Upload 17 files

app.py

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+from flask import Flask, render_template, request, jsonify
+# from flask_cors import CORS
+from source_data import get_response
+from flask_limiter import Limiter
+from flask_limiter.util import get_remote_address
+from cahtbott import send_message
+app = Flask(__name__)
+# CORS(app)
+limiter = Limiter(app)
+
+# Comment out @app.get and index_get() if you are using CORS(app) for standalone frontend and uncomment all other commented lines
+@app.get("/")
+@limiter.limit("3/second; 200/minute; 1200/hour")
+def index_get():
+    return render_template("base.html")
+
+@app.post("/predict")
+def predict():
+    text = request.get_json().get("message")
+    if len(text) > 100:
+        message = {"answer": "I'm sorry, your query has too many characters for me to process. If you would like to speak to a live agent, say 'I would like to speak to a live agent'"}
+        return jsonify(message)
+    response = get_response(text)
+    message = {"answer": response}
+    return jsonify(message)
+
+
+if __name__ == "__main__":
+    app.run(debug = True)
+    # from waitress import serve
+    # serve(app, host="0.0.0.0", port=8080)

cahtbott.py

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+import openai
+from openai import Completion
+import speech_recognition as sr
+import pyttsx3
+import webbrowser
+import time
+
+# Set up the OpenAI API client
+openai.api_key = "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"
+
+
+
+def send_message(message):
+    # Use the OpenAI API to get a response from ChatGPT
+    response = Completion.create(
+        engine="text-davinci-003",
+        prompt=message,
+        max_tokens=1024,
+        temperature=0.5,
+    )
+    return response.get('choices')[0].get('text')
+
+
+
+
+
+
+

data.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d5740e8791085fc5ebb320a0229be52802887ec12814a865f7e5f8307acd4339
+size 3671

intents.json

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+{"intents": [
+  {"tag": "options",
+    "patterns": ["show me options", "options", "what can you do", "what are my options"],
+    "responses": ["These are the options please choose one and tell me: 1-Coding, 2-Blog writing, 3-product marketing, 4-Website creating, 5-Digital marketing, 6-Social media content creating", "please choose one and tell me: 1-Coding, 2-Blog writing, 3-product marketing, 4-Website creating, 5-Digital marketing, 6-Social media content creating"]
+    
+  },
+  {"tag": "Coding",
+    "patterns": ["Coding", "1-coding", "Code","programming"],
+    "responses": ["You can ask for 1-telegram bot, 2-discord bot, 3-Deep neural network in pytorch, 4-Deep neural network in tensorflow, 5-Classification model in pytorch, 6-Classification model in tensorflow, 7-Regression model in pytorch, 8-Complete working regression flow, 9-Pytorch chatbot, 10-Chatterbot chatbot", "You can ask: 1-telegram bot, 2-discord bot, 3-Deep neural network in pytorch, 4-Deep neural network in tensorflow, 5-Classification model in pytorch, 6-Classification model in tensorflow, 7-Regression model in pytorch, 8-Complete working regression flow, 9-Pytorch chatbot, 10-Chatterbot chatbot"]
+  },
+  {"tag": "telegram bot",
+    "patterns": ["telegram bot", "telegram", "1-telegram bot"],
+    "responses": ["Can you help me create a Telegram bot with Python code?", "I need a Telegram bot and I was wondering if you could provide me with some code to get started?", "I'm looking to create a Telegram bot using code, can you assist me with that?","Do you have any tips or resources for creating a Telegram bot with programming code?","I want to build a Telegram bot using code, can you guide me on the process and provide me with some example code?"]
+  },
+  {"tag": "discord bot",
+    "patterns": ["discord bot", "2-discord bot", "discord"],
+    "responses": ["Could you help me create a Discord bot using code in Python/JavaScript?", "I'm interested in building a Discord bot, do you have any resources or example code to help me get started","Can you assist me in creating a Discord bot with code? I'm not sure where to begin","I would like to create a Discord bot using programming code, can you provide me with guidance and some sample code?"]
+  },
+  {"tag": "Blog writing",
+    "patterns": ["blog writing", "Blog","writing"],
+    "responses": ["You can ask for: 1-health blog, 2-gaming blog", "You can ask for chatgpt: 1-health blog, 2-gaming blog"]
+  },
+  {"tag": "health blog",
+    "patterns": ["health", "health blog"],
+    "responses": ["I'm interested in having a health blog created, would you be able to help me with that?", "Can you assist me in building a health blog that covers various topics related to fitness, nutrition, and mental health?","I need a health blog created and I would like it to be informative and engaging, can you help me with that?"]
+
+  },
+  {"tag": "gaming blog",
+    "patterns": ["gaming blog", "gaming"],
+    "responses": ["I would like to create a blog focused on gaming and esports, can you help me get started?", "I'm looking to start a gaming blog and I need someone to create it for me, can you provide me with guidance and create a blog that meets my needs?","Can you assist me in building a gaming blog that covers various topics related to gaming news, reviews, and strategies?"]
+  }
+ 
+]
+}

