deeksonparlma
commited on
Commit
·
15e0607
1
Parent(s):
1643835
update on model
Browse files- .ipynb_checkpoints/model-checkpoint.ipynb +236 -16
- .~lock.mental_health_bot.xlsx# +1 -0
- excel-data.xls +0 -0
- mental_health_bot.csv +0 -0
- mental_health_bot.ods +0 -0
- mental_health_bot.xlsx +0 -0
- model.ipynb +196 -17
- requirements.txt +3 -1
.ipynb_checkpoints/model-checkpoint.ipynb
CHANGED
@@ -2,17 +2,90 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count":
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"id": "ace57031",
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"metadata": {},
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"outputs": [
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{
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"# Step 1: Collect and preprocess data\n",
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"questions
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"\n",
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"vectorizer = TfidfVectorizer()\n",
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"X = vectorizer.fit_transform(questions)\n",
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"# Step 4: Train the model\n",
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"model.fit(X_train, y_train)\n",
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"\n",
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"# Step 5: Evaluate the model\n",
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"y_pred = model.predict(X_test)\n",
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"accuracy = accuracy_score(y_test, y_pred)\n",
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"print(\"Accuracy:\", accuracy)\n",
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"\n",
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"# Step 6: Use the model to make predictions\n",
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"new_question_vector = vectorizer.transform([new_question])\n",
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"prediction = model.predict(new_question_vector)\n",
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"print(\"Prediction:\", prediction)
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]
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}
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],
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.7"
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}
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},
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"nbformat": 4,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "ace57031",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Question_ID</th>\n",
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" <th>Questions</th>\n",
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" <th>Answers</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1590140</td>\n",
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" <td>What does it mean to have a mental illness?</td>\n",
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" <td>Mental illnesses are health conditions that di...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2110618</td>\n",
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" <td>Who does mental illness affect?</td>\n",
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" <td>It is estimated that mental illness affects 1 ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>6361820</td>\n",
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" <td>What causes mental illness?</td>\n",
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" <td>It is estimated that mental illness affects 1 ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>9434130</td>\n",
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" <td>What are some of the warning signs of mental i...</td>\n",
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" <td>Symptoms of mental health disorders vary depen...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>7657263</td>\n",
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" <td>Can people with mental illness recover?</td>\n",
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" <td>When healing from mental illness, early identi...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Question_ID Questions \\\n",
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"0 1590140 What does it mean to have a mental illness? \n",
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"1 2110618 Who does mental illness affect? \n",
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"2 6361820 What causes mental illness? \n",
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"3 9434130 What are some of the warning signs of mental i... \n",
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"4 7657263 Can people with mental illness recover? \n",
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"\n",
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" Answers \n",
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"0 Mental illnesses are health conditions that di... \n",
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"1 It is estimated that mental illness affects 1 ... \n",
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"2 It is estimated that mental illness affects 1 ... \n",
