Upload 44 files

data/pinecone.ipynb

@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "small-delaware",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
+      "\n",
+      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
+      "\n",
+      "mediapipe 0.8.1 requires opencv-python, which is not installed.\n",
+      "djitellopy 2.4.0 requires opencv-python, which is not installed.\n",
+      "umojahack2023 0.1 requires numpy==1.23.5, but you'll have numpy 1.24.4 which is incompatible.\n",
+      "tensorflow 2.11.0 requires gast<=0.4.0,>=0.2.1, but you'll have gast 0.5.3 which is incompatible.\n",
+      "streamlit 1.3.1 requires click<8.0,>=7.0, but you'll have click 8.1.3 which is incompatible.\n",
+      "pandas-profiling 3.2.0 requires joblib~=1.1.0, but you'll have joblib 1.2.0 which is incompatible.\n",
+      "pandas-profiling 3.2.0 requires markupsafe~=2.1.1, but you'll have markupsafe 2.0.1 which is incompatible.\n",
+      "mediapipe 0.8.1 requires numpy==1.19.3, but you'll have numpy 1.24.4 which is incompatible.\n",
+      "huggingface-hub 0.14.1 requires packaging>=20.9, but you'll have packaging 20.8 which is incompatible.\n",
+      "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you'll have protobuf 3.19.3 which is incompatible.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -qU pinecone-client \\\n",
+    "                 tqdm \\\n",
+    "                 httpimport \\\n",
+    "                 requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "legal-course",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/paul/miniconda3/lib/python3.8/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import requests\n",
+    "\n",
+    "import tqdm\n",
+    "import httpimport\n",
+    "import pinecone\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "\n",
+    "DATA_DIRECTORY = 'tmp'\n",
+    "INDEX_NAME = 'jumia-product-search'\n",
+    "INDEX_DIMENSION = 512\n",
+    "BATCH_SIZE=200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "political-robertson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PINECONE_API_KEY = \"22e8b95b-18a6-42b4-8824-dc65f08a60e1\"\n",
+    "PINECONE_ENV = \"us-west1-gcp-free\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "contained-financing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pinecone.init(api_key= PINECONE_API_KEY,\n",
+    "              environment=PINECONE_ENV)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "biblical-decision",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'dimension': 512,\n",
+       " 'index_fullness': 0.0,\n",
+       " 'namespaces': {},\n",
+       " 'total_vector_count': 0}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index_name = 'jumia-product-embeddings'\n",
+    "\n",
+    "if index_name not in pinecone.list_indexes():\n",
+    "    # now create the new index\n",
+    "    pinecone.create_index(\n",
+    "        index_name,\n",
+    "        dimension= 512,  # 768\n",
+    "        metric='cosine',\n",
+    "        pod_type='s1',\n",
+    "        pods=1\n",
+    "    )\n",
+    "\n",
+    "# connect to index\n",
+    "index = pinecone.Index(index_name)\n",
+    "# then check index status\n",
+    "index.describe_index_stats()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "advance-composite",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "\n",
+    "EMBED_FILE = \"../image_search_engine/artifacts/embeddings/embed_2023-07-09_15-17-45.pkl\"\n",
+    "\n",
+    "\n",
+    "def load_serialized_object(file_path):\n",
+    "    try:\n",
+    "        obj = joblib.load(file_path)\n",
+    "        return obj\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"File not found: {file_path}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error loading serialized object: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "lasting-wyoming",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = load_serialized_object(EMBED_FILE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "dramatic-superintendent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = [embed.tolist() for embed in embeddings]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "formal-million",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2941"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(embed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "equipped-conversion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "def load_json_file(file_path):\n",
+    "    with open(file_path, \"r\") as file:\n",
+    "        data = json.load(file)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "veterinary-greenhouse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "json_data = load_json_file(\"processed/jumia_3650/jumia_3650.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "important-literacy",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b01efeef77e42268c86219a3dab6a5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['JUMIA3650.0', array([ 0.01932903, -1.791061  ,  1.3284612 ], dtype=float32), {'filename': 'product_ear_pods_401_0.jpg', 'product_id': 'product_ear_pods_401', 'product_category': 'ear_pods', 'product_name': 'Power Pod Fingerprint Touch Bluetooth 5.0 Wireless Stereo Hear Phone Pod', 'product_image_url': 'https://ng.jumia.is/unsafe/fit-in/680x680/filters:fill(white)/product/66/4640212/1.jpg?9552', 'product_url': 'https://www.jumia.com.ng/power-pod-fingerprint-touch-bluetooth-5.0-wireless-stereo-hear-phone-pod-212046466.html', 'discounted_price': '₦ 6,500', 'original_price': '₦ 8,500'}]\n",