source_data.py

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+import random
+import json
+
+import torch
+
+from model import NeuralNet
+from nltk_utils import bag_of_words, tokenize
+
+# Prompt three most commonly asked FAQs
+samples = ""
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+with open('intents.json', 'r') as json_data:
+    intents = json.load(json_data)
+
+FILE = "data.pth"
+data = torch.load(FILE)
+
+input_size = data["input_size"]
+hidden_size = data["hidden_size"]
+output_size = data["output_size"]
+all_words = data['all_words']
+tags = data['tags']
+model_state = data["model_state"]
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+model.load_state_dict(model_state)
+model.eval()
+
+bot_name = "chatgpt prompt bot"
+
+
+def get_response(msg):
+    sentence = tokenize(msg)
+    X = bag_of_words(sentence, all_words)
+    X = X.reshape(1, X.shape[0])
+    X = torch.from_numpy(X).to(device)
+
+    output = model(X)
+    _, predicted = torch.max(output, dim=1)
+
+    tag = tags[predicted.item()]
+
+    probs = torch.softmax(output, dim=1)
+    prob = probs[0][predicted.item()]
+    if prob.item() > 0.85:  # Increasing specifisity to reduce incorrect classifications
+        for intent in intents['intents']:
+            if tag == intent["tag"]:
+                return random.choice(intent['responses'])
+
+    return f"I'm sorry, but I cannot understand your query. {samples} "
+
+
+if __name__ == "__main__":
+    print("Please enter options (type 'quit' to exit)")
+    while True:
+        # sentence = "do you use credit cards?"
+        sentence = input("You: ")
+        if sentence == "quit":
+            break
+
+        resp = get_response(sentence)
+        print(resp)
+

standalone-frontend/app.js

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+class Chatbox {
+    constructor() {
+        this.args = {
+            openButton: document.querySelector('.chatbox__button'),
+            chatBox: document.querySelector('.chatbox__support'),
+            sendButton: document.querySelector('.send__button')
+        }
+
+        this.state = false;
+        this.messages = [];
+    }
+
+    display() {
+        const {openButton, chatBox, sendButton} = this.args;
+
+        openButton.addEventListener('click', () => this.toggleState(chatBox))
+
+        sendButton.addEventListener('click', () => this.onSendButton(chatBox))
+
+        const node = chatBox.querySelector('input');
+        node.addEventListener("keyup", ({key}) => {
+            if (key === "Enter") {
+                this.onSendButton(chatBox)
+            }
+        })
+    }
+
+    toggleState(chatbox) {
+        this.state = !this.state;
+
+        // show or hides the box
+        if(this.state) {
+            chatbox.classList.add('chatbox--active')
+        } else {
+            chatbox.classList.remove('chatbox--active')
+        }
+    }
+
+    onSendButton(chatbox) {
+        var textField = chatbox.querySelector('input');
+        let text1 = textField.value
+        if (text1 === "") {
+            return;
+        }
+
+        let msg1 = { name: "User", message: text1 }
+        this.messages.push(msg1);
+
+        fetch('http://127.0.0.1:5000/predict', {
+            method: 'POST',
+            body: JSON.stringify({ message: text1 }),
+            mode: 'cors',
+            headers: {
+              'Content-Type': 'application/json'
+            },
+          })
+          .then(r => r.json())
+          .then(r => {
+            let msg2 = { name: "Sam", message: r.answer };
+            this.messages.push(msg2);
+            this.updateChatText(chatbox)
+            textField.value = ''
+
+        }).catch((error) => {
+            console.error('Error:', error);
+            this.updateChatText(chatbox)
+            textField.value = ''
+          });
+    }
+
+    updateChatText(chatbox) {
+        var html = '';
+        this.messages.slice().reverse().forEach(function(item, index) {
+            if (item.name === "Sam")
+            {
+                html += '<div class="messages__item messages__item--visitor">' + item.message + '</div>'
+            }
+            else
+            {
+                html += '<div class="messages__item messages__item--operator">' + item.message + '</div>'
+            }
+          });
+
+        const chatmessage = chatbox.querySelector('.chatbox__messages');
+        chatmessage.innerHTML = html;
+    }
+}
+
+
+const chatbox = new Chatbox();
+chatbox.display();

