Add ChatBot

AI_CHATBOT_METAVERSE/README.md

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+# AI_CHATBOT
+It is an Ai chatbot developed using natural language toolkit NLTK, pytorch
+I have developed this bot.
+
+Feel free to update the intents.json to make the domain  of the chatbot wider.

AI_CHATBOT_METAVERSE/chat.py

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+import random
+import json
+
+import torch
+
+from model import NeuralNet
+from nltk_utils import bag_of_words, tokenize
+
+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 = "Snapitizer"
+print("Let's chat! (type 'quit' to exit)")
+while True:
+    # sentence = "do you use credit cards?"
+    sentence = input("You: ")
+    if sentence == "quit":
+        break
+
+    sentence = tokenize(sentence)
+    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() > 0.75:
+        for intent in intents['intents']:
+            if tag == intent["tag"]:
+                print(f"{bot_name}: {random.choice(intent['responses'])}")
+    else:
+        print(f"{bot_name}: I do not understand...")

AI_CHATBOT_METAVERSE/intents.json

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+{
+  "intents": [
+    {
+      "tag": "greeting",
+      "patterns": [
+        "Hi",
+        "Hello",
+        "Hey",
+        "How are you",
+        "Is anyone there?"
+      ],
+      "responses": [
+        "Hello! Welcome to the Metaverse Front-End Playground. How can I assist you today?",
+        "Hi there! How can I help you with the Metaverse Front-End Playground today?",
+        "Hello! How can we make your experience better with the Metaverse Front-End Playground?"
+      ]
+    },
+    {
+      "tag": "goodbye",
+      "patterns": ["Bye", "Goodbye", "See you later"],
+      "responses": [
+        "Goodbye! Thank you for visiting the Metaverse Front-End Playground.",
+        "See you later! Have a great day!",
+        "Bye! Come back again soon to the Metaverse Front-End Playground."
+      ]
+    },
+    {
+      "tag": "thanks",
+      "patterns": ["Thanks", "Thank you", "That's helpful", "Thank you so much"],
+      "responses": [
+        "You're welcome! Happy to help.",
+        "Anytime! We're here to assist you.",
+        "My pleasure! If you have more questions, feel free to ask."
+      ]
+    },
+    {
+      "tag": "mission",
+      "patterns": [
+        "What is the Metaverse Front-End Playground?",
+        "Tell me about the Metaverse Front-End Playground",
+        "What does the Metaverse Front-End Playground do?",
+        "What is the mission of the Metaverse Front-End Playground?"
+      ],
+      "responses": [
+        "The Metaverse Front-End Playground is an open-source repository created to empower developers to explore, innovate, and collaborate in the metaverse space. Our mission is to provide a platform for developers to showcase their projects and dive into the virtual realm."
+      ]
+    },
+    {
+      "tag": "features",
+      "patterns": [
+        "What features does the Metaverse Front-End Playground offer?",
+        "Tell me about the features of the Metaverse Front-End Playground",
+        "What can the Metaverse Front-End Playground do?"
+      ],
+      "responses": [
+        "The Metaverse Front-End Playground offers several features, including a platform for developers to showcase their metaverse projects, tools for innovation and collaboration, and a dynamic community for networking and learning."
+      ]
+    },
+    {
+      "tag": "community",
+      "patterns": [
+        "What is the community like?",
+        "Can I join the community?",
+        "Tell me about the Metaverse Front-End Playground community"
+      ],
+      "responses": [
+        "The Metaverse Front-End Playground community is a dynamic and inclusive group of developers from all backgrounds. Whether you're a seasoned developer or a newcomer, you're invited to join us and collaborate on exciting projects."
+      ]
+    },
+    {
+      "tag": "collaboration",
+      "patterns": [
+        "How can I collaborate on projects?",
+        "Can I contribute to existing projects?",
+        "Tell me about collaboration opportunities"
+      ],
+      "responses": [
+        "You can collaborate on projects by joining our repository on GitHub and participating in ongoing projects. Feel free to contribute your ideas, code, and expertise to help us innovate in the metaverse space."
+      ]
+    },
+    {
+      "tag": "projects",
+      "patterns": [
+        "What projects are available?",
+        "Can I see some projects?",
+        "Tell me about the projects in the Metaverse Front-End Playground"
+      ],
+      "responses": [
+        "You can explore various projects on our GitHub repository. We have a wide range of projects showcasing different aspects of metaverse development, from virtual reality experiences to interactive web applications."
+      ]
+    },
+    {
+      "tag": "volunteer",
+      "patterns": [
+        "How can I volunteer?",
+        "Can I volunteer for the Metaverse Front-End Playground?",
+        "Tell me about volunteering opportunities"
+      ],
+      "responses": [
+        "Thank you for your interest in volunteering! Please visit our GitHub repository and get involved in our projects. Your contributions are valuable and appreciated."
+      ]
+    },
+    {
+      "tag": "donate",
+      "patterns": [
+        "How can I donate?",
+        "Can I donate to the Metaverse Front-End Playground?",
+        "Tell me about donation options"
+      ],
+      "responses": [
+        "Thank you for considering a donation! You can support our projects by contributing to our GitHub repository. Your donations help us continue our mission to innovate and collaborate in the metaverse space."
+      ]
+    }
+  ]
+}