+      "['JUMIA3650.1', array([ 0.12243042, -1.9603508 ,  2.0345879 ], dtype=float32), {'filename': 'product_headset_671_3.jpg', 'product_id': 'product_headset_671', 'product_category': 'headset', 'product_name': 'Solar Charging Bluetooth Earphone Wireless TWS Headset', 'product_image_url': 'https://ng.jumia.is/unsafe/fit-in/680x680/filters:fill(white)/product/43/4238202/1.jpg?0528', 'product_url': 'https://www.jumia.com.ng/generic-solar-charging-bluetooth-earphone-wireless-tws-headset-202832434.html', 'discounted_price': '₦ 8,190', 'original_price': '₦ 13,000'}]\n",
+      "['JUMIA3650.2', array([-0.71607244, -1.5286068 ,  1.0747794 ], dtype=float32), {'filename': 'product_ear_pods_406_1.jpg', 'product_id': 'product_ear_pods_406', 'product_category': 'ear_pods', 'product_name': 'Wireless 5.0 Bluetooth Headset 8D Stereo Hear  Pod', 'product_image_url': 'https://ng.jumia.is/unsafe/fit-in/680x680/filters:fill(white)/product/82/4189211/1.jpg?5980', 'product_url': 'https://www.jumia.com.ng/generic-wireless-5.0-bluetooth-headset-8d-stereo-hear-pod-112981428.html', 'discounted_price': '₦ 7,900', 'original_price': '₦ 10,000'}]\n",
+      "['JUMIA3650.3', array([-1.2911813 , -0.97381216,  0.1008972 ], dtype=float32), {'filename': 'product_ear_pods_524_0.jpg', 'product_id': 'product_ear_pods_524', 'product_category': 'ear_pods', 'product_name': 'Foam cover for Apple AirPods Max', 'product_image_url': 'https://ng.jumia.is/unsafe/fit-in/680x680/filters:fill(white)/product/19/8334991/1.jpg?4544', 'product_url': 'https://www.jumia.com.ng/generic-foam-cover-for-apple-airpods-max-199433891.html', 'discounted_price': '₦ 12,960', 'original_price': '₦ 15,552'}]\n",
+      "['JUMIA3650.4', array([ 0.7770873, -1.4093312,  0.375482 ], dtype=float32), {'filename': 'product_headset_672_6.jpg', 'product_id': 'product_headset_672', 'product_category': 'headset', 'product_name': 'I12TWS Wireless Bluetooth Earphone Earbuds For IOS/Android', 'product_image_url': 'https://ng.jumia.is/unsafe/fit-in/680x680/filters:fill(white)/product/28/177079/1.jpg?2017', 'product_url': 'https://www.jumia.com.ng/generic-i12tws-wireless-bluetooth-earphone-earbuds-for-iosandroid-97077182.html', 'discounted_price': '₦ 3,300', 'original_price': '₦ 5,704'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "batch_size = 300\n",
+    "n_embed = len(embeddings)  # number of records to index from each language\n",
+    "\n",
+    "for i in tqdm(range(0, 5)):\n",
+    "    embed = embeddings[i]\n",
+    "    id_ = f\"JUMIA3650.{i}\"\n",
+    "    metadata = json_data[i]\n",
+    "    \n",
+    "    print([id_, embed[:3], metadata])\n",
+    "#     index.upsert(zip(ids, embeds, metadata))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "unnecessary-field",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a697e31ff7a14a49a8138cd2db6eafc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_size = 300\n",
+    "n_embed = len(embeddings)  # number of records to index from each language\n",
+    "\n",
+    "\n",
+    "\n",
+    "upsert = []\n",
+    "for i in tqdm(range(0, n_embed, batch_size)):\n",
+    "    ids = [f\"JUMIA_3650.{idx}\" for idx in range(i, i+batch_size)]\n",
+    "    batch_embeddings = embeddings[i:i+batch_size]\n",
+    "    batch_metadata = json_data[i:i+batch_size]\n",
+    "        \n",
+    "    index.upsert(zip(ids, batch_embeddings, batch_metadata))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "friendly-mount",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'dimension': 512,\n",
+       " 'index_fullness': 0.0,\n",
+       " 'namespaces': {'': {'vector_count': 2941}},\n",
+       " 'total_vector_count': 2941}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index.describe_index_stats()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "lesser-outside",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "300"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(embeddings[i:i+batch_size])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "alpine-production",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('JUMIA_3650.302',\n",
+       " {'filename': 'product_usb_hub_181_2.jpg',\n",
+       "  'product_id': 'product_usb_hub_181',\n",
+       "  'product_category': 'usb_hub',\n",
+       "  'product_name': '4 Ports USB Hub High Speed Adapter For DESKTOP LAPTOP Black Black',\n",
+       "  'product_image_url': 'https://ng.jumia.is/unsafe/fit-in/680x680/filters:fill(white)/product/42/215709/1.jpg?7924',\n",
+       "  'product_url': 'https://www.jumia.com.ng/generic-4-ports-usb-hub-high-speed-adapter-for-desktop-laptop-black-black-90751224.html',\n",
+       "  'discounted_price': '₦ 2,355',\n",
+       "  'original_price': '₦ 3,533'})"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "upsert[1][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "mobile-printer",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/processed/jumia_3650/test.csv