standalone-frontend/base.html

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+<!DOCTYPE html>
+<html lang="en">
+<link rel="stylesheet" href="style.css">
+
+<head>
+    <meta charset="UTF-8">
+    <title>Chatbot</title>
+</head>
+<body>
+<div class="container">
+    <div class="chatbox">
+        <div class="chatbox__support">
+            <div class="chatbox__header">
+                <div class="chatbox__image--header">
+                    <img src="https://img.icons8.com/color/48/000000/circled-user-female-skin-type-5--v1.png" alt="image">
+                </div>
+                <div class="chatbox__content--header">
+                    <h4 class="chatbox__heading--header">Chat support</h4>
+                    <p class="chatbox__description--header">Hi. My name is Sam. How can I help you?</p>
+                </div>
+            </div>
+            <div class="chatbox__messages">
+                <div></div>
+            </div>
+            <div class="chatbox__footer">
+                <input type="text" placeholder="Write a message...">
+                <button class="chatbox__send--footer send__button">Send</button>
+            </div>
+        </div>
+        <div class="chatbox__button">
+            <button><img src="./images/chatbox-icon.svg" /></button>
+        </div>
+    </div>
+</div>
+
+    <script src="./app.js"></script>
+
+</body>
+</html>

standalone-frontend/style.css

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+* {
+    box-sizing: border-box;
+    margin: 0;
+    padding: 0;
+}
+
+body {
+    font-family: 'Nunito', sans-serif;
+    font-weight: 400;
+    font-size: 100%;
+    background: #F1F1F1;
+}
+
+*, html {
+    --primaryGradient: linear-gradient(93.12deg, #581B98 0.52%, #9C1DE7 100%);
+    --secondaryGradient: linear-gradient(268.91deg, #581B98 -2.14%, #9C1DE7 99.69%);
+    --primaryBoxShadow: 0px 10px 15px rgba(0, 0, 0, 0.1);
+    --secondaryBoxShadow: 0px -10px 15px rgba(0, 0, 0, 0.1);
+    --primary: #581B98;
+}
+
+/* CHATBOX
+=============== */
+.chatbox {
+    position: absolute;
+    bottom: 30px;
+    right: 30px;
+}
+
+/* CONTENT IS CLOSE */
+.chatbox__support {
+    display: flex;
+    flex-direction: column;
+    background: #eee;
+    width: 300px;
+    height: 350px;
+    z-index: -123456;
+    opacity: 0;
+    transition: all .5s ease-in-out;
+}
+
+/* CONTENT ISOPEN */
+.chatbox--active {
+    transform: translateY(-40px);
+    z-index: 123456;
+    opacity: 1;
+
+}
+
+/* BUTTON */
+.chatbox__button {
+    text-align: right;
+}
+
+.send__button {
+    padding: 6px;
+    background: transparent;
+    border: none;
+    outline: none;
+    cursor: pointer;