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"3 Symptoms of mental health disorders vary depen... \n",
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"4 When healing from mental illness, early identi... "
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import accuracy_score\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import torch\n",
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"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
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"from huggingface_hub import notebook_login\n",
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"# notebook_login()\n",
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"# Step 1: Collect and preprocess data\n",
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"# Get all the questions from Questions column and responses from Questions column in the dataset data.csv\n",
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"# questions = data[\"Questions\"].tolist()\n",
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"# responses = data[\"Responses\"].tolist()\n",
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"questions = []\n",
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"responses = []\n",
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"q_id = []\n",
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"with open(\"mental_health_bot.csv\", \"r\") as f:\n",
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" for line in f:\n",
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" \n",
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" array = line.split(\",\") \n",
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" # questions.append(question)\n",
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" # responses.append(response)\n",
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" # q_id.append(question_id)\n",
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" try:\n",
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" question = array[1]\n",
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" response = array[2]\n",
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" question_id = array[0]\n",
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" questions.append(question)\n",
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" responses.append(response)\n",
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" q_id.append(question_id)\n",
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" except:\n",
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" pass\n",
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"\n",
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"data = pd.read_csv(\"data.csv\")\n",
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"data.head()\n",
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" \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "60e154b4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"missing values: Question_ID 0\n",
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"Questions 0\n",
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"Answers 0\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"print('missing values:', data.isnull().sum())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "41311468",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 149 entries, 0 to 148\n",
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"Data columns (total 3 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Question_ID 149 non-null object\n",
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" 1 Questions 149 non-null object\n",
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" 2 Answers 149 non-null object\n",
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"dtypes: object(3)\n",
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"memory usage: 3.6+ KB\n",
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"None\n"
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]
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}
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],
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"source": [
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"print(data.info())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "f6719ffa",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.03333333333333333\n"
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]
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}
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],
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"source": [
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"# print(questions)\n",
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"# print(responses)\n",
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"\n",
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"\n",
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"# questions = [\"What are some symptoms of depression?\",\n",
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"# \"How can I manage my anxiety?\",\n",
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"# \"What are the treatments for bipolar disorder?\"]\n",