@@ -0,0 +1,710 @@
+filepath,filename,class
+index/product_usb_hub_36_2.jpg,product_usb_hub_36_2.jpg,usb_hub
+index/product_office_chairs_1324_1.jpg,product_office_chairs_1324_1.jpg,office_chairs
+index/product_mouse_229_5.jpg,product_mouse_229_5.jpg,mouse
+index/product_ear_pods_552_1.jpg,product_ear_pods_552_1.jpg,ear_pods
+index/product_usb_hub_16_5.jpg,product_usb_hub_16_5.jpg,usb_hub
+index/product_mouse_203_2.jpg,product_mouse_203_2.jpg,mouse
+index/product_backpack_828_1.jpg,product_backpack_828_1.jpg,backpack
+index/product_ear_pods_449_1.jpg,product_ear_pods_449_1.jpg,ear_pods
+index/product_backpack_854_6.jpg,product_backpack_854_6.jpg,backpack
+index/product_wrist_watch_1030_2.jpg,product_wrist_watch_1030_2.jpg,wrist_watch
+index/product_flash_drive_1411_5.jpg,product_flash_drive_1411_5.jpg,flash_drive
+index/product_backpack_865_3.jpg,product_backpack_865_3.jpg,backpack
+index/product_ear_pods_421_5.jpg,product_ear_pods_421_5.jpg,ear_pods
+index/product_mouse_263_0.jpg,product_mouse_263_0.jpg,mouse
+index/product_wrist_watch_1049_0.jpg,product_wrist_watch_1049_0.jpg,wrist_watch
+index/product_usb_hub_24_0.jpg,product_usb_hub_24_0.jpg,usb_hub
+index/product_wrist_watch_1189_2.jpg,product_wrist_watch_1189_2.jpg,wrist_watch
+index/product_backpack_885_4.jpg,product_backpack_885_4.jpg,backpack
+index/product_wrist_watch_1048_1.jpg,product_wrist_watch_1048_1.jpg,wrist_watch
+index/product_mouse_203_0.jpg,product_mouse_203_0.jpg,mouse
+index/product_backpack_839_2.jpg,product_backpack_839_2.jpg,backpack
+index/product_backpack_889_2.jpg,product_backpack_889_2.jpg,backpack
+index/product_mouse_203_7.jpg,product_mouse_203_7.jpg,mouse
+index/product_wrist_watch_1026_4.jpg,product_wrist_watch_1026_4.jpg,wrist_watch
+index/product_wrist_watch_1164_1.jpg,product_wrist_watch_1164_1.jpg,wrist_watch
+index/product_wrist_watch_1154_0.jpg,product_wrist_watch_1154_0.jpg,wrist_watch
+index/product_backpack_828_4.jpg,product_backpack_828_4.jpg,backpack
+index/product_wrist_watch_1124_1.jpg,product_wrist_watch_1124_1.jpg,wrist_watch
+index/product_headset_774_0.jpg,product_headset_774_0.jpg,headset
+index/product_mouse_246_0.jpg,product_mouse_246_0.jpg,mouse
+index/product_wrist_watch_1061_0.jpg,product_wrist_watch_1061_0.jpg,wrist_watch
+index/product_wrist_watch_1026_3.jpg,product_wrist_watch_1026_3.jpg,wrist_watch
+index/product_backpack_862_2.jpg,product_backpack_862_2.jpg,backpack
+index/product_usb_hub_37_0.jpg,product_usb_hub_37_0.jpg,usb_hub
+index/product_backpack_839_1.jpg,product_backpack_839_1.jpg,backpack
+index/product_headset_764_4.jpg,product_headset_764_4.jpg,headset
+index/product_mouse_387_1.jpg,product_mouse_387_1.jpg,mouse
+index/product_headset_662_1.jpg,product_headset_662_1.jpg,headset
+index/product_mouse_221_0.jpg,product_mouse_221_0.jpg,mouse
+index/product_flash_drive_1402_1.jpg,product_flash_drive_1402_1.jpg,flash_drive
+index/product_backpack_854_3.jpg,product_backpack_854_3.jpg,backpack
+index/product_ear_pods_593_4.jpg,product_ear_pods_593_4.jpg,ear_pods
+index/product_backpack_880_5.jpg,product_backpack_880_5.jpg,backpack
+index/product_ear_pods_445_4.jpg,product_ear_pods_445_4.jpg,ear_pods
+index/product_flash_drive_1402_4.jpg,product_flash_drive_1402_4.jpg,flash_drive
+index/product_wrist_watch_1095_0.jpg,product_wrist_watch_1095_0.jpg,wrist_watch
+index/product_headset_721_1.jpg,product_headset_721_1.jpg,headset
+index/product_usb_hub_68_2.jpg,product_usb_hub_68_2.jpg,usb_hub
+index/product_usb_hub_12_1.jpg,product_usb_hub_12_1.jpg,usb_hub
+index/product_headset_635_7.jpg,product_headset_635_7.jpg,headset
+index/product_backpack_828_0.jpg,product_backpack_828_0.jpg,backpack
+index/product_usb_hub_24_2.jpg,product_usb_hub_24_2.jpg,usb_hub
+index/product_flash_drive_1407_3.jpg,product_flash_drive_1407_3.jpg,flash_drive
+index/product_usb_hub_11_3.jpg,product_usb_hub_11_3.jpg,usb_hub
+index/product_office_chairs_1325_1.jpg,product_office_chairs_1325_1.jpg,office_chairs
+index/product_mouse_207_5.jpg,product_mouse_207_5.jpg,mouse
+index/product_headset_669_1.jpg,product_headset_669_1.jpg,headset
+index/product_backpack_802_4.jpg,product_backpack_802_4.jpg,backpack
+index/product_backpack_885_1.jpg,product_backpack_885_1.jpg,backpack
+index/product_mouse_374_1.jpg,product_mouse_374_1.jpg,mouse
+index/product_mouse_246_1.jpg,product_mouse_246_1.jpg,mouse
+index/product_wrist_watch_1006_2.jpg,product_wrist_watch_1006_2.jpg,wrist_watch
+index/product_flash_drive_1420_8.jpg,product_flash_drive_1420_8.jpg,flash_drive
+index/product_backpack_850_2.jpg,product_backpack_850_2.jpg,backpack
+index/product_wrist_watch_1030_3.jpg,product_wrist_watch_1030_3.jpg,wrist_watch
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image_search_engine/artifacts/config.json

@@ -0,0 +1 @@
+{"seed": 2022, "epochs": 10, "img_size": 224, "model_name": "tf_efficientnet_b0_ns", "num_classes": 8, "train_batch_size": 32, "valid_batch_size": 32, "learning_rate": 0.001, "scheduler": "OneCycleLR", "min_lr": 1e-05, "T_max": 500, "weight_decay": 1e-06, "n_fold": 100, "n_accumulate": 1, "device": "cuda", "test_mode": true, "enable_amp_half_precision": false, "s": 10.0, "m": 0.1, "ls_eps": 0.0, "easy_margin": false}

image_search_engine/artifacts/label_encoder/class_encoder_jumia_3650.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5400e30ac91878cf846e975acb82b7972bf64cccf84d9040e6ef404047fee879
+size 644

image_search_engine/artifacts/model_staged/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d05416e85b4ffa7eba35f9e453a1ad8153666a6917a56c9028089cf800fb2664
+size 6023748

image_search_engine/artifacts/model_staged/model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5369b9fc74deea65f75cb5d219f26a717bec873d590e0f4771b58ae02b73b0e
+size 18943957

image_search_engine/artifacts/weights/Loss0.6555_epoch3.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:12a5ae97a2fb28ea0d6845091646f90509c50013a7b441555aa3f58565621ae4
+size 18971557

image_search_engine/data/__init__.py

@@ -0,0 +1 @@
+from .jumia_3650_dataset import Jumia3650Dataset

image_search_engine/data/base_data_module.py

@@ -0,0 +1,39 @@
+import pandas as pd
+import torch
+from PIL import Image
+from sklearn.preprocessing import LabelEncoder
+from torch.utils.data import DataLoader, Dataset
+
+from image_search_engine.metadata import jumia_3650
+
+encoder = LabelEncoder()
+
+
+class JumiaImageDataset(Dataset):
+    def __init__(self, data_filename, transforms=None):
+        self.df = pd.read_csv(data_filename)
+        self.file_paths = self.df["filepath"].values
+        self.labels = encoder.fit_transform(self.df["class"])
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, index):
+        img_path = jumia_3650.PROCESSED_DATA_DIRNAME / self.file_paths[index]
+        img = Image.open(img_path).convert("RGB")
+        label = self.labels[index]
+
+        if self.transforms:
+            img = self.transforms(img)
+
+        return {"image": img, "label": torch.tensor(label, dtype=torch.long)}
+
+    def create_dataloader(self, batch_size, shuffle=True, num_workers=0):
+        return DataLoader(
+            self,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            num_workers=num_workers,
+            pin_memory=True,
+        )

