Merge 4e0e0783ad into 19476c845f
Paskal Sunari
1 week ago
committed by
GitHub
commit
055394cf2d
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Welcome to the world of Machine learning\n",
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"###### create the .venv environment form https://scikit-learn.org/stable/install.html\n",
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"###### use the .venv environment for the execution of the ipykernel\n",
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"###### first install scikit-learn, matplotlib, numpy, pandas, ipykernel in virtual environment"
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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": 7,
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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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"Shape: (2, 3, 4)\n",
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"3D Array: \n",
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" [[[ 1 2 3 4]\n",
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" [ 5 6 7 8]\n",
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" [ 9 10 11 12]]\n",
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"\n",
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" [[13 14 15 16]\n",
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" [17 18 19 20]\n",
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" [21 22 23 24]]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"a = np.array([\n",
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" [\n",
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" [1, 2, 3, 4],\n",
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" [5, 6, 7, 8],\n",
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" [9, 10, 11, 12]\n",
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" ],\n",
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" [\n",
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" [13, 14, 15, 16],\n",
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" [17, 18, 19, 20],\n",
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" [21, 22, 23, 24]\n",
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" ] \n",
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"])\n",
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"print(\"Shape:\", a.shape)\n",
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"print(\"3D Array: \\n\", a)"
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]
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}
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],
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"metadata": {
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||||
"kernelspec": {
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||||
"display_name": ".venv",
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"language": "python",
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||||
"name": "python3"
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||||
},
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||||
"language_info": {
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||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
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||||
"mimetype": "text/x-python",
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||||
"name": "python",
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||||
"nbconvert_exporter": "python",
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||||
"pygments_lexer": "ipython3",
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||||
"version": "3.13.3"
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||||
}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,447 @@
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{
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"cells": [
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{
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||||
"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"## Building the web Appliation"
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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": 28,
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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",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
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||||
"\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>datetime</th>\n",
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" <th>city</th>\n",
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||||
" <th>state</th>\n",
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" <th>country</th>\n",
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" <th>shape</th>\n",
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" <th>duration (seconds)</th>\n",
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" <th>duration (hours/min)</th>\n",
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" <th>comments</th>\n",
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" <th>date posted</th>\n",
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" <th>latitude</th>\n",
|
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" <th>longitude</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>10/10/1949 20:30</td>\n",
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" <td>san marcos</td>\n",
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" <td>tx</td>\n",
|
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" <td>us</td>\n",
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" <td>cylinder</td>\n",
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" <td>2700.0</td>\n",
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" <td>45 minutes</td>\n",
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" <td>This event took place in early fall around 194...</td>\n",
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" <td>4/27/2004</td>\n",
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" <td>29.883056</td>\n",
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" <td>-97.941111</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>10/10/1949 21:00</td>\n",
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" <td>lackland afb</td>\n",
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" <td>tx</td>\n",
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" <td>NaN</td>\n",
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" <td>light</td>\n",
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" <td>7200.0</td>\n",
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" <td>1-2 hrs</td>\n",
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" <td>1949 Lackland AFB&#44 TX. Lights racing acros...</td>\n",
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" <td>12/16/2005</td>\n",
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" <td>29.384210</td>\n",
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" <td>-98.581082</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>10/10/1955 17:00</td>\n",
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" <td>chester (uk/england)</td>\n",
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" <td>NaN</td>\n",
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" <td>gb</td>\n",
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" <td>circle</td>\n",
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" <td>20.0</td>\n",
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" <td>20 seconds</td>\n",
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" <td>Green/Orange circular disc over Chester&#44 En...</td>\n",
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" <td>1/21/2008</td>\n",
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" <td>53.200000</td>\n",
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" <td>-2.916667</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>10/10/1956 21:00</td>\n",
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" <td>edna</td>\n",
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" <td>tx</td>\n",
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" <td>us</td>\n",
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" <td>circle</td>\n",
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" <td>20.0</td>\n",
|
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" <td>1/2 hour</td>\n",
|
||||
" <td>My older brother and twin sister were leaving ...</td>\n",
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||||
" <td>1/17/2004</td>\n",
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||||
" <td>28.978333</td>\n",
|
||||
" <td>-96.645833</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>10/10/1960 20:00</td>\n",
|
||||
" <td>kaneohe</td>\n",
|
||||
" <td>hi</td>\n",
|
||||
" <td>us</td>\n",
|
||||
" <td>light</td>\n",
|
||||
" <td>900.0</td>\n",
|
||||
" <td>15 minutes</td>\n",
|
||||
" <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n",
|
||||
" <td>1/22/2004</td>\n",
|
||||
" <td>21.418056</td>\n",
|
||||
" <td>-157.803611</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
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||||
],
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"text/plain": [
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" datetime city state country shape \\\n",
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||||
"0 10/10/1949 20:30 san marcos tx us cylinder \n",
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"1 10/10/1949 21:00 lackland afb tx NaN light \n",
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||||
"2 10/10/1955 17:00 chester (uk/england) NaN gb circle \n",
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"3 10/10/1956 21:00 edna tx us circle \n",
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||||
"4 10/10/1960 20:00 kaneohe hi us light \n",
|
||||
"\n",
|
||||
" duration (seconds) duration (hours/min) \\\n",
|
||||
"0 2700.0 45 minutes \n",
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"1 7200.0 1-2 hrs \n",
|
||||
"2 20.0 20 seconds \n",
|
||||
"3 20.0 1/2 hour \n",
|
||||
"4 900.0 15 minutes \n",
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||||
"\n",
|
||||
" comments date posted latitude \\\n",
|
||||
"0 This event took place in early fall around 194... 4/27/2004 29.883056 \n",
|
||||
"1 1949 Lackland AFB, TX. Lights racing acros... 12/16/2005 29.384210 \n",
|
||||
"2 Green/Orange circular disc over Chester, En... 1/21/2008 53.200000 \n",
|
||||
"3 My older brother and twin sister were leaving ... 1/17/2004 28.978333 \n",
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||||
"4 AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.418056 \n",
|
||||
"\n",
|
||||
" longitude \n",
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||||
"0 -97.941111 \n",
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||||
"1 -98.581082 \n",
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"2 -2.916667 \n",
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||||
"3 -96.645833 \n",
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||||
"4 -157.803611 "
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||||
]
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||||
},
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||||
"execution_count": 28,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
|
||||
"import pandas as pd \n",
|
||||
"import numpy as np\n",
|
||||
"ufos = pd.read_csv('./data/ufos.csv')\n",
|
||||
"ufos.head()"
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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": 29,
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"metadata": {},
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"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": [
|
||||
"array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
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||||
]
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},
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||||
"execution_count": 29,
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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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||||
"ufos = pd.DataFrame({\n",
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||||
" 'Seconds': ufos['duration (seconds)'],\n",
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||||
" 'Country': ufos['country'],\n",
|
||||
" 'Latitude': ufos['latitude'],\n",
|
||||
" 'Longitude': ufos['longitude']\n",
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||||
"})\n",
|
||||
"ufos['Country'].unique()"
