sQn2p",
        "outputId": "6d6d71f5-29df-4ba1-ac53-71a4d18bf903"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': 49613, 'gender': 'Women', 'masterCategory': 'Footwear', 'subCategory': 'Shoes', 'articleType': 'Heels', 'baseColour': 'Multi', 'season': 'Winter', 'year': 2015.0, 'usage': 'Casual', 'productDisplayName': 'Catwalk Women Multi Coloured Heels', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=60x80 at 0x7CF7D4F5BBB0>, 'image_id': 397}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=60x80>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qdrant_client.http import models\n",
        "\n",
        "filter_on_masterCategory = models.Filter(\n",
        "    must=[\n",
        "        models.FieldCondition(key=\"masterCategory\", match=models.MatchValue(value=payload_dict['masterCategory'])),\n",
        "        models.FieldCondition(key=\"baseColour\", match=models.MatchValue(value=payload_dict['baseColour'])),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "8tmOKv-eX7R-"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "1OsoKPU-o-54"
      },
      "outputs": [],
      "source": [
        "search_results = client.search(\n",
        "    collection_name=my_collection,\n",
        "    query_vector=image_embedding.tolist(),\n",
        "    #query_filter=filter_on_masterCategory,\n",
        "    #score_threshold=0.92,\n",
        "    limit=10\n",
        ")\n",
        "\n",
        "\n",
        "scored_points_list = []\n",
        "for scored_point in search_results:\n",
        "    scored_point_dict = {\n",
        "        \"id\": scored_point.id,\n",
        "        \"version\": scored_point.version,\n",
        "        \"score\": scored_point.score,\n",
        "        \"payload\": scored_point.payload,\n",
        "        \"vector\": scored_point.vector,\n",
        "    }\n",
        "    scored_points_list.append(scored_point_dict)\n",
        "\n",
        "\n",
        "id_list= []\n",
        "for i in scored_points_list:\n",
        "  id_list.append(i['payload'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract unique IDs from scored_points_list\n",
        "filtered_ids = set(point['image_id'] for point in id_list)\n",
        "# Filter the Hugging Face dataset based on the extracted IDs\n",
        "filtered_dataset = dataset.filter(lambda example: example['image_id'] in filtered_ids)\n",
        "filtered_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "56a1bf55bee44b6d9de71da9ff49a04a",
            "0624261a4b4342ab862473e648aff0fa",
            "493bc4eb859a43c09c56f8e6abc8a4df",
            "e7d9a8cc62d846eb8b81f0c24b8e956a",
            "e3d708425683455fa01d6bfa815007f3",
            "97d82b91d77346c6bf36196800931811",
            "756d75450a214e86859c54ba64ef9827",
            "e150c78baa0f451b8b998558553d6af4",
            "7b995a1ba34d4d34b7eb28d9f7e0c40d",
            "6f8726c825c14bb5a59abb8097a8570c",
            "6f6ac1771cba4fc692412182d64e6cec"
          ]
        },
        "id": "6H0eBs66QxZM",
        "outputId": "22e3391d-11b9-4525-a96e-cd8b326b70d4"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/20000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56a1bf55bee44b6d9de71da9ff49a04a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'gender', 'masterCategory', 'subCategory', 'articleType', 'baseColour', 'season', 'year', 'usage', 'productDisplayName', 'image', 'image_id', 'embeddings'],\n",
              "    num_rows: 10\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "num_rows = 2\n",
        "num_columns = 5\n",
        "total_images = num_rows * num_columns\n",
        "\n",
        "fig, axes = plt.subplots(num_rows, num_columns, figsize=(12, 10))\n",
        "\n",
        "for i, example in enumerate(filtered_dataset):\n",
        "    if i >= total_images:\n",
        "        break\n",
        "\n",
        "    row = i // num_columns\n",
        "    col = i % num_columns\n",
        "\n",
        "    image_bytes = example[\"image\"]\n",
        "\n",
        "\n",
        "    axes[row, col].imshow(image_bytes)\n",
        "    axes[row, col].axis('off')\n",
        "\n",
        "# Fill any remaining empty subplots with blank images\n",
        "for i in range(len(filtered_dataset), total_images):\n",
        "    row = i // num_columns\n",
        "    col = i % num_columns\n",
        "    blank_image = np.zeros((100, 100, 3), dtype=np.uint8)  # Creating a blank black image\n",
        "    axes[row, col].imshow(blank_image)\n",
        "    axes[row, col].axis('off')\n",
        "\n",
        "# Adjust spacing and layout\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "tTwvv-VBefG_",
        "outputId": "7705e70a-f202-44d8-f162-442d4e690246"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 10 Axes>"
            ],
            "image/png": 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