image_search_engine/data/jumia_3650_dataset.py

@@ -0,0 +1,60 @@
+from pathlib import Path
+
+import joblib
+import pandas as pd
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+
+from image_search_engine.metadata import jumia_3650
+
+PACKAGE_DIR = Path(__file__).parent.parent
+
+# Load the pickled file
+with open(
+    PACKAGE_DIR / "artifacts/label_encoder/class_encoder_jumia_3650.pkl", "rb"
+) as file:
+    encoder = joblib.load(file)
+
+
+class Jumia3650Dataset(Dataset):
+    def __init__(self, data_filename, data_transforms=None, img_size=224):
+        self.df = pd.read_csv(data_filename)
+        self.file_paths = self.df["filepath"].values
+        self.labels = encoder.transform(self.df["class"])
+        self.classes = encoder.classes_
+        self.class_to_idx = {l: i for i, l in enumerate(encoder.classes_)}
+        if transforms is None:
+            self.data_transforms = transforms.Compose(
+                [
+                    transforms.ToTensor(),
+                    transforms.Resize((img_size, img_size)),
+                    transforms.Normalize(
+                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                    ),
+                ]
+            )
+        else:
+            self.data_transforms = data_transforms
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, index):
+        img_path = jumia_3650.PROCESSED_DATA_DIRNAME / self.file_paths[index]
+        img = Image.open(img_path).convert("RGB")
+        label = self.labels[index]
+
+        img = self.data_transforms(img)
+
+        return {"image": img, "label": torch.tensor(label, dtype=torch.long)}
+
+    def create_dataloader(self, batch_size, shuffle=True, num_workers=0):
+        return DataLoader(
+            self,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            num_workers=num_workers,
+            pin_memory=True,
+        )

image_search_engine/data/utils.py

@@ -0,0 +1,16 @@
+import json
+from pathlib import Path
+
+package_dir = Path(__file__).resolve().parents[1]
+
+
+def load_config(
+    file_path=package_dir / "artifacts/config.json",
+):
+    with open(file_path) as file:
+        data = json.load(file)
+    return data
+
+
+if __name__ == "__main__":
+    load_config()

image_search_engine/image_search_engine.egg-info/PKG-INFO

@@ -0,0 +1,5 @@
+Metadata-Version: 2.1
+Name: image-search-engine
+Version: 0.1.0
+Summary: A visual search engine for Jumia that lets users search for products by uploading an image. It uses computer vision to find similar or identical products within the store's inventory, saving users time and providing a more personalized shopping experience.
+Author: Paul Okewunmi

image_search_engine/image_search_engine.egg-info/SOURCES.txt

@@ -0,0 +1,20 @@
+setup.py
+artifacts/__init__.py
+data/__init__.py
+data/base_data_module.py
+data/jumia_3650_dataset.py
+data/utils.py
+evaluation/__init__.py
+image_search_engine.egg-info/PKG-INFO
+image_search_engine.egg-info/SOURCES.txt
+image_search_engine.egg-info/dependency_links.txt
+image_search_engine.egg-info/top_level.txt
+metadata/__init__.py
+metadata/jumia_3650.py
+metadata/shared.py
+models/__init__.py
+models/arc_margin_product.py
+models/base.py
+models/efficientnet_ns.py
+models/gem_pooling.py
+tests/__init__.py

image_search_engine/image_search_engine.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

image_search_engine/image_search_engine.egg-info/top_level.txt

@@ -0,0 +1,6 @@
+artifacts
+data
+evaluation
+metadata
+models
+tests

image_search_engine/metadata/__init__.py

@@ -0,0 +1 @@
+from . import jumia_3650, shared

image_search_engine/metadata/jumia_3650.py

@@ -0,0 +1,19 @@
+# from pathlib import Path
+from image_search_engine.metadata import shared
+
+RAW_DATA_DIRNAME = shared.DATA_DIRNAME / "raw" / "jumia_3650"
+METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
+DL_DATA_DIRNAME = shared.DATA_DIRNAME / "downloaded" / "jumia_3650"
+PROCESSED_DATA_DIRNAME = shared.DATA_DIRNAME / "processed" / "jumia_3650"
+
+
+CLASS_DICT = {
+    "backpack": 0,
+    "ear_pods": 1,
+    "flash_drive": 2,
+    "headset": 3,
+    "mouse": 4,
+    "office_chairs": 5,
+    "usb_hub": 6,
+    "wrist_watch": 7,
+}