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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": 30,
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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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||||
"Index: 25863 entries, 2 to 80330\n",
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||||
"Data columns (total 4 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
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||||
"--- ------ -------------- ----- \n",
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||||
" 0 Seconds 25863 non-null float64\n",
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" 1 Country 25863 non-null object \n",
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" 2 Latitude 25863 non-null float64\n",
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" 3 Longitude 25863 non-null float64\n",
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"dtypes: float64(3), object(1)\n",
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"memory usage: 1010.3+ KB\n"
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]
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||||
}
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||||
],
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"source": [
|
||||
"ufos.dropna(inplace=True)\n",
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"ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
|
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"ufos.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": 31,
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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",
|
||||
"<style scoped>\n",
|
||||
" .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",
|
||||
"<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>Seconds</th>\n",
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" <th>Country</th>\n",
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" <th>Latitude</th>\n",
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" <th>Longitude</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>2</th>\n",
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" <td>20.0</td>\n",
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" <td>3</td>\n",
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" <td>53.200000</td>\n",
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" <td>-2.916667</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>20.0</td>\n",
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" <td>4</td>\n",
|
||||
" <td>28.978333</td>\n",
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||||
" <td>-96.645833</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
|
||||
" <td>30.0</td>\n",
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||||
" <td>4</td>\n",
|
||||
" <td>35.823889</td>\n",
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||||
" <td>-80.253611</td>\n",
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||||
" </tr>\n",
|
||||
" <tr>\n",
|
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" <th>23</th>\n",
|
||||
" <td>60.0</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>45.582778</td>\n",
|
||||
" <td>-122.352222</td>\n",
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||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>24</th>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>51.783333</td>\n",
|
||||
" <td>-0.783333</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Seconds Country Latitude Longitude\n",
|
||||
"2 20.0 3 53.200000 -2.916667\n",
|
||||
"3 20.0 4 28.978333 -96.645833\n",
|
||||
"14 30.0 4 35.823889 -80.253611\n",
|
||||
"23 60.0 4 45.582778 -122.352222\n",
|
||||
"24 3.0 3 51.783333 -0.783333"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.preprocessing import LabelEncoder\n",
|
||||
"ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
|
||||
"ufos.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"Selected_features = ['Seconds','Latitude','Longitude']\n",
|
||||
"x = ufos[Selected_features]\n",
|
||||
"y = ufos['Country']\n",
|
||||
"Xtrain, Xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Confusion matrix result\n",
|
||||
"##### [0,0] = 41: 41 samples of country 0 were correctly predicted as 0.\n",
|
||||
"##### [1,1] = 50: 50 samples of country 1 were correctly predicted as 1.\n",
|
||||
"##### [1,4] = 238: 238 samples of country 1 were incorrectly predicted as country 4.\n",
|
||||
"##### [4,4] = 4686: 4686 samples of country 4 were correctly predicted as 4.\n",
|
||||
"##### [4,1] = 14: 14 samples of country 4 were incorrectly predicted as 1."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
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||||
"metadata": {},
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"outputs": [
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||||
{
|
||||
"name": "stdout",
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"output_type": "stream",
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"text": [
|
||||
" precision recall f1-score support\n",
|
||||
"\n",
|
||||
" 0 1.00 1.00 1.00 41\n",
|
||||
" 1 0.78 0.17 0.28 288\n",
|
||||
" 2 1.00 0.90 0.95 10\n",
|
||||
" 3 0.99 1.00 1.00 134\n",
|
||||
" 4 0.95 1.00 0.97 4700\n",
|
||||
"\n",
|
||||
" accuracy 0.95 5173\n",
|
||||
" macro avg 0.95 0.81 0.84 5173\n",
|
||||
"weighted avg 0.94 0.95 0.94 5173\n",
|
||||
"\n",
|
||||
"Predicted labels: [4 4 4 ... 4 4 1]\n",
|
||||
"Accuracy [4 4 4 ... 4 4 1]\n",
|
||||
"Confusion matrix:\n",
|
||||
" [[ 41 0 0 0 0]\n",
|
||||
" [ 0 50 0 0 238]\n",
|
||||
" [ 0 0 9 1 0]\n",
|
||||
" [ 0 0 0 134 0]\n",
|
||||
" [ 0 14 0 0 4686]]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
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||||
"output_type": "stream",
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"text": [
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||||
"d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||||
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
|
||||
"\n",
|
||||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||||
"Please also refer to the documentation for alternative solver options:\n",
|
||||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||||
" n_iter_i = _check_optimize_result(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"model = LogisticRegression()\n",
|
||||
"model.fit(Xtrain, ytrain)\n",
|
||||
"prediction = model.predict(Xtest)\n",
|
||||
"print(classification_report(ytest, prediction))\n",
|
||||
"print('Predicted labels:', prediction)\n",
|
||||
"print('Accuracy', prediction)\n",
|
||||
"print('Confusion matrix:\\n', confusion_matrix(ytest, prediction))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Model loaded from disk [3]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pickle\n",
|
||||
"model_filename = 'ufos_model.pkl'\n",
|
||||
"pickle.dump(model, open(model_filename, 'wb'))\n",
|
||||
"model = pickle.load(open(model_filename, 'rb'))\n",
|
||||
"print('Model loaded from disk', model.predict([[50, 44, -12]]))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"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": "3.13.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
File diff suppressed because one or more lines are too long
@ -0,0 +1,43 @@
|
||||
// Test alert message to verify JavaScript is working
|
||||
alert('JavaScript is working! CuisineMatcher.js loaded successfully.');
|
||||
|
||||
const ingredients = Array(380).fill(0);
|
||||
|
||||
const checks = [...document.querySelectorAll('.checkbox')];
|
||||
|
||||
checks.forEach(check => {
|
||||
check.addEventListener('change', function() {
|
||||
ingredients[check.value] = check.checked ? 1 : 0;
|
||||
});
|
||||
});
|
||||
|
||||
function testCheckboxes() {
|
||||
return checks.some(check => check.checked);
|
||||
}
|
||||
|
||||
async function startInference() {
|
||||
|
||||
let atLeastOneChecked = testCheckboxes()
|
||||
|
||||
if (!atLeastOneChecked) {
|
||||
alert('Please select at least one ingredient.');
|
||||
return;
|
||||
}
|
||||
try {
|
||||
|
||||
const session = await ort.InferenceSession.create('./model.onnx');
|
||||
|
||||
const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
|
||||
const feeds = { float_input: input };
|
||||
|
||||
// feed inputs and run
|
||||
const results = await session.run(feeds);
|
||||
|
||||
// read from results
|
||||
alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
|
||||
|
||||
} catch (e) {
|
||||
console.log(`failed to inference ONNX model`);
|
||||
console.error(e);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,65 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>Cuisine Matcher</title>
|
||||
<link rel="stylesheet" href="styles.css">
|
||||
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.9.0/dist/ort.min.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<nav>
|
||||
<div class="logo">
|
||||
<a href="#">Cuisine Matcher</a>
|
||||
</div>
|
||||
<div class="nav-links">
|
||||
<a href="#">Home</a>
|
||||
<a href="#">Recipes</a>
|
||||
<a href="#">About</a>
|
||||
<a href="#">Contact</a>
|
||||
</div>
|
||||
</nav>
|
||||
|
||||
<h1 style="padding: 20px 20px 0 20px;">Check your refrigerator. What can you create?</h1>
|
||||
|
||||
<div id="wrapper">
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="4" class="checkbox">
|
||||
<label>apple</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="247" class="checkbox">
|
||||
<label>pear</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="77" class="checkbox">
|
||||
<label>cherry</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="126" class="checkbox">
|
||||
<label>fenugreek</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="302" class="checkbox">
|
||||
<label>sake</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="327" class="checkbox">
|
||||
<label>soy sauce</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="112" class="checkbox">
|
||||
<label>cumin</label>
|
||||
</div>
|
||||
</div>
|
||||
<div style="padding: 20px">
|
||||
<button onClick="startInference()">What kind of cuisine can you make?</button>
|
||||
</div>
|
||||
<script src="CouisineMatcher.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,40 @@
|
||||
/* Basic styling for navigation */
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
nav {
|
||||
background-color: #4CAF50;
|
||||
padding: 10px 20px;
|
||||
color: white;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
nav a {
|
||||
color: white;
|
||||
text-decoration: none;
|
||||
margin: 0 10px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
nav a:hover {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
#wrapper {
|
||||
padding: 20px;
|
||||
}
|
||||
|
||||
.boxCont {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
button {
|
||||
padding: 10px 20px;
|
||||
font-size: 16px;
|
||||
cursor: pointer;
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -1,26 +1,91 @@
|
||||
import random
|
||||
import string
|
||||
# New Update chatbot code with intent detection and state management
|
||||
INTENTS = {
|
||||
"greeting": ["hello", "hi", "hey"],
|
||||
"question": ["why", "what", "how", "is", "are", "?"],
|
||||
"opinion": ["i think", "i believe", "in my opinion"],
|
||||
"goodbye": ["bye", "exit", "quit"]
|
||||
}
|
||||
responses ={
|
||||
"greeting": [
|
||||
"Hello. How can I assist you today?"
|
||||
],
|
||||
"question": [
|
||||
"That is a thoughtful question. Could you clarify further?",
|
||||
"Let us explore that in more detail."
|
||||
],
|
||||
"opinion": [
|
||||
"That is an interesting perspective. What led you to that conclusion?"
|
||||
],
|
||||
"unknown": [
|
||||
"I am not entirely sure I understand. Could you rephrase?",
|
||||
"Could you provide more detail so I can respond appropriately?"
|
||||
]
|
||||
}
|
||||
conversation_state = {
|
||||
"last_intent": None,
|
||||
"last_topic": None
|
||||
}
|
||||
def normalize(text):
|
||||
text = text.lower()
|
||||
return text.translate(str.maketrans('', '', string.punctuation))
|
||||
def detect_intent(text):
|
||||
for intent, patterns in INTENTS.items():
|
||||
for pattern in patterns:
|
||||
if pattern in text:
|
||||
return intent
|
||||
return "unknown"
|
||||
|
||||
# This list contains the random responses (you can add your own or translate them into your own language too)
|
||||
random_responses = ["That is quite interesting, please tell me more.",
|
||||
"I see. Do go on.",
|
||||
"Why do you say that?",
|
||||
"Funny weather we've been having, isn't it?",
|
||||
"Let's change the subject.",
|
||||
"Did you catch the game last night?"]
|
||||
|
||||
print("Hello, I am Marvin, the simple robot.")