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"# responses = [\"Symptoms of depression include sadness, lack of energy, and loss of interest in activities.\",\n",
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"# \"You can manage your anxiety through techniques such as deep breathing, meditation, and therapy.\",\n",
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"# \"Treatments for bipolar disorder include medication, therapy, and lifestyle changes.\"]\n",
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"\n",
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"vectorizer = TfidfVectorizer()\n",
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"X = vectorizer.fit_transform(questions)\n",
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"# Step 4: Train the model\n",
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"model.fit(X_train, y_train)\n",
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"\n",
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"# model.push_to_hub(\"tabibu-ai/mental-health-chatbot\")\n",
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"# pt_model = DistilBertForSequenceClassification.from_pretrained(\"model.ipynb\", from_tf=True)\n",
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"# pt_model.save_pretrained(\"model.ipynb\")\n",
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"# load model from hub\n",
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"\n",
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"# Step 5: Evaluate the model\n",
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"y_pred = model.predict(X_test)\n",
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"accuracy = accuracy_score(y_test, y_pred)\n",
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"print(\"Accuracy:\", accuracy)\n",
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"\n",
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"# Step 6: Use the model to make predictions\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "d8d18524",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ask me anythingWho are you\n"
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]
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}
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],
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"source": [
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"new_question = input(\"Ask me anything : \")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "e51d4ca5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Prediction: ['\"It is estimated that mental illness affects 1 in 5 adults in America']\n"
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]
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}
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],
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"source": [
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"new_question_vector = vectorizer.transform([new_question])\n",
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"prediction = model.predict(new_question_vector)\n",
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"print(\"Prediction:\", prediction)"
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]
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}
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],
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.7"
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},
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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.~lock.mental_health_bot.xlsx#
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,dickson,dickson,20.02.2023 22:13,file:///home/dickson/.config/libreoffice/4;
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excel-data.xls
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mental_health_bot.csv
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The diff for this file is too large to render.
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mental_health_bot.ods
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Binary file (85.9 kB). View file
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mental_health_bot.xlsx
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Binary file (55.6 kB). View file
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model.ipynb
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"cells": [
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{
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"cell_type": "code",
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"execution_count":
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"id": "ace57031",
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"metadata": {},
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"outputs": [
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{
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"source": [
|
@@ -20,7 +93,12 @@
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"# Step 1: Collect and preprocess data\n",
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"# Get all the questions from Questions column and responses from Questions column in the dataset data.csv\n",
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"# questions = data[\"Questions\"].tolist()\n",
|
@@ -28,7 +106,7 @@