+}
+
+
+/* HEADER */
+.chatbox__header {
+    position: sticky;
+    top: 0;
+    background: orange;
+}
+
+/* MESSAGES */
+.chatbox__messages {
+    margin-top: auto;
+    display: flex;
+    overflow-y: scroll;
+    flex-direction: column-reverse;
+}
+
+.messages__item {
+    background: orange;
+    max-width: 60.6%;
+    width: fit-content;
+}
+
+.messages__item--operator {
+    margin-left: auto;
+}
+
+.messages__item--visitor {
+    margin-right: auto;
+}
+
+/* FOOTER */
+.chatbox__footer {
+    position: sticky;
+    bottom: 0;
+}
+
+.chatbox__support {
+    background: #f9f9f9;
+    height: 450px;
+    width: 350px;
+    box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.1);
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+}
+
+/* HEADER */
+.chatbox__header {
+    background: var(--primaryGradient);
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    justify-content: center;
+    padding: 15px 20px;
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+    box-shadow: var(--primaryBoxShadow);
+}
+
+.chatbox__image--header {
+    margin-right: 10px;
+}
+
+.chatbox__heading--header {
+    font-size: 1.2rem;
+    color: white;
+}
+
+.chatbox__description--header {
+    font-size: .9rem;
+    color: white;
+}
+
+/* Messages */
+.chatbox__messages {
+    padding: 0 20px;
+}
+
+.messages__item {
+    margin-top: 10px;
+    background: #E0E0E0;
+    padding: 8px 12px;
+    max-width: 70%;
+}
+
+.messages__item--visitor,
+.messages__item--typing {
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+    border-bottom-right-radius: 20px;
+}
+
+.messages__item--operator {
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+    border-bottom-left-radius: 20px;
+    background: var(--primary);
+    color: white;
+}
+
+/* FOOTER */
+.chatbox__footer {
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    justify-content: space-between;
+    padding: 20px 20px;
+    background: var(--secondaryGradient);
+    box-shadow: var(--secondaryBoxShadow);
+    border-bottom-right-radius: 10px;
+    border-bottom-left-radius: 10px;
+    margin-top: 20px;
+}
+
+.chatbox__footer input {
+    width: 80%;
+    border: none;
+    padding: 10px 10px;
+    border-radius: 30px;
+    text-align: left;
+}
+
+.chatbox__send--footer {
+    color: white;
+}
+
+.chatbox__button button,
+.chatbox__button button:focus,
+.chatbox__button button:visited {
+    padding: 10px;
+    background: white;
+    border: none;
+    outline: none;
+    border-top-left-radius: 50px;
+    border-top-right-radius: 50px;
+    border-bottom-left-radius: 50px;
+    box-shadow: 0px 10px 15px rgba(0, 0, 0, 0.1);
+    cursor: pointer;
+}

static/app.js

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+class Chatbox {
+    constructor() {
+        this.args = {
+            openButton: document.querySelector('.chatbox__button'),
+            chatBox: document.querySelector('.chatbox__support'),
+            sendButton: document.querySelector('.send__button')
+        }
+
+        this.state = false;
+        this.messages = [];
+    }
+
+    display() {
+        const {openButton, chatBox, sendButton} = this.args;
+
+        this.prompt(chatBox)
+
+        openButton.addEventListener('click', () => this.toggleState(chatBox))
+
+        sendButton.addEventListener('click', () => this.onSendButton(chatBox))
+
+        const node = chatBox.querySelector('input');
+        node.addEventListener("keyup", ({key}) => {
+            if (key === "Enter") {
+                this.onSendButton(chatBox)
+            }
+        })
+    }
+
+    prompt(chatbox) {
+        this.messages.push({ name: "Bot", message: "Hi! I'm Chatgpt prompt assistant. Please type 'options' " });
+        this.updateChatText(chatbox)
+    }
+
+    toggleState(chatbox) {
+        this.state = !this.state;
+        // show or hides the box
+        if(this.state) {
+            chatbox.classList.add('chatbox--active')
+        } else {
+            chatbox.classList.remove('chatbox--active')
+        }
+    }
+
+    onSendButton(chatbox) {
+        var textField = chatbox.querySelector('input');
+        let text1 = textField.value
+        if (text1 === "") {
+            return;
+        }
+
+        let msg1 = { name: "User", message: text1 }
+        this.messages.push(msg1);
+
+        fetch($SCRIPT_ROOT + '/predict', {
+            method: 'POST',
+            body: JSON.stringify({ message: text1 }),
+            mode: 'cors',
+            headers: {
+              'Content-Type': 'application/json'
+            },
+          })
+          .then(r => r.json())
+          .then(r => {
+            let msg2 = { name: "Bot", message: r.answer };
+            this.messages.push(msg2);
+            this.updateChatText(chatbox)
+            textField.value = ''
+
+        }).catch((error) => {
+            console.error('Error:', error);
+            this.updateChatText(chatbox)
+            textField.value = ''
+          });
+    }
+
+    updateChatText(chatbox) {
+        var html = '';
+        this.messages.slice().reverse().forEach(function(item, index) {
+            if (item.name === "Bot")
+            {
+                html += '<div class="messages__item messages__item--visitor">' + item.message + '</div>'
+            }
+            else
+            {
+                html += '<div class="messages__item messages__item--operator">' + item.message + '</div>'
+            }
+          });
+
+        const chatmessage = chatbox.querySelector('.chatbox__messages');
+        chatmessage.innerHTML = html;
+    }
+}
+
+
+const chatbox = new Chatbox();
+chatbox.display();
+
+// let promise = new Promise(chatbox.display(resolve, reject) {
+//     // the function is executed automatically when the promise is constructed
+
+//     // after 1 second signal that the job is done with the result "done"
+//     setTimeout(() => resolve("Done"), 1000);
+//   });

static/base.html

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+<!DOCTYPE html>
+<html lang="en">
+<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+
+<head>
+    <meta charset="UTF-8">
+    <title>Chatbot</title>
+</head>
+<body>
+
+<h2 align="center"></h2>
+<div class="container">
+    <div class="chatbox">
+        <div class="chatbox__support">
+            <div class="chatbox__header">
+                <div class="chatbox__image--header">
+                    <img src="https://img.icons8.com/color/48/000000/circled-user-female-skin-type-5--v1.png" alt="image">
+                </div>
+                <div class="chatbox__content--header">
+                    <h4 class="chatbox__heading--header">Chatgpt prompt assistant</h4>
+
+                </div>
+            </div>
+            <div class="chatbox__messages">
+                <div></div>
+            </div>
+            <div class="chatbox__footer">
+                <input type="text" placeholder="Write a message...">
+                <button class="chatbox__send--footer send__button">Send</button>
+            </div>
+        </div>
+        <div class="chatbox__button">
+            <button><img src="{{ url_for('static', filename='images/chatbox-icon.svg') }}" /></button>
+        </div>
+    </div>
+</div>
+
+    <script>
+        $SCRIPT_ROOT = {{ request.script_root|tojson }};
+    </script>
+    <script type="text/javascript" src="{{ url_for('static', filename='app.js') }}"></script>
+
+</body>
+</html>