image_search_engine/metadata/shared.py

@@ -0,0 +1,4 @@
+from pathlib import Path
+
+DATA_DIRNAME = Path(__file__).resolve().parents[2] / "data"
+DOWNLOADED_DATA_DIRNAME = DATA_DIRNAME / "downloaded"

image_search_engine/models/__init__.py

@@ -0,0 +1,3 @@
+from .arc_margin_product import ArcMarginProduct
+from .efficientnet_ns import EfficientNet_b0_ns
+from .gem_pooling import GeM

image_search_engine/models/arc_margin_product.py

@@ -0,0 +1,60 @@
+import math
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+ENABLE_HALF_PRECISION = False
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class ArcMarginProduct(nn.Module):
+    r"""Implement of large margin arc distance: :
+    Args:
+        in_features: size of each input sample
+        out_features: size of each output sample
+        s: norm of input feature
+        m: margin
+        cos(theta + m)
+    """
+
+    def __init__(
+        self, in_features, out_features, s=10, m=0.10, easy_margin=False, ls_eps=0.0
+    ):
+        super(ArcMarginProduct, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.s = s
+        self.m = m
+        self.ls_eps = ls_eps  # label smoothing
+        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = easy_margin
+        self.cos_m = math.cos(m)
+        self.sin_m = math.sin(m)
+        self.th = math.cos(math.pi - m)
+        self.mm = math.sin(math.pi - m) * m
+
+    def forward(self, input, label):
+        # --------------------------- cos(theta) & phi(theta) ---------------------
+        cosine = F.linear(F.normalize(input), F.normalize(self.weight))
+        if ENABLE_HALF_PRECISION == True:
+            cosine = cosine.to(torch.float32)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        # --------------------------- convert label to one-hot ---------------------
+        # one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')
+        one_hot = torch.zeros(cosine.size(), device=DEVICE)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        if self.ls_eps > 0:
+            one_hot = (1 - self.ls_eps) * one_hot + self.ls_eps / self.out_features
+        # -------------torch.where(out_i = {x_i if condition_i else y_i) ------------
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+
+        return output

