diff --git a/Phase 2/task_7.ipynb b/Phase 2/task_7.ipynb
index f782863..3a72b77 100644
--- a/Phase 2/task_7.ipynb	
+++ b/Phase 2/task_7.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,14 +46,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "image_sim-cm_fd-kmeans-10-semantics.json loaded\n"
+      "label_sim-cm_fd-lda-10-model.joblib loaded\n"
      ]
     }
    ],
@@ -87,28 +87,45 @@
     "\n",
     "match selected_latent_space:\n",
     "    case \"\":\n",
-    "        if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
-    "            data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
-    "            print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
-    "        else:\n",
-    "          print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
+    "        if selected_dim_reduction_method == \"lda\":\n",
+    "            if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\") and os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\"):\n",
+    "                    model = load(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\")\n",
+    "                    data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib and json loaded\")\n",
+    "                else:\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib does not exist\")\n",
+    "            else:\n",
+    "                if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                    data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
+    "                else:\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
     "    case \"cp\":\n",
     "        if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n",
     "            data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n",
     "            print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n",
-    "        else:          \n",
+    "        else:\n",
     "          print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n",
     "    case _:\n",
-    "        if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
-    "            data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
-    "            print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
+    "        if selected_dim_reduction_method == \"lda\":\n",
+    "            if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\") and os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                model = load(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\")\n",
+    "                data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib loaded\")\n",
+    "            else:\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib does not exist\")\n",
     "        else:\n",
-    "          print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n"
+    "            if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
+    "            else:\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,6 +169,19 @@
     "          comparison_vector.append(math.dist(sim_matrix[int(image_id / 2)], centroid))\n",
     "        else:\n",
     "          comparison_vector.append(math.dist(image_fd, centroid))\n",
+    "    \n",
+    "    case \"lda\":\n",
+    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
+    "      if latent_space == \"image_sim\":\n",
+    "        comparison_vector = comparison_feature_space[int(image_id / 2)]\n",
+    "      else:\n",
+    "        fd = np.array(all_images[int(image_id / 2)][selected_feature_model])\n",
+    "        min_value = np.min(fd)\n",
+    "        feature_vectors_shifted = fd - min_value\n",
+    "        comparison_vector = model.transform(feature_vectors_shifted.flatten().reshape(1, -1)).flatten()\n",
+    "        print(comparison_feature_space.shape)\n",
+    "        print(comparison_vector.shape)\n",
+    "      # print(retValue)\n",
     "\n",
     "  n = len(comparison_feature_space)\n",
     "\n",
@@ -168,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,6 +254,10 @@
     "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
     "      comparison_vector = comparison_feature_space[img_label]\n",
     "\n",
+    "    case \"lda\":\n",
+    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
+    "      comparison_vector = comparison_feature_space[img_label]\n",
+    "\n",
     "  n = len(comparison_feature_space)\n",
     "  distance = float('inf')\n",
     "  most_similar_label = img_label\n",
@@ -244,23 +278,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'image_id': 2457, 'label': 39, 'distance': 5.400083378408386}\n",
-      "{'image_id': 2629, 'label': 46, 'distance': 6.360136822031199}\n",
-      "{'image_id': 1916, 'label': 23, 'distance': 8.279651870400942}\n",
-      "{'image_id': 1975, 'label': 24, 'distance': 9.305370097143731}\n",
-      "{'image_id': 3287, 'label': 65, 'distance': 9.696792665660324}\n",
-      "{'image_id': 292, 'label': 1, 'distance': 10.198675122162054}\n",
-      "{'image_id': 3965, 'label': 90, 'distance': 11.544874878013612}\n",
-      "{'image_id': 4018, 'label': 92, 'distance': 12.064116415014514}\n",
-      "{'image_id': 4037, 'label': 92, 'distance': 13.383239007317492}\n",
-      "{'image_id': 4307, 'label': 99, 'distance': 14.448284626506538}\n"
+      "Most similar label to 0 is 19\n",
+      "3634\n",
+      "3600\n",
+      "3562\n",
+      "3618\n",
+      "3574\n",
+      "3604\n",
+      "3550\n",
+      "3542\n",
+      "3564\n",
+      "3538\n"
      ]
     }
    ],
@@ -279,6 +314,13 @@
     "\n",
     "    extract_similarities_ls2(data, image_id)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {