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Merge pull request #39 from metafy-social/revert-37-chatbot
Revert "Add Chatbot AI project"
This commit is contained in:
Advaita Saha
GitHub
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f9e6243216
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# Chatbot AI 🤖
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This project contains an AI powered chatbot that you can interact with for both fun and to learn about how these technologies work.
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## Tech Stack
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* Python 3.9
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* Tensorflow
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* Keras
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* nltk
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* numpy
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## How to setup the project
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* Clone this repo
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```bash
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git clone https://github.com/metafy-social/daily-python-scripts.git
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```
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* Move to the project folder
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```bash
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cd '.\scripts\Chatbot AI\'
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```
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* Install dependencies
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```bash
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pip install -r requirements.txt
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```
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* Run the python script `chatbot.py`
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## Screenshots of the project
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import random
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import json
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import pickle
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import numpy as np
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import nltk
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from nltk.stem import WordNetLemmatizer
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from tensorflow import keras
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from keras.models import load_model
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lemmatizer = WordNetLemmatizer()
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intents = json.loads(open('intents.json').read())
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words = pickle.load(open('words.pkl', 'rb'))
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classes = pickle.load(open('classes.pkl', 'rb'))
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model = load_model('chatbot_model.h5')
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def clean_up_sentence(sentence):
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sentence_words = nltk.word_tokenize(sentence)
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sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]
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return sentence_words
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def bag_of_words(sentence):
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sentence_words = clean_up_sentence(sentence)
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bag = [0] * len(words)
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for w in sentence_words:
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for i, word in enumerate(words):
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if word == w:
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bag[i] = 1
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return np.array(bag)
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def predict_class(sentence):
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bow = bag_of_words(sentence)
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res = model.predict(np.array([bow]), verbose=0)[0]
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ERROR_THRESHOLD = 0.25
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results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
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results.sort(key=lambda x: x[1], reverse=True)
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return_list = []
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for r in results:
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return_list.append({'intent': classes[r[0]], 'probability': str(r[1])})
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return return_list
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def get_response(intents_list, intents_json):
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tag = intents_list[0]['intent']
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list_of_intents = intents_json['intents']
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for i in list_of_intents:
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if i['tag'] == tag:
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result = random.choice(i['responses'])
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break
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return result
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print("Welcome to Metafy-Social Chatbot, let's start chatting!")
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while True:
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message = input("\n>")
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ints = predict_class(message)
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res = get_response(ints, intents)
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print(f'Chatbot: {res}')
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{"intents": [
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{"tag": "greetings",
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"patterns": ["hello", "hey", "hi", "good day", "greetings", "what's up?", "how is it going?"],
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"responses": ["Hello!", "Hey!", "What can i do for you?"]
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},
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{"tag": "goodbye",
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"patterns": ["cya", "See you later", "Goodbye", "I am leaving", "Have a Good day", "bye", "cao", "see ya"],
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"responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!"]
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},
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{"tag": "age",
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"patterns": ["how old", "how old are you?", "what is your age?", "age"],
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"responses": ["I am immortal", "I do not age", "you guess", "I'm too shy >.<"]
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},
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{"tag": "name",
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"patterns": ["What is your name?", "What should i call you?", "what's your name?", "who are you?", "Can you tell me your name ?"],
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"responses": ["I am metafy-social AI", "I am metafy-social Chatbot"]
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},
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{"tag": "hours",
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"patterns": ["when are you guys open?", "hours open?", "Are you open today?", "Are you open"],
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"responses": ["24/7", "We are open 24/7", "We are always open"]
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},
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{"tag": "learn",
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"patterns": ["What are good resources to learn python?", "Where can i learn python programming", "Where to learn python?", "How to learn python?", "Where can i study python?", "Study?"],
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"responses": ["You can learn python at our repository 'https://github.com/metafy-social/daily-python-scripts'"]}
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]}
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keras==2.9.0
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nltk==3.7
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numpy==1.20.3
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tensorflow==2.9.1
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import random
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import json
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import pickle
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import numpy as np
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import nltk
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nltk.download('punkt')
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nltk.download('wordnet')
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nltk.download('omw-1.4')
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from nltk.stem import WordNetLemmatizer
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from tensorflow import keras
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from keras.models import Sequential
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from keras.layers import Dense, Activation, Dropout
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from keras.optimizers import SGD
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lemmatizer = WordNetLemmatizer()
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intents = json.loads(open('intents.json').read())
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words = []
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classes = []
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documents = []
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ignore_letters = ['?', '!', '.', ',']
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for intent in intents['intents']:
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for pattern in intent['patterns']:
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word_list = nltk.word_tokenize(pattern)
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words.extend(word_list)
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documents.append((word_list, intent['tag']))
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if intent['tag'] not in classes:
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classes.append(intent['tag'])
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# print(documents)
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words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
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words = sorted(set(words))
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classes = sorted(set(classes))
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pickle.dump(words, open('words.pkl', 'wb'))
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pickle.dump(classes, open('classes.pkl', 'wb'))
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training = []
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output_empty = [0] * len(classes)
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for document in documents:
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bag = []
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word_patterns = document[0]
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word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
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for word in words:
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bag.append(1) if word in word_patterns else bag.append(0)
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output_row = list(output_empty)
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output_row[classes.index(document[1])] = 1
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training.append([bag, output_row])
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random.shuffle(training)
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training = np.array(training)
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train_x = list(training[:, 0])
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train_y = list(training[:, 1])
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model = Sequential()
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model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(64, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(len(train_y[0]), activation='softmax'))
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sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
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model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
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hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
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model.save('chatbot_model.h5', hist)
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print('Done')
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