Create README.md

README.md

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+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.")\