AI_CHATBOT_METAVERSE/model.py

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+import torch
+import torch.nn as nn
+
+
+class NeuralNet(nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NeuralNet, self).__init__()
+        self.l1 = nn.Linear(input_size, hidden_size) 
+        self.l2 = nn.Linear(hidden_size, hidden_size) 
+        self.l3 = nn.Linear(hidden_size, num_classes)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        out = self.l1(x)
+        out = self.relu(out)
+        out = self.l2(out)
+        out = self.relu(out)
+        out = self.l3(out)
+        return out

AI_CHATBOT_METAVERSE/nltk_utils.py

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+import numpy as np
+import nltk
+nltk.download('punkt')
+from nltk.stem.porter import PorterStemmer
+stemmer = PorterStemmer()
+
+def tokenize(sentence):
+    return nltk.word_tokenize(sentence)
+
+
+def stem(word):
+    return stemmer.stem(word.lower())
+
+
+def bag_of_words(tokenized_sentence, words):
+
+    sentence_words = [stem(word) for word in tokenized_sentence]
+    bag = np.zeros(len(words), dtype=np.float32)
+    for idx, w in enumerate(words):
+        if w in sentence_words: 
+            bag[idx] = 1
+
+    return bag

AI_CHATBOT_METAVERSE/train.py

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+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
+
+with open('intents.json', 'r') as f:
+    intents = json.load(f)
+
+all_words = []
+tags = []
+xy = []
+for intent in intents['intents']:
+    tag = intent['tag']
+    tags.append(tag)
+    for pattern in intent['patterns']:
+        w = tokenize(pattern)
+        all_words.extend(w)
+        xy.append((w, tag))
+
+ignore_words = ['?', '.', '!']
+all_words = [stem(w) for w in all_words if w not in ignore_words]
+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)
+
+X_train = []
+y_train = []
+for (pattern_sentence, tag) in xy:
+    bag = bag_of_words(pattern_sentence, all_words)
+    X_train.append(bag)
+    label = tags.index(tag)
+    y_train.append(label)
+
+X_train = np.array(X_train)
+y_train = np.array(y_train)
+
+num_epochs = 1000
+batch_size = 8
+learning_rate = 0.01
+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
+
+    def __getitem__(self, index):
+        return self.x_data[index], self.y_data[index]
+    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)
+
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+for epoch in range(num_epochs):
+    for (words, labels) in train_loader:
+        words = words.to(device)
+        labels = labels.to(dtype=torch.long).to(device)
+        outputs = model(words)
+
+        loss = criterion(outputs, labels)
+        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}')