image_search_engine/models/base.py

@@ -0,0 +1,200 @@
+import copy
+import gc
+import time
+import warnings
+from collections import defaultdict
+from pathlib import Path
+
+# Utils
+import numpy as np
+
+# Pytorch Imports
+import torch
+import torch.nn as nn
+from colorama import Back, Fore, Style
+from PIL import Image
+
+# Sklearn Imports
+from torch import nn
+from torchvision import transforms
+from tqdm import tqdm
+
+# import torch.nn as nn
+
+
+b_ = Fore.BLUE
+sr_ = Style.RESET_ALL
+
+warnings.filterwarnings("ignore")
+
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class BaseModel(nn.Module):
+    def __init__(self, predtrained=True) -> None:
+        super(BaseModel, self).__init__()
+
+    def forward(self):
+        pass
+
+    def _criterion(self, outputs, labels):
+        return nn.CrossEntropyLoss()(outputs, labels)
+
+    def _train_one_epoch(self, dataloader, optimizer, scheduler, device, epoch):
+        self.train()
+
+        dataset_size = 0
+        running_loss = 0.0
+
+        bar = tqdm(enumerate(dataloader), total=len(dataloader))
+        for step, data in bar:
+            images = data["image"].to(device, dtype=torch.float)
+            labels = data["label"].to(device, dtype=torch.long)
+
+            batch_size = images.size(0)
+
+            outputs, _ = self(images, labels)
+            loss = self._criterion(outputs, labels)
+
+            loss.backward()
+
+            optimizer.step()
+            optimizer.zero_grad()
+            if scheduler is not None:
+                scheduler.step()
+
+            running_loss += loss.item() * batch_size
+            dataset_size += batch_size
+
+            epoch_loss = running_loss / dataset_size
+
+            bar.set_postfix(
+                Epoch=epoch, Train_Loss=epoch_loss, LR=optimizer.param_groups[0]["lr"]
+            )
+        gc.collect()
+
+        return epoch_loss
+
+    @torch.inference_mode()
+    def _valid_one_epoch(self, dataloader, optimizer, device, epoch):
+        self.eval()
+
+        dataset_size = 0
+        running_loss = 0.0
+
+        bar = tqdm(enumerate(dataloader), total=len(dataloader))
+        for step, data in bar:
+            images = data["image"].to(device, dtype=torch.float)
+            labels = data["label"].to(device, dtype=torch.long)
+
+            batch_size = images.size(0)
+
+            outputs, _ = self(images, labels)
+            loss = self._criterion(outputs, labels)
+
+            running_loss += loss.item() * batch_size
+            dataset_size += batch_size
+
+            epoch_loss = running_loss / dataset_size
+
+            bar.set_postfix(
+                Epoch=epoch, Valid_Loss=epoch_loss, LR=optimizer.param_groups[0]["lr"]
+            )
+
+        gc.collect()
+
+        return epoch_loss
+
+    def run_training(
+        self,
+        train_loader,
+        valid_loader,
+        optimizer,
+        scheduler,
+        device,
+        num_epochs,
+        weights_dir,
+    ):
+        # To automatically log gradients
+
+        if torch.cuda.is_available():
+            print("[INFO] Using GPU: {}\n".format(torch.cuda.get_device_name()))
+
+        start = time.time()
+        best_model_wts = copy.deepcopy(self.state_dict())
+        best_epoch_loss = np.inf
+        history = defaultdict(list)
+
+        for epoch in range(1, num_epochs + 1):
+            gc.collect()
+
+            # dataloader, optimizer, scheduler, criterion, device, epoch
+            train_epoch_loss = self._train_one_epoch(
+                optimizer=optimizer,
+                scheduler=scheduler,
+                dataloader=train_loader,
+                device=device,
+                epoch=epoch,
+            )
+            val_epoch_loss = self._valid_one_epoch(
+                valid_loader, optimizer, device=device, epoch=epoch
+            )
+
+            history["Train Loss"].append(train_epoch_loss)
+            history["Valid Loss"].append(val_epoch_loss)
+
+            # deep copy the model
+            if val_epoch_loss <= best_epoch_loss:
+                print(
+                    f"{b_}Validation Loss Improved ({best_epoch_loss} ---> {val_epoch_loss})"
+                )
+                best_epoch_loss = val_epoch_loss
+                best_model_wts = copy.deepcopy(self.state_dict())
+                PATH = "Loss{:.4f}_epoch{:.0f}.bin".format(best_epoch_loss, epoch)
+                torch.save(self.state_dict(), weights_dir / PATH)
+                # Save a model file from the current directory
+                print(f"Model Saved{sr_}")
+
+            print()
+
+        end = time.time()
+        time_elapsed = end - start
+        print(
+            "Training complete in {:.0f}h {:.0f}m {:.0f}s".format(
+                time_elapsed // 3600,
+                (time_elapsed % 3600) // 60,
+                (time_elapsed % 3600) % 60,
+            )
+        )
+        print("Best Loss: {:.4f}".format(best_epoch_loss))
+
+        # load best model weights
+        self.load_state_dict(best_model_wts)
+
+        return history
+
+    def generate_embeddings(self, image, data_transforms=None):
+        if data_transforms is None:
+            data_transforms = transforms.Compose(
+                [
+                    transforms.ToTensor(),
+                    transforms.Resize((224, 224)),
+                    transforms.Normalize(
+                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                    ),
+                ]
+            )
+
+        self.eval()
+
+        image = image.convert("RGB")
+        image = data_transforms(image)
+        image = image.unsqueeze(0)  # Add batch dimension
+        image = image.to(DEVICE)
+
+        # Generate embedding
+        with torch.no_grad():
+            embedding = self(image)
+
+        return embedding.squeeze().cpu().numpy().tolist()

image_search_engine/models/efficientnet_ns.py

@@ -0,0 +1,48 @@
+import timm
+import torch
+import torch.nn as nn
+
+from image_search_engine.models.arc_margin_product import ArcMarginProduct
+from image_search_engine.models.base import BaseModel
+from image_search_engine.models.gem_pooling import GeM
+from image_search_engine.utils import PACKAGE_DIR
+
+CLASSES = 8
+SCALE = 10
+MARGIN = 0.1
+EMBEDING_SIZE = 512
+
+WEIGHTS_DIR = PACKAGE_DIR / "artifacts/weights"
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class EfficientNet_b0_ns(BaseModel):
+    def __init__(self, pretrained=True, load_weights=False):
+        super(EfficientNet_b0_ns, self).__init__(predtrained=pretrained)
+        self.model = timm.create_model("tf_efficientnet_b0_ns", pretrained=pretrained)
+        in_features = self.model.classifier.in_features
+        self.model.classifier = nn.Identity()
+        self.model.global_pool = nn.Identity()
+        self.pooling = GeM()
+        self.drop = nn.Dropout(p=0.2, inplace=False)
+        self.fc = nn.Linear(in_features, EMBEDING_SIZE)
+        self.arc = ArcMarginProduct(
+            EMBEDING_SIZE,
+            CLASSES,
+        )
+        if load_weights:
+            self.load_state_dict(
+                torch.load(WEIGHTS_DIR / "Loss0.6555_epoch3.bin", map_location=DEVICE)
+            )
+
+    def forward(self, images, labels=None):
+        features = self.model(images)
+        pooled_features = self.pooling(features).flatten(1)
+        pooled_drop = self.drop(pooled_features)
+        emb = self.fc(pooled_drop)
+
+        if labels is not None:
+            output = self.arc(emb, labels)
+            return output, emb
+        else:
+            return emb

