ferdian15/chatbotbookrecommendation

from google.colab import drive drive.mount('/content/drive') import nltk

nltk.download('punkt') nltk.download('wordnet')

import json import random import numpy as np import nltk from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout from tensorflow.keras.optimizers import SGD from sklearn.preprocessing import LabelEncoder from nltk.stem import WordNetLemmatizer

file_path = '/content/drive/MyDrive/Colab_Notebooks/Dataset/intents.json' with open(file_path,'r') as file: data = json.load(file)

lemmatizer = WordNetLemmatizer() words = [] classes = [] documents = [] ignore_words = ['?', '!', '.']

for intent in data['intents']: for pattern in intent['patterns']: # Tokenize each word word_list = nltk.word_tokenize(pattern) words.extend(word_list) documents.append((word_list, intent['tag'])) if intent['tag'] not in classes: classes.append(intent['tag'])

words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words] words = sorted(list(set(words))) classes = sorted(list(set(classes)))

training = [] output_empty = [0] * len(classes)

for doc in documents: bag = [] word_patterns = doc[0] word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns] for w in words: bag.append(1 if w in word_patterns else 0)

output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1
training.append([bag, output_row])

random.shuffle(training) training = np.array(training, dtype=object)

train_x = np.array(list(training[:, 0])) train_y = np.array(list(training[:, 1]))

model = Sequential() model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu')) model.add(Dropout(0.5)) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(len(train_y[0]), activation='softmax'))

sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

hist = model.fit(train_x, train_y, epochs=200, batch_size=5, verbose=1) model.save('chatbot_model.h5', hist)

print("Model created and saved successfully!")

import tensorflow as tf model = tf.keras.models.load_model('chatbot_model.h5')

def clean_up_sentence(sentence): sentence_words = nltk.word_tokenize(sentence) sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words] return sentence_words

def bag_of_words(sentence, words): sentence_words = clean_up_sentence(sentence) bag = [0] * len(words) for s in sentence_words: for i, w in enumerate(words): if w == s: bag[i] = 1 return np.array(bag)

def predict_class(sentence, model): bow = bag_of_words(sentence, words) res = model.predict(np.array([bow]))[0] ERROR_THRESHOLD = 0.25 results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]

results.sort(key=lambda x: x[1], reverse=True)
return_list = [{"intent": classes[r[0]], "probability": str(r[1])} for r in results]
return return_list

def get_response(intents_list, intents_json): tag = intents_list[0]['intent'] for i in intents_json['intents']: if i['tag'] == tag: return random.choice(i['responses'])

print("Bot is ready to chat! Type 'quit' to stop.") while True: message = input("You: ") if message.lower() == "quit": break

ints = predict_class(message, model)
if ints:
    res = get_response(ints, data)
    print("Bot:", res)
else:
    print("Bot: Sorry, I don't understand that.")\