static/style.css

@@ -0,0 +1,205 @@
+* {
+    box-sizing: border-box;
+    margin: 0;
+    padding: 0;
+}
+
+body {
+    font-family: 'Nunito', sans-serif;
+    font-weight: 400;
+    font-size: 100%;
+    background-image: url('new.png');
+    background-repeat: no-repeat;
+    background-size: cover;
+
+}
+
+/* rest of your code */
+
+*, html {
+    --primaryGradient: linear-gradient(93.12deg, #1d4791 0.52%, #008aff 100%);
+    --secondaryGradient: linear-gradient(268.91deg, #1d4791 -2.14%, #008aff 99.69%);
+    --primaryBoxShadow: 0px 10px 15px rgba(0, 0, 0, 0.1);
+    --secondaryBoxShadow: 0px -10px 15px rgba(0, 0, 0, 0.1);
+    --primary: #1b2398;
+}
+
+/* CHATBOX
+=============== */
+.chatbox {
+    position: absolute;
+    bottom: 30px;
+    right: 30px;
+}
+
+/* CONTENT IS CLOSE */
+.chatbox__support {
+    display: flex;
+    flex-direction: column;
+    background: #369df8;
+    width: 300px;
+    height: 350px;
+    z-index: -123456;
+    opacity: 0;
+    transition: all .5s ease-in-out;
+}
+
+/* CONTENT ISOPEN */
+.chatbox--active {
+    transform: translateY(-40px);
+    z-index: 123456;
+    opacity: 1;
+
+}
+
+/* BUTTON */
+.chatbox__button {
+    text-align: right;
+}
+
+.send__button {
+    padding: 6px;
+    background: transparent;
+    border: none;
+    outline: none;
+    cursor: pointer;
+}
+
+
+/* HEADER */
+.chatbox__header {
+    position: sticky;
+    top: 0;
+    background: white;
+}
+
+/* MESSAGES */
+.chatbox__messages {
+    margin-top: auto;
+    display: flex;
+    overflow-y: scroll;
+    flex-direction: column-reverse;
+}
+
+.messages__item {
+    background: white;
+    max-width: 60.6%;
+    width: fit-content;
+}
+
+.messages__item--operator {
+    margin-left: auto;
+}
+
+.messages__item--visitor {
+    margin-right: auto;
+}
+
+/* FOOTER */
+.chatbox__footer {
+    position: sticky;
+    bottom: 0;
+}
+
+.chatbox__support {
+    background: #008aff;
+    height: 450px;
+    width: 350px;
+    box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.1);
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+}
+
+/* HEADER */
+.chatbox__header {
+    background: 'white';
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    justify-content: center;
+    padding: 15px 20px;
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+    box-shadow: var(--primaryBoxShadow);
+}
+
+.chatbox__image--header {
+    margin-right: 10px;
+}
+
+.chatbox__heading--header {
+    font-size: 1.2rem;
+    color: #008aff;
+}
+
+.chatbox__description--header {
+    font-size: .9rem;
+    color: #008aff
+}
+
+/* Messages */
+.chatbox__messages {
+    padding: 0 20px;
+}
+
+.messages__item {
+    margin-top: 10px;
+    background: 'white';
+    padding: 8px 12px;
+    max-width: 70%;
+}
+
+.messages__item--visitor,
+.messages__item--typing {
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+    border-bottom-right-radius: 20px;
+}
+
+.messages__item--operator {
+    border-top-left-radius: 20px;
+    border-top-right-radius: 20px;
+    border-bottom-left-radius: 20px;
+    background: 'white';
+    color: 'white';
+}
+
+/* FOOTER */
+.chatbox__footer {
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    justify-content: space-between;
+    padding: 20px 20px;
+    background: var(--secondaryGradient);
+    box-shadow: var(--secondaryBoxShadow);
+    border-bottom-right-radius: 10px;
+    border-bottom-left-radius: 10px;
+    margin-top: 20px;
+}
+
+.chatbox__footer input {
+    width: 80%;
+    border: none;
+    padding: 10px 10px;
+    border-radius: 30px;
+    text-align: left;
+}
+
+.chatbox__send--footer {
+    color: #008aff;
+}
+
+.chatbox__button button,
+.chatbox__button button:focus,
+.chatbox__button button:visited {
+    padding: 10px;
+    background: #008aff;
+    border: none;
+    outline: none;
+    border-top-left-radius: 50px;
+    border-top-right-radius: 50px;
+    border-bottom-left-radius: 50px;
+    box-shadow: 0px 10px 15px rgba(0, 0, 0, 0.1);
+    cursor: pointer;
+}