image_search_engine/models/gem_pooling.py

@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class GeM(nn.Module):
+    def __init__(self, p=3, eps=1e-6):
+        super(GeM, self).__init__()
+        self.p = nn.Parameter(torch.ones(1) * p)
+        self.eps = eps
+
+    def forward(self, x):
+        return self.gem(x, p=self.p, eps=self.eps)
+
+    def gem(self, x, p=3, eps=1e-6):
+        return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
+            1.0 / p
+        )
+
+    def __repr__(self):
+        return (
+            self.__class__.__name__
+            + "("
+            + "p="
+            + "{:.4f}".format(self.p.data.tolist()[0])
+            + ", "
+            + "eps="
+            + str(self.eps)
+            + ")"
+        )

image_search_engine/product_image_search.py

@@ -0,0 +1,64 @@
+import numpy as np
+import torch
+import os
+
+from image_search_engine import utils
+from image_search_engine.models import EfficientNet_b0_ns
+from typing import Union
+from pathlib import Path
+from PIL import Image
+from dotenv import load_dotenv
+import pinecone
+
+MODEL_FILE = "model.pt"
+INDEX_FILE = "index.pkl"
+
+PROJECT_DIR = utils.PACKAGE_DIR.parent
+INDEX_NAME = "jumia-product-embeddings"
+
+PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
+PINECONE_ENV = os.environ.get("PINECONE_ENV")
+
+
+def load_pinecone_existing_index():
+    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
+    index = pinecone.Index(INDEX_NAME)
+    return index
+
+
+index = load_pinecone_existing_index()
+
+
+class JumiaProductSearch:
+    def __init__(self, model_path=None):
+        if model_path is None:
+            model_path = utils.STAGED_MODEL_DIR / MODEL_FILE
+        self.model = EfficientNet_b0_ns()
+        self.model.load_state_dict(torch.load(model_path))
+        self.index = utils.load_serialized_object(utils.STAGED_MODEL_DIR / INDEX_FILE)
+
+    def _encode(self, image: Union[str, Path, Image.Image]):
+        image_pil = image
+        if not isinstance(image, Image.Image):
+            image_pil = utils.read_image_pil(image)
+
+        query_embedding = self.model.generate_embeddings(image_pil)
+
+        return query_embedding
+
+    def search(self, image, k):
+        xq = self._encode(image)
+        result = index.query(xq, top_k=k, include_metadata=True)
+        return result
+
+    def search_nn(self, image):
+        query_embedding = self.encode(image)
+        distances, idxs = self.index.kneighbors(query_embedding, return_distance=True)
+        return idxs
+
+
+if __name__ == "__main__":
+    search = JumiaProductSearch()
+    test_img = utils.PACKAGE_DIR / "tests/test_img/1.jpg"
+    idx = search.search(test_img)
+    print(idx)

image_search_engine/utils.py

@@ -0,0 +1,48 @@
+import json
+from pathlib import Path
+import smart_open
+import joblib
+from typing import Union
+from PIL import Image
+
+PACKAGE_DIR = Path(__file__).parent
+STAGED_MODEL_DIR = PACKAGE_DIR / "artifacts/model_staged"
+
+
+def read_image_pil(image_uri: Union[Path, str]) -> Image:
+    with smart_open.open(image_uri, "rb") as image_file:
+        return read_image_pil_file(image_file)
+
+
+def read_image_pil_file(image_file) -> Image:
+    with Image.open(image_file) as image:
+        image = image.convert(mode=image.mode)
+    return image
+
+
+def load_serialized_object(file_path):
+    try:
+        obj = joblib.load(file_path)
+        return obj
+    except FileNotFoundError:
+        print(f"File not found: {file_path}")
+    except Exception as e:
+        print(f"Error loading serialized object: {str(e)}")
+
+
+def load_config(
+    file_path=PACKAGE_DIR / "artifacts/config.json",
+):
+    with open(file_path) as file:
+        data = json.load(file)
+    return data
+
+
+def load_json_file(file_path):
+    with open(file_path, "r") as file:
+        data = json.load(file)
+    return data
+
+
+if __name__ == "__main__":
+    load_config()