image_upload.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "\n",
    "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",
    "import torch\n",
    "from torchvision import transforms\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining constants\n",
    "LECTURE_SLIDES_DIR = os.path.join(os.getcwd(), \"lecture_slides\")\n",
    "\n",
    "# load API keys from globally-availabe .env file\n",
    "load_dotenv(dotenv_path='/mnt/project/chatbotai/huggingface_cache/internal_api_keys.env', override=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "folder count =  86\n",
      "1749\n",
      "1749\n"
     ]
    }
   ],
   "source": [
    "# loading image dataset\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "img_folder_path = \"/mnt/project/chatbotai/asmita/main_fn/lecture_slides\"\n",
    "folder_list = os.listdir(img_folder_path)\n",
    "print(\"folder count = \", len(folder_list))\n",
    "count_images = 0\n",
    "image_dict = {}\n",
    "\n",
    "for folder in folder_list:\n",
    "    file_list = os.listdir(img_folder_path+ \"/\" + folder)\n",
    "    image_dict[folder] = len(file_list)\n",
    "    count_images += len(file_list)\n",
    "\n",
    "print(count_images)\n",
    "print(sum(image_dict.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['uiuc-chatbot-images']\n"
     ]
    }
   ],
   "source": [
    "# initiate pinecone\n",
    "pinecone_index = pinecone.Index(\"uiuc-chatbot-images\")\n",
    "pinecone.init(api_key=os.environ['PINECONE_API_KEY_FOR_IMAGES'], environment=os.environ['PINECONE_ENVIRONMENT_FOR_IMAGES'])\n",
    "print(pinecone.list_indexes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pinecone.create_index(\"uiuc-chatbot-images\", dimension=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/avd6/.cache/torch/hub/pytorch_vision_v0.6.0\n",
      "/home/avd6/utils/miniconda3/envs/torch/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/avd6/utils/miniconda3/envs/torch/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "# Load pre-trained ResNet CNN\n",
    "model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True)\n",
    "model.eval()\n",
    "\n",
    "# Define image transformation\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize(256),\n",
    "    transforms.CenterCrop(224),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                         std=[0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "img_folder_path = \"/mnt/project/chatbotai/asmita/main_fn/lecture_slides\"\n",
    "folder_list = os.listdir(img_folder_path)\n",
    "images_to_upsert = []\n",
    "img_embedding = []\n",
    "\n",
    "for folder in folder_list:\n",
    "    image_list = os.listdir(img_folder_path+ \"/\" + folder)\n",
    "    \n",
    "    for i in image_list:\n",
    "        path = img_folder_path + \"/\" + folder + \"/\" + i\n",
    "\n",
    "        # create image id\n",
    "        image_name = i.split(\".\")[0]\n",
    "        img_id = folder + \"_\" + image_name\n",
    "\n",
    "        # create image metadata\n",
    "        img_metadata = {'lec': folder, 'image': image_name}\n",
    "\n",
    "        # load image and preprocess\n",
    "        img = Image.open(path)\n",
    "        img = transform(img)\n",
    "        img = img.unsqueeze(0)\n",
    "\n",
    "        # generate image embedding\n",
    "        with torch.no_grad():\n",
    "            features = model(img)\n",
    "        embedding = features.flatten().numpy().tolist()\n",
    "        \n",
    "        img_tuple = (img_id, embedding)\n",
    "        #print(img_tuple)\n",
    "        pinecone_index.upsert(vectors=[img_tuple])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IndexDescription(name='uiuc-chatbot-images', metric='cosine', replicas=1, dimension=1000.0, shards=1, pods=1, pod_type='p1.x1', status={'ready': True, 'state': 'Ready'}, metadata_config=None, source_collection='')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinecone.describe_index(\"uiuc-chatbot-images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query the database\n",
    "\n",
    "test_img_path = \"/mnt/project/chatbotai/asmita/main_fn/lecture_slides/002/Slide4.jpeg\"\n",
    "\n",
    "# load image and preprocess\n",
    "test_img = Image.open(test_img_path)\n",
    "test_img = transform(test_img)\n",
    "test_img = test_img.unsqueeze(0)\n",
    "\n",
    "# generate image embedding\n",
    "with torch.no_grad():\n",
    "    features = model(img)\n",
    "embedding = features.flatten().numpy().tolist()\n",
    "\n",
    "retrieved_embeddings = pinecone_index.query(vector=embedding, top_k=3, include_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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