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"questions = []\n",
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"responses = []\n",
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"q_id = []\n",
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-
"with open(\"
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" for line in f:\n",
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" \n",
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" array = line.split(\",\") \n",
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@@ -45,9 +123,74 @@
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" except:\n",
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" pass\n",
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"\n",
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"# print(questions)\n",
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"# print(responses)\n",
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"\n",
|
@@ -72,8 +215,9 @@
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"# Step 4: Train the model\n",
|
73 |
"model.fit(X_train, y_train)\n",
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"\n",
|
75 |
-
"model.push_to_hub(\"tabibu-ai/mental-health-chatbot\")\n",
|
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-
"\n",
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"# load model from hub\n",
|
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"\n",
|
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"# Step 5: Evaluate the model\n",
|
@@ -82,16 +226,51 @@
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"print(\"Accuracy:\", accuracy)\n",
|
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"\n",
|
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"# Step 6: Use the model to make predictions\n",
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-
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"new_question_vector = vectorizer.transform([new_question])\n",
|
87 |
"prediction = model.predict(new_question_vector)\n",
|
88 |
-
"print(\"Prediction:\", prediction)
|
89 |
]
|
90 |
}
|
91 |
],
|
92 |
"metadata": {
|
93 |
"kernelspec": {
|
94 |
-
"display_name": "Python 3",
|
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"language": "python",
|
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"name": "python3"
|
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},
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"cells": [
|
3 |
{
|
4 |
"cell_type": "code",
|
5 |
+
"execution_count": 8,
|
6 |
"id": "ace57031",
|
7 |
"metadata": {},
|
8 |
"outputs": [
|
9 |
{
|
10 |
+
"data": {
|
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+
"text/html": [
|
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"<div>\n",
|
13 |
+
"<style scoped>\n",
|
14 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
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+
" vertical-align: middle;\n",
|
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+
" }\n",
|
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+
"\n",
|
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+
" .dataframe tbody tr th {\n",
|
19 |
+
" vertical-align: top;\n",
|
20 |
+
" }\n",
|
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+
"\n",
|
22 |
+
" .dataframe thead th {\n",
|
23 |
+
" text-align: right;\n",
|
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+
" }\n",
|
25 |
+
"</style>\n",
|
26 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
27 |
+
" <thead>\n",
|
28 |
+
" <tr style=\"text-align: right;\">\n",
|
29 |
+
" <th></th>\n",
|
30 |
+
" <th>Question_ID</th>\n",
|
31 |
+
" <th>Questions</th>\n",
|
32 |
+
" <th>Answers</th>\n",
|
33 |
+
" </tr>\n",
|
34 |
+
" </thead>\n",
|
35 |
+
" <tbody>\n",
|
36 |
+
" <tr>\n",
|
37 |
+
" <th>0</th>\n",
|
38 |
+
" <td>1590140</td>\n",
|
39 |
+
" <td>What does it mean to have a mental illness?</td>\n",
|
40 |
+
" <td>Mental illnesses are health conditions that di...</td>\n",
|
41 |
+
" </tr>\n",
|
42 |
+
" <tr>\n",
|
43 |
+
" <th>1</th>\n",
|
44 |
+
" <td>2110618</td>\n",
|
45 |
+
" <td>Who does mental illness affect?</td>\n",
|
46 |
+
" <td>It is estimated that mental illness affects 1 ...</td>\n",
|
47 |
+
" </tr>\n",
|
48 |
+
" <tr>\n",
|
49 |
+
" <th>2</th>\n",
|
50 |
+
" <td>6361820</td>\n",
|
51 |
+
" <td>What causes mental illness?</td>\n",
|
52 |
+
" <td>It is estimated that mental illness affects 1 ...</td>\n",
|
53 |
+
" </tr>\n",
|
54 |
+
" <tr>\n",
|
55 |
+
" <th>3</th>\n",
|
56 |
+
" <td>9434130</td>\n",
|
57 |
+
" <td>What are some of the warning signs of mental i...</td>\n",
|
58 |
+
" <td>Symptoms of mental health disorders vary depen...</td>\n",
|
59 |
+
" </tr>\n",
|
60 |
+
" <tr>\n",
|
61 |
+
" <th>4</th>\n",
|
62 |
+
" <td>7657263</td>\n",
|
63 |
+
" <td>Can people with mental illness recover?</td>\n",
|
64 |
+
" <td>When healing from mental illness, early identi...</td>\n",
|
65 |
+
" </tr>\n",
|
66 |
+
" </tbody>\n",
|
67 |
+
"</table>\n",
|
68 |
+
"</div>"
|
69 |
+
],
|
70 |
+
"text/plain": [
|
71 |
+
" Question_ID Questions \\\n",
|
72 |
+
"0 1590140 What does it mean to have a mental illness? \n",
|
73 |
+
"1 2110618 Who does mental illness affect? \n",
|
74 |
+
"2 6361820 What causes mental illness? \n",
|
75 |
+
"3 9434130 What are some of the warning signs of mental i... \n",
|
76 |
+
"4 7657263 Can people with mental illness recover? \n",
