Merge remote-tracking branch 'origin/dev' into feature/retrain_for_ne…

…w_users

.github/workflows/main2.yml

@@ -43,7 +43,6 @@ jobs:
         working-directory: client
 
       - name: Run Flutter Tests
-        continue-on-error: true
         run: flutter test
         working-directory: client
 
@@ -81,7 +80,6 @@ jobs:
         run: ./gradlew ktlintCheck
 
       - name: Run Tests
-        continue-on-error: true
         run: ./gradlew test
 
       - name: Build Spring Boot project

.gitignore

@@ -99,9 +99,6 @@ env
 */amplify
 */node_modules
 !client/lib/
-<<<<<<< HEAD
-!gradle-wrapper.jar
-=======
+
 !gradle-wrapper.jar
-config.json
->>>>>>> d075a8edfcf0503bd2778e6b3d7b1d8fba6186f9
+config.json

AI/Notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e423f1c8-2d49-4d7b-95a7-c17fe21c5ad4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias']\n",
+      "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the weights of TFBertModel were initialized from the PyTorch model.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n",
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\vanzy\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\vanzy\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# preprocessing.py\n",
+    "\n",
+    "from transformers import BertTokenizer, TFBertModel\n",
+    "from joblib import load\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk.stem import PorterStemmer\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Initialize the BERT tokenizer and model\n",
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+    "model = TFBertModel.from_pretrained('bert-base-uncased')\n",
+    "\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "\n",
+    "def bert_feature_extraction(text):\n",
+    "    with tf.device('/GPU:0'):\n",
+    "        inputs = tokenizer.encode_plus(text, return_tensors='tf', add_special_tokens=True, max_length=50, truncation=True, padding='max_length')\n",
+    "        outputs = model(inputs)\n",
+    "        return outputs.last_hidden_state[:,0,:].numpy()\n",
+    "\n",
+    "def preprocess_text(text):\n",
+    "    if not isinstance(text, str):\n",
+    "        text = str(text)\n",
+    "\n",
+    "    tokens = word_tokenize(text)\n",
+    "    \n",
+    "    stop_words = set(stopwords.words('english'))\n",
+    "    tokens = [word for word in tokens if word.lower() not in stop_words]\n",
+    "    \n",
+    "    ps = PorterStemmer()\n",
+    "    tokens = [ps.stem(word) for word in tokens]\n",
+    "    \n",
+    "    return ' '.join(tokens)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4e94ef96-9295-493f-9bac-3f5228b9039d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = load('KOJA-CR.joblib')\n",
+    "def classify_sentence(sentence):\n",
+    "    # Preprocess the sentence\n",
+    "    preprocessed_sentence = preprocess_text(sentence)\n",
+    "    \n",
+    "    # Extract features using BERT\n",
+    "    features = bert_feature_extraction(preprocessed_sentence)\n",
+    "    \n",
+    "    # Reshape the features to match the input shape\n",
+    "    features = features.reshape(1, -1)\n",
+    "    \n",
+    "    # Make the prediction\n",
+    "    return classifier.predict(features)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "28fcdfdd-df37-41f6-8287-94de015db56c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nature\n",
+      "Kids\n",
+      "Nature\n",
+      "Nature\n",
+      "Arts\n",
+      "Education\n",
+      "Kids\n",
+      "Arts\n",
+      "Nature\n",
+      "Education\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = classify_sentence(\"Meeting with the marketing team\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Annual company picnic\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Project deadline\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Client presentation\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Team building workshop\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Quarterly business review\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Product launch event\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Board of directors meeting\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Employee training session\")\n",
+    "print(result)\n",
+    "\n",
+    "result = classify_sentence(\"Year-end party\")\n",
+    "print(result)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d315791c-8d0e-4335-a865-4846a0da05ee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}