Add Chatbot AI project

scripts/Chatbot AI/README.md

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+
+# Chatbot AI 🤖
+
+This project contains an AI powered chatbot that you can interact with for both fun and to learn about how these technologies work.
+
+## Tech Stack
+
+* Python 3.9
+* Tensorflow
+* Keras
+* nltk
+* numpy
+
+## How to setup the project
+
+* Clone this repo
+
+```bash
+    git clone https://github.com/metafy-social/daily-python-scripts.git
+```
+
+* Move to the project folder
+```bash
+     cd '.\scripts\Chatbot AI\'
+```
+
+* Install dependencies
+```bash
+    pip install -r requirements.txt
+```
+
+* Run the python script `chatbot.py`
+
+
+## Screenshots of the project
+
+
+![screenshot 1](https://user-images.githubusercontent.com/61963664/193405239-5403c601-2c5b-4443-b463-1452e72cbb20.PNG)
+
+![screenshot 2](https://user-images.githubusercontent.com/61963664/193405262-5b05c68b-994c-4b04-ba77-df878d3f42c8.PNG)

scripts/Chatbot AI/chatbot.py

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+import random
+import json
+import pickle
+import numpy as np
+
+import nltk
+from nltk.stem import WordNetLemmatizer
+
+from tensorflow import keras
+from keras.models import load_model
+
+lemmatizer = WordNetLemmatizer()
+intents = json.loads(open('intents.json').read())
+
+words = pickle.load(open('words.pkl', 'rb'))
+classes = pickle.load(open('classes.pkl', 'rb'))
+model = load_model('chatbot_model.h5')
+
+def clean_up_sentence(sentence):
+    sentence_words = nltk.word_tokenize(sentence)
+    sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]
+    return sentence_words
+
+
+def bag_of_words(sentence):
+    sentence_words = clean_up_sentence(sentence)
+    bag = [0] * len(words)
+    for w in sentence_words:
+        for i, word in enumerate(words):
+            if word == w:
+                bag[i] = 1
+    return np.array(bag)
+
+def predict_class(sentence):
+    bow = bag_of_words(sentence)
+    res = model.predict(np.array([bow]), verbose=0)[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 = []
+    for r in results:
+        return_list.append({'intent': classes[r[0]], 'probability': str(r[1])})
+    return return_list
+
+def get_response(intents_list, intents_json):
+    tag = intents_list[0]['intent']
+    list_of_intents = intents_json['intents']
+    for i in list_of_intents:
+        if i['tag'] == tag:
+            result = random.choice(i['responses'])
+            break
+    return result
+
+print("Welcome to Metafy-Social Chatbot, let's start chatting!")
+
+while True:
+    message = input("\n>")
+    ints = predict_class(message)
+    res = get_response(ints, intents)
+    print(f'Chatbot: {res}')

scripts/Chatbot AI/intents.json

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+{"intents": [
+    {"tag": "greetings",
+    "patterns": ["hello", "hey", "hi", "good day", "greetings", "what's up?", "how is it going?"],
+    "responses": ["Hello!", "Hey!", "What can i do for you?"]
+    },
+    {"tag": "goodbye",
+    "patterns": ["cya", "See you later", "Goodbye", "I am leaving", "Have a Good day", "bye", "cao", "see ya"],
+    "responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!"]
+    },
+    {"tag": "age",
+    "patterns": ["how old", "how old are you?", "what is your age?", "age"],
+    "responses": ["I am immortal", "I do not age", "you guess", "I'm too shy >.<"]
+    },
+    {"tag": "name",
+    "patterns": ["What is your name?", "What should i call you?", "what's your name?", "who are you?", "Can you tell me your name ?"],
+    "responses": ["I am metafy-social AI", "I am metafy-social Chatbot"]
+    },
+    {"tag": "hours",
+    "patterns": ["when are you guys open?", "hours open?", "Are you open today?", "Are you open"],
+    "responses": ["24/7", "We are open 24/7", "We are always open"]
+    },
+    {"tag": "learn",
+    "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?"],
+    "responses": ["You can learn python at our repository 'https://github.com/metafy-social/daily-python-scripts'"]}
+]}

scripts/Chatbot AI/requirements.txt

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+keras==2.9.0
+nltk==3.7
+numpy==1.20.3
+tensorflow==2.9.1

scripts/Chatbot AI/training.py

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+import random
+import json
+import pickle
+import numpy as np
+
+import nltk
+nltk.download('punkt')
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+from nltk.stem import WordNetLemmatizer
+
+from tensorflow import keras
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Dropout
+from keras.optimizers import SGD
+
+lemmatizer = WordNetLemmatizer()
+
+intents = json.loads(open('intents.json').read())
+
+words = []
+classes = []
+documents = []
+ignore_letters = ['?', '!', '.', ',']
+
+for intent in intents['intents']:
+    for pattern in intent['patterns']:
+        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'])
+
+# print(documents)
+
+words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
+words = sorted(set(words))
+
+classes = sorted(set(classes))
+
+pickle.dump(words, open('words.pkl', 'wb'))
+pickle.dump(classes, open('classes.pkl', 'wb'))
+
+training = []
+output_empty = [0] * len(classes)
+
+for document in documents:
+    bag = []
+    word_patterns = document[0]
+    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
+    for word in words:
+        bag.append(1) if word in word_patterns else bag.append(0)
+
+    output_row = list(output_empty)
+    output_row[classes.index(document[1])] = 1
+    training.append([bag, output_row])
+
+random.shuffle(training)
+training = np.array(training)
+
+train_x = list(training[:, 0])
+train_y = 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(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+
+hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
+model.save('chatbot_model.h5', hist)
+print('Done')