|
77 |
+
"\n",
|
78 |
+
" Answers \n",
|
79 |
+
"0 Mental illnesses are health conditions that di... \n",
|
80 |
+
"1 It is estimated that mental illness affects 1 ... \n",
|
81 |
+
"2 It is estimated that mental illness affects 1 ... \n",
|
82 |
+
"3 Symptoms of mental health disorders vary depen... \n",
|
83 |
+
"4 When healing from mental illness, early identi... "
|
84 |
+
]
|
85 |
+
},
|
86 |
+
"execution_count": 8,
|
87 |
+
"metadata": {},
|
88 |
+
"output_type": "execute_result"
|
89 |
}
|
90 |
],
|
91 |
"source": [
|
|
|
93 |
"from sklearn.model_selection import train_test_split\n",
|
94 |
"from sklearn.linear_model import LogisticRegression\n",
|
95 |
"from sklearn.metrics import accuracy_score\n",
|
96 |
+
"import pandas as pd\n",
|
97 |
+
"import numpy as np\n",
|
98 |
+
"import torch\n",
|
99 |
+
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
|
100 |
+
"from huggingface_hub import notebook_login\n",
|
101 |
+
"# notebook_login()\n",
|
102 |
"# Step 1: Collect and preprocess data\n",
|
103 |
"# Get all the questions from Questions column and responses from Questions column in the dataset data.csv\n",
|
104 |
"# questions = data[\"Questions\"].tolist()\n",
|
|
|
106 |
"questions = []\n",
|
107 |
"responses = []\n",
|
108 |
"q_id = []\n",
|
109 |
+
"with open(\"mental_health_bot.csv\", \"r\") as f:\n",
|
110 |
" for line in f:\n",
|
111 |
" \n",
|
112 |
" array = line.split(\",\") \n",
|
|
|
123 |
" except:\n",
|
124 |
" pass\n",
|
125 |
"\n",
|
126 |
+
"data = pd.read_csv(\"data.csv\")\n",
|
127 |
+
"data.head()"
|
128 |
+
]
|
129 |
+
},
|
130 |
+
{
|
131 |
+
"cell_type": "code",
|
132 |
+
"execution_count": 9,
|
133 |
+
"id": "8f51e39d",
|
134 |
+
"metadata": {},
|
135 |
+
"outputs": [
|
136 |
+
{
|
137 |
+
"name": "stdout",
|
138 |
+
"output_type": "stream",
|
139 |
+
"text": [
|
140 |
+
"missing values: Question_ID 0\n",
|
141 |
+
"Questions 0\n",
|
142 |
+
"Answers 0\n",
|
143 |
+
"dtype: int64\n"
|
144 |
+
]
|
145 |
+
}
|
146 |
+
],
|
147 |
+
"source": [
|
148 |
+
"print('missing values:', data.isnull().sum())"
|
149 |
+
]
|
150 |
+
},
|
151 |
+
{
|
152 |
+
"cell_type": "code",
|
153 |
+
"execution_count": 10,
|
154 |
+
"id": "1d697a39",
|
155 |
+
"metadata": {},
|
156 |
+
"outputs": [
|
157 |
+
{
|
158 |
+
"name": "stdout",
|
159 |
+
"output_type": "stream",
|
160 |
+
"text": [
|
161 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
162 |
+
"RangeIndex: 149 entries, 0 to 148\n",
|
163 |
+
"Data columns (total 3 columns):\n",
|
164 |
+
" # Column Non-Null Count Dtype \n",
|
165 |
+
"--- ------ -------------- ----- \n",
|
166 |
+
" 0 Question_ID 149 non-null object\n",
|
167 |
+
" 1 Questions 149 non-null object\n",
|
168 |
+
" 2 Answers 149 non-null object\n",
|
169 |
+
"dtypes: object(3)\n",
|
170 |
+
"memory usage: 3.6+ KB\n",
|
171 |
+
"None\n"
|
172 |
+
]
|
173 |
+
}
|
174 |
+
],
|
175 |
+
"source": [
|
176 |
+
"print(data.info())"
|
177 |
+
]
|
178 |
+
},
|
179 |
+
{
|
180 |
+
"cell_type": "code",
|
181 |
+
"execution_count": 12,
|
182 |
+
"id": "c5dde0e4",
|
183 |
+
"metadata": {},
|
184 |
+
"outputs": [
|
185 |
+
{
|
186 |
+
"name": "stdout",
|
187 |
+
"output_type": "stream",
|
188 |
+
"text": [
|
189 |
+
"Accuracy: 0.03333333333333333\n"
|
190 |
+
]
|
191 |
+
}
|
192 |
+
],
|
193 |
+
"source": [
|
194 |
"# print(questions)\n",
|
195 |
"# print(responses)\n",
|
196 |
"\n",
|
|
|
215 |
"# Step 4: Train the model\n",
|
216 |
"model.fit(X_train, y_train)\n",
|
217 |
"\n",
|
218 |
+
"# model.push_to_hub(\"tabibu-ai/mental-health-chatbot\")\n",
|
219 |
+
"pt_model = DistilBertForSequenceClassification.from_pretrained(\"model.ipynb\", from_tf=True)\n",
|
220 |
+
"pt_model.save_pretrained(\"model.ipynb\")\n",
|
221 |
"# load model from hub\n",
|
222 |
"\n",
|
223 |
"# Step 5: Evaluate the model\n",
|
|
|
226 |
"print(\"Accuracy:\", accuracy)\n",
|
227 |
"\n",
|
228 |
"# Step 6: Use the model to make predictions\n",
|
229 |
+
"\n"
|
230 |
+
]
|
231 |
+
},
|
232 |
+
{
|
233 |
+
"cell_type": "code",
|
234 |
+
"execution_count": 18,
|
235 |
+
"id": "14406312",
|
236 |
+
"metadata": {},
|
237 |
+
"outputs": [
|
238 |
+
{
|
239 |
+
"name": "stdout",
|
240 |
+
"output_type": "stream",
|
241 |
+
"text": [
|
242 |
+
"Ask me anything : I feel sad\n"
|
243 |
+
]
|
244 |
+
}
|
245 |
+
],
|
246 |
+
"source": [
|
247 |
+
"new_question = input(\"Ask me anything : \")\n"
|
248 |
+
]
|
249 |
+
},
|
250 |
+
{
|
251 |
+
"cell_type": "code",
|
252 |
+
"execution_count": 17,
|
253 |
+
"id": "6b9198db",
|
254 |
+
"metadata": {},
|
255 |
+
"outputs": [
|
256 |
+
{
|
257 |
+
"name": "stdout",
|
258 |
+
"output_type": "stream",
|
259 |
+
"text": [
|
260 |
+
"Prediction: ['\"It is estimated that mental illness affects 1 in 5 adults in America']\n"
|
261 |
+
]
|
262 |
+
}
|
263 |
+
],
|
264 |
+
"source": [
|
265 |
"new_question_vector = vectorizer.transform([new_question])\n",
|
266 |
"prediction = model.predict(new_question_vector)\n",
|
267 |
+
"print(\"Prediction:\", prediction)"
|
268 |
]
|
269 |
}
|
270 |
],
|
271 |
"metadata": {
|
272 |
"kernelspec": {
|
273 |
+
"display_name": "Python 3 (ipykernel)",
|
274 |
"language": "python",
|
275 |
"name": "python3"
|
276 |
},
|
requirements.txt
CHANGED
@@ -1,2 +1,4 @@
|
|
1 |
torch
|
2 |
-
transformers
|
|
|
|
|
|
1 |
torch
|
2 |
+
transformers
|
3 |
+
huggingface_hub
|
4 |
+
tensorflow
|