{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ace57031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question_ID</th>\n",
       "      <th>Questions</th>\n",
       "      <th>Answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1590140</td>\n",
       "      <td>What does it mean to have a mental illness?</td>\n",
       "      <td>Mental illnesses are health conditions that di...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2110618</td>\n",
       "      <td>Who does mental illness affect?</td>\n",
       "      <td>It is estimated that mental illness affects 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6361820</td>\n",
       "      <td>What causes mental illness?</td>\n",
       "      <td>It is estimated that mental illness affects 1 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9434130</td>\n",
       "      <td>What are some of the warning signs of mental i...</td>\n",
       "      <td>Symptoms of mental health disorders vary depen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7657263</td>\n",
       "      <td>Can people with mental illness recover?</td>\n",
       "      <td>When healing from mental illness, early identi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Question_ID                                          Questions  \\\n",
       "0     1590140        What does it mean to have a mental illness?   \n",
       "1     2110618                    Who does mental illness affect?   \n",
       "2     6361820                        What causes mental illness?   \n",
       "3     9434130  What are some of the warning signs of mental i...   \n",
       "4     7657263            Can people with mental illness recover?   \n",
       "\n",
       "                                             Answers  \n",
       "0  Mental illnesses are health conditions that di...  \n",
       "1  It is estimated that mental illness affects 1 ...  \n",
       "2  It is estimated that mental illness affects 1 ...  \n",
       "3  Symptoms of mental health disorders vary depen...  \n",
       "4  When healing from mental illness, early identi...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from huggingface_hub import notebook_login\n",
    "# notebook_login()\n",
    "# Step 1: Collect and preprocess data\n",
    "# Get all the questions from Questions column and responses from Questions column in the dataset data.csv\n",
    "# questions = data[\"Questions\"].tolist()\n",
    "# responses = data[\"Responses\"].tolist()\n",
    "questions = []\n",
    "responses = []\n",
    "q_id = []\n",
    "with open(\"mental_health_bot.csv\", \"r\") as f:\n",
    "    for line in f:\n",
    "        \n",
    "        array = line.split(\",\") \n",
    "        # questions.append(question)\n",
    "        # responses.append(response)\n",
    "        # q_id.append(question_id)\n",
    "        try:\n",
    "            question = array[1]\n",
    "            response = array[2]\n",
    "            question_id = array[0]\n",
    "            questions.append(question)\n",
    "            responses.append(response)\n",
    "            q_id.append(question_id)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "data = pd.read_csv(\"data.csv\")\n",
    "data.head()\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60e154b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing values: Question_ID    0\n",
      "Questions      0\n",
      "Answers        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('missing values:', data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41311468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149 entries, 0 to 148\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Question_ID  149 non-null    object\n",
      " 1   Questions    149 non-null    object\n",
      " 2   Answers      149 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6719ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.03333333333333333\n"
     ]
    }
   ],
   "source": [
    "# print(questions)\n",
    "# print(responses)\n",
    "\n",
    "\n",
    "# questions = [\"What are some symptoms of depression?\",\n",
    "#              \"How can I manage my anxiety?\",\n",
    "#              \"What are the treatments for bipolar disorder?\"]\n",
    "# responses = [\"Symptoms of depression include sadness, lack of energy, and loss of interest in activities.\",\n",
    "#              \"You can manage your anxiety through techniques such as deep breathing, meditation, and therapy.\",\n",
    "#              \"Treatments for bipolar disorder include medication, therapy, and lifestyle changes.\"]\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(questions)\n",
    "y = responses\n",
    "\n",
    "# Step 2: Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Step 3: Choose a machine learning algorithm\n",
    "model = LogisticRegression()\n",
    "\n",
    "# Step 4: Train the model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# model.push_to_hub(\"tabibu-ai/mental-health-chatbot\")\n",
    "# pt_model = DistilBertForSequenceClassification.from_pretrained(\"model.ipynb\", from_tf=True)\n",
    "# pt_model.save_pretrained(\"model.ipynb\")\n",
    "# load model from hub\n",
    "\n",
    "# Step 5: Evaluate the model\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Step 6: Use the model to make predictions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8d18524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ask me anythingWho are you\n"
     ]
    }
   ],
   "source": [
    "new_question = input(\"Ask me anything : \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e51d4ca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: ['\"It is estimated that mental illness affects 1 in 5 adults in America']\n"
     ]
    }
   ],
   "source": [
    "new_question_vector = vectorizer.transform([new_question])\n",
    "prediction = model.predict(new_question_vector)\n",
    "print(\"Prediction:\", prediction)"
   ]
  }
 ],
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