templates/base.html

@@ -0,0 +1,44 @@
+<!DOCTYPE html>
+<html lang="en">
+<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+
+<head>
+    <meta charset="UTF-8">
+    <title>Chatbot</title>
+</head>
+<body>
+
+<h2 align="center"></h2>
+<div class="container">
+    <div class="chatbox">
+        <div class="chatbox__support">
+            <div class="chatbox__header">
+                <div class="chatbox__image--header">
+                    <img src="chatbox-icon.png" alt="image">
+                </div>
+                <div class="chatbox__content--header">
+                    <h4 class="chatbox__heading--header">Chatgpt prompt assistant</h4>
+
+                </div>
+            </div>
+            <div class="chatbox__messages">
+                <div></div>
+            </div>
+            <div class="chatbox__footer">
+                <input type="text" placeholder="Write a message...">
+                <button class="chatbox__send--footer send__button">Send</button>
+            </div>
+        </div>
+        <div class="chatbox__button">
+            <button><img src="{{ url_for('static', filename='images/chatbox-icon.svg') }}" /></button>
+        </div>
+    </div>
+</div>
+
+    <script>
+        $SCRIPT_ROOT = {{ request.script_root|tojson }};
+    </script>
+    <script type="text/javascript" src="{{ url_for('static', filename='app.js') }}"></script>
+
+</body>
+</html>

train.py

@@ -0,0 +1,131 @@
+import numpy as np
+import random
+import json
+
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+from nltk_utils import bag_of_words, tokenize, stem
+from model import NeuralNet
+
+# Make sure you run this file as many times as it takes to get a low value for loss ~0.0004, so that gibberish words will not be misinterpreted
+with open('intents.json', 'r') as f:
+    intents = json.load(f)
+
+all_words = []
+tags = []
+xy = []
+# loop through each sentence in our intents patterns
+for intent in intents['intents']:
+    tag = intent['tag']
+    # add to tag list
+    tags.append(tag)
+    for pattern in intent['patterns']:
+        # tokenize each word in the sentence
+        w = tokenize(pattern)
+        # add to our words list
+        all_words.extend(w)
+        # add to xy pair
+        xy.append((w, tag))
+
+# stem and lower each word
+ignore_words = ['?', '.', '!']
+all_words = [stem(w) for w in all_words if w not in ignore_words]
+# remove duplicates and sort
+all_words = sorted(set(all_words))
+tags = sorted(set(tags))
+
+print(len(xy), "patterns")
+print(len(tags), "tags:", tags)
+print(len(all_words), "unique stemmed words:", all_words)
+
+# create training data
+X_train = []
+y_train = []
+for (pattern_sentence, tag) in xy:
+    # X: bag of words for each pattern_sentence
+    bag = bag_of_words(pattern_sentence, all_words)
+    X_train.append(bag)
+    # y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
+    label = tags.index(tag)
+    y_train.append(label)
+
+X_train = np.array(X_train)
+y_train = np.array(y_train)
+
+# Hyper-parameters
+num_epochs = 1000
+batch_size = 8
+learning_rate = 0.001
+input_size = len(X_train[0])
+hidden_size = 8
+output_size = len(tags)
+print(input_size, output_size)
+
+
+class ChatDataset(Dataset):
+
+    def __init__(self):
+        self.n_samples = len(X_train)
+        self.x_data = X_train
+        self.y_data = y_train
+
+    # support indexing such that dataset[i] can be used to get i-th sample
+    def __getitem__(self, index):
+        return self.x_data[index], self.y_data[index]
+
+    # we can call len(dataset) to return the size
+    def __len__(self):
+        return self.n_samples
+
+
+dataset = ChatDataset()
+train_loader = DataLoader(dataset=dataset,
+                          batch_size=batch_size,
+                          shuffle=True,
+                          num_workers=0)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+for epoch in range(num_epochs):
+    for (words, labels) in train_loader:
+        words = words.to(device)
+        labels = labels.to(dtype=torch.long).to(device)
+
+        # Forward pass
+        outputs = model(words)
+        # if y would be one-hot, we must apply
+        # labels = torch.max(labels, 1)[1]
+        loss = criterion(outputs, labels)
+
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    if (epoch + 1) % 100 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+print(f'final loss: {loss.item():.4f}')
+
+data = {
+    "model_state": model.state_dict(),
+    "input_size": input_size,
+    "hidden_size": hidden_size,
+    "output_size": output_size,
+    "all_words": all_words,
+    "tags": tags
+}
+
+FILE = "data.pth"
+torch.save(data, FILE)
+
+print(f'training complete. file saved to {FILE}')