|
||||
print("You can end this conversation at any time by typing 'bye'")
|
||||
print("After typing each answer, press 'enter'")
|
||||
print("How are you today?")
|
||||
def extract_topic(text):
|
||||
keywords = ["coding", "programming", "money", "career", "finance"]
|
||||
for word in keywords:
|
||||
if word in text:
|
||||
return word
|
||||
return None
|
||||
|
||||
def generate_response(intent, topic):
|
||||
base_response = random.choice(responses.get(intent, responses["unknown"]))
|
||||
if topic:
|
||||
return f"{base_response} You mentioned {topic}, Would you like to discuss it further?"
|
||||
return base_response
|
||||
print("Hello, I am Paskal Chatbot, the conversational AI.")
|
||||
print("Type 'bye' to end the conversation.")
|
||||
while True:
|
||||
# wait for the user to enter some text
|
||||
user_input = input("> ")
|
||||
if user_input.lower() == "bye":
|
||||
# if they typed in 'bye' (or even BYE, ByE, byE etc.), break out of the loop
|
||||
normalized_input = normalize(user_input)
|
||||
if normalized_input == "bye":
|
||||
print("It was nice talking to you, goodbye!")
|
||||
break
|
||||
else:
|
||||
response = random.choices(random_responses)[0]
|
||||
intent = detect_intent(normalized_input)
|
||||
topic = extract_topic(normalized_input)
|
||||
conversation_state["last_intent"] = intent
|
||||
if topic:
|
||||
conversation_state["last_topic"] = topic
|
||||
response = generate_response(intent, topic)
|
||||
print(response)
|
||||
|
||||
print("It was nice talking to you, goodbye!")
|
||||
# This list contains the random responses (you can add your own or translate them into your own language too)
|
||||
# random_responses = ["That is quite interesting, please tell me more.",
|
||||
# "I see. Do go on.",
|
||||
# "Why do you say that?",
|
||||
# "Funny weather we've been having, isn't it?",
|
||||
# "Let's change the subject.",
|
||||
# "Did you catch the game last night?"]
|
||||
|
||||
# print("Hello, I am Marvin, the simple robot.")
|
||||
# print("You can end this conversation at any time by typing 'bye'")
|
||||
# print("After typing each answer, press 'enter'")
|
||||
# print("How are you today?")
|
||||
|
||||
# while True:
|
||||
# # wait for the user to enter some text
|
||||
# user_input = input("> ")
|
||||
# if user_input.lower() == "bye":
|
||||
# # if they typed in 'bye' (or even BYE, ByE, byE etc.), break out of the loop
|
||||
# break
|
||||
# else:
|
||||
# response = random.choices(random_responses)[0]
|
||||
# print(response)
|
||||
|
||||
# print("It was nice talking to you, goodbye!")
|
||||
|
||||
@ -0,0 +1,40 @@
|
||||
from textblob import TextBlob
|
||||
from textblob.np_extractors import ConllExtractor
|
||||
extracctor = ConllExtractor();
|
||||
|
||||
|
||||
def mani():
|
||||
print("Hello, I am Paskal, the friendly robot")
|
||||
print("You can end the conversation at any time typing 'bye'")
|
||||
print("After typing each sentence presthe Enter for the sentiment analysis")
|
||||
print("How are you feeling today?")
|
||||
|
||||
while True:
|
||||
user_input = input("You:")
|
||||
if user_input.lower() in ('bye', "exit", "quit"):
|
||||
break
|
||||
|
||||
user_blob = TextBlob(user_input, np_extractor=extracctor)
|
||||
polarity = user_blob.polarity
|
||||
noun_phrases = user_blob.noun_phrases
|
||||
|
||||
if polarity <= -0.5:
|
||||
response = "Oh Dear, that sounds really bad!"
|
||||
elif polarity <= 0:
|
||||
response = "Hmm, that's not great."
|
||||
elif polarity < 0.5:
|
||||
response = "Thats sound positive! "
|
||||
else:
|
||||
response = "Yay! That sounds awesome!"
|
||||
if noun_phrases:
|
||||
np = noun_phrases[0]
|
||||
try:
|
||||
plural_np = np.pluralize()
|
||||
except Exception:
|
||||
plural_np = np
|
||||
response += f"Can you tell me more about {plural_np}?"
|
||||
else:
|
||||
response += "Can you tell me more about that?"
|
||||
print(response)
|
||||
print("Goodbye! It was nice talking to you.")
|
||||
mani()
|
||||
Loading…
Reference in new issue