Test the model with real life applications.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d95d0a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"preprocessed reddit dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8330385",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[[\"tokenized_text\" , \"pos_tags\"]]\n",
    "Y = df[[\"is_depression\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e884420f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cv = CountVectorizer()\n",
    "x_train, x_test, Y_train, Y_test = train_test_split(x, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "x_train_bow = cv.fit_transform(x_train['tokenized_text']).toarray()\n",
    "x_test_bow = cv.transform(x_test['tokenized_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0858e318",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Load the model from the pickle file\n",
    "with open(\"random_forest_model.pkl\", \"rb\") as file:\n",
    "    random_forest_model = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71a5ed10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter text: Hi Kartikey! How are you? I hope you are doing well. By the way, I am doing great and this Semester is off to a great start, really enjoying the peaceful and pleasant days that are continuing. I hope this year brings with it a lot of cool and fun stuff.\n",
      "\n",
      "\n",
      "\n",
      "not depressed\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from autocorrect import Speller\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "def predict_depression():\n",
    "    # Input text\n",
    "    text = input(\"Enter text: \")\n",
    "\n",
    "    # Remove tags and URLs from the text\n",
    "    cleaned_text = re.sub(r'<.*?>|https?://\\S+|www\\.\\S+', '', text)\n",
    "\n",
    "    # Make all uppercases lowercase\n",
    "    cleaned_text = cleaned_text.lower()\n",
    "\n",
    "    # Remove filler words and extra whitespace\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = word_tokenize(cleaned_text)\n",
    "    filtered_tokens = [token for token in tokens if token not in stop_words]\n",
    "    cleaned_text = ' '.join(filtered_tokens)\n",
    "\n",
    "    # Replace emojis (if any) with UTF code\n",
    "    # Code to replace emojis goes here...\n",
    "\n",
    "    # Remove all punctuations\n",
    "    cleaned_text = re.sub(r'[^\\w\\s]', '', cleaned_text)\n",
    "\n",
    "    # Remove any numbers\n",
    "    cleaned_text = re.sub(r'\\d+', '', cleaned_text)\n",
    "\n",
    "    # Passing the text through autocorrect library to correct misspelled words\n",
    "    spell = Speller(lang='en')\n",
    "    cleaned_text = spell(cleaned_text)\n",
    "\n",
    "    # Tokenize the text (each word is a token)\n",
    "    tokens = word_tokenize(cleaned_text)\n",
    "\n",
    "    # Stemming on the text\n",
    "    stemmer = PorterStemmer()\n",
    "    stemmed_tokens = [stemmer.stem(token) for token in tokens]\n",
    "\n",
    "    # Convert stemmed_tokens back to text as a single document\n",
    "    stemmed_text = ' '.join(stemmed_tokens)\n",
    "\n",
    "    # Transform the input text using the same CountVectorizer as used during training\n",
    "    mycv = cv.transform([stemmed_text]).toarray()\n",
    "\n",
    "    output = random_forest_model.predict(mycv)\n",
    "\n",
    "    print()\n",
    "    print()\n",
    "    print()\n",
    "\n",
    "    if output == 1:\n",
    "        print(\"depressed\")\n",
    "    else:\n",
    "        print(\"not depressed\")\n",
    "    \n",
    "predict_depression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c61ede",
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   "execution_count": null,
   "id": "e88fa017",
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   "cell_type": "code",
   "execution_count": null,
   "id": "c1e2acb1",
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