Add 3 new bots

Cheerful/Cheerful.py

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+import json
+import pickle
+import random
+
+import nltk
+import numpy
+from nltk.stem import LancasterStemmer
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.models import Sequential, model_from_json
+import ssl
+
+ssl._create_default_https_context = ssl._create_unverified_context
+
+nltk.download('punkt')
+
+stemmer = LancasterStemmer()
+
+with open("Cheerful/intents.json") as file:
+    data = json.load(file)
+
+try:
+    with open("cheerful.pickle", "rb") as file:
+        words, labels, training, output = pickle.load(file)
+
+except FileNotFoundError:
+    words = []
+    labels = []
+    docs_x = []
+    docs_y = []
+
+    for intent in data["intents"]:
+        for pattern in intent["patterns"]:
+            wrds = nltk.word_tokenize(pattern)
+            words.extend(wrds)
+            docs_x.append(wrds)
+            docs_y.append(intent["tag"])
+
+        if intent["tag"] not in labels:
+            labels.append(intent["tag"])
+
+    words = [stemmer.stem(w.lower()) for w in words if w != "?"]
+    words = sorted(list(set(words)))
+
+    labels = sorted(labels)
+
+    training = []
+    output = []
+
+    output_empty = [0 for _ in range(len(labels))]
+
+    for x, doc in enumerate(docs_x):
+        bag = []
+
+        wrds = [stemmer.stem(w.lower()) for w in doc]
+
+        for w in words:
+            if w in wrds:
+                bag.append(1)
+            else:
+                bag.append(0)
+
+        output_row = output_empty[:]
+        output_row[labels.index(docs_y[x])] = 1
+
+        training.append(bag)
+        output.append(output_row)
+
+    training = numpy.array(training)
+    output = numpy.array(output)
+
+    with open("cheerful.pickle", "wb") as file:
+        pickle.dump((words, labels, training, output), file)
+
+try:
+    json_file = open('cheerful.json', 'r')
+    loaded_model_json = json_file.read()
+    json_file.close()
+    myChatModel = model_from_json(loaded_model_json)
+    myChatModel.load_weights("cheerful.h5")
+    print("Loaded model from disk")
+
+except FileNotFoundError:
+    # Make our neural network
+    myChatModel = Sequential()
+    myChatModel.add(Dense(8, input_shape=[len(words)], activation='relu'))
+    myChatModel.add(Dense(len(labels), activation='softmax'))
+
+    # optimize the model
+    myChatModel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+    # train the model
+    myChatModel.fit(training, output, epochs=5000, batch_size=20)
+
+    # serialize model to JSON and save it to disk
+    model_json = myChatModel.to_json()
+    with open("cheerful.json", "w") as json_file:
+        json_file.write(model_json)
+
+    # serialize weights to HDF5
+    myChatModel.save_weights("cheerful.h5")
+    print("Saved model to disk")
+
+
+def bag_of_words(s, words):
+    bag = [0 for _ in range(len(words))]
+
+    s_words = nltk.word_tokenize(s)
+    s_words = [stemmer.stem(word.lower()) for word in s_words]
+
+    for se in s_words:
+        for i, w in enumerate(words):
+            if w == se:
+                bag[i] = 1
+
+    return numpy.array(bag)
+
+
+def chatWithCheerful(inputText):
+    currentText = bag_of_words(inputText, words)
+    currentTextArray = [currentText]
+    numpyCurrentText = numpy.array(currentTextArray)
+
+    if numpy.all((numpyCurrentText == 0)):
+        return "I didn't get that, try again"
+
+    result = myChatModel.predict(numpyCurrentText.reshape(1, -1))  # Reshape to represent a batch of size 1
+    result_index = numpy.argmax(result, axis=-1)
+    tag = labels[result_index[0]]
+
+    if result[0][result_index] > 0.5:
+        for tg in data["intents"]:
+            if tg['tag'] == tag:
+                responses = tg['responses']
+
+        return random.choice(responses)
+
+    else:
+        return "I didn't get that, try again"
+
+def chat():
+    print("Start talking with the chatbot (try quit to stop)")
+
+    while True:
+        inp = input("You: ")
+        if inp.lower() == "quit":
+            break
+
+        print(chatWithCheerful(inp))
+
+#chat()

Cheerful/cheerful.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 824], "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_input"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "batch_input_shape": [null, 824], "units": 8, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 824]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 133, "activation": "softmax", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 8]}}]}, "keras_version": "2.13.1", "backend": "tensorflow"}