11.5 Improving and Maintaining the Chatbot

from flask import Flask, request, jsonifyfrom nlp_engine import predict_intentfrom api_integration import get_weatherfrom task_manager import add_reminder, view_reminders, delete_reminder app = Flask(__name__) feedback_data = [] @app.route('/chat', methods=['POST'])def chat():    user_input = request.json.get('message')    intent = predict_intent(user_input)     if intent == "greeting":        response = "Hello! How can I assist you today?"    elif intent == "goodbye":        response = "Goodbye! Have a great day!"    elif intent == "weather":        city = request.json.get('city')        response = get_weather(city)    elif intent == "reminder":        reminder_text = request.json.get('reminder_text')        reminder_time = request.json.get('reminder_time')        response = add_reminder(reminder_text, reminder_time)    elif intent == "view_reminders":        response = view_reminders()    elif intent == "delete_reminder":        reminder_text = request.json.get('reminder_text')        response = delete_reminder(reminder_text)    else:        response = "I'm sorry, I don't understand that. Can you please rephrase?"     return jsonify({'response': response}) @app.route('/feedback', methods=['POST'])def feedback():    user_feedback = request.json    feedback_data.append(user_feedback)    return jsonify({'message': 'Thank you for your feedback!'}) if __name__ == '__main__':    app.run(debug=True)

{    "message": "What's the weather like?",    "response": "The temperature in New York is 25°C with clear skies.",    "rating": 4,    "comment": "The response was quick and accurate."}

{    "intents": [        {            "tag": "greeting",            "patterns": ["Hi", "Hello", "Hey", "Good morning", "Good afternoon", "Good evening"],            "responses": ["Hello! How can I assist you today?", "Hi there! What can I do for you?", "Hey! How can I help?", "Good day! How can I assist you?"]        },        {            "tag": "goodbye",            "patterns": ["Bye", "Goodbye", "See you later", "Talk to you soon"],            "responses": ["Goodbye! Have a great day!", "See you later! Take care!", "Talk to you soon!"]        },        {            "tag": "weather",            "patterns": ["What's the weather like?", "Tell me the weather", "How's the weather today?", "Weather update", "Is it going to rain today?"],            "responses": ["Let me check the weather for you.", "Fetching the weather details...", "Here's the weather update."]        },        {            "tag": "reminder",            "patterns": ["Set a reminder", "Remind me to", "Add a reminder", "Can you remind me?", "Create a reminder"],            "responses": ["Sure, what would you like to be reminded about?", "When would you like the reminder to be set?", "Got it. What is the reminder for?"]        },        {            "tag": "knowledge",            "patterns": ["Tell me a fact", "Do you know any interesting facts?", "Share some knowledge", "Give me a fun fact"],            "responses": ["Here's a fun fact: Did you know that honey never spoils?", "Sure! Did you know that octopuses have three hearts?", "Did you know that a day on Venus is longer than a year on Venus?"]        }    ]} 

import jsonimport numpy as npimport tensorflow as tffrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense, Dropoutfrom sklearn.preprocessing import LabelEncoderfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.model_selection import train_test_splitimport nltkfrom nltk.corpus import stopwordsfrom nltk.stem import WordNetLemmatizerimport stringimport pickle # Download NLTK resourcesnltk.download('punkt')nltk.download('stopwords')nltk.download('wordnet') # Load the updated intents filewith open('data/intents.json') as file:    intents = json.load(file) # Initialize lemmatizerlemmatizer = WordNetLemmatizer() # Define preprocessing functiondef preprocess_text(text):    text = text.lower()    tokens = nltk.word_tokenize(text)    tokens = [word for word in tokens if word not in string.punctuation and word not in stopwords.words('english')]    tokens = [lemmatizer.lemmatize(word) for word in tokens]    return ' '.join(tokens) # Preprocess patterns and extract corresponding tagspatterns = []tags = []for intent in intents['intents']:    for pattern in intent['patterns']:        patterns.append(preprocess_text(pattern))        tags.append(intent['tag']) # Encode the tagslabel_encoder = LabelEncoder()labels = label_encoder.fit_transform(tags) # Vectorize the patternsvectorizer = TfidfVectorizer()X = vectorizer.fit_transform(patterns).toarray()y = np.array(labels) # Split the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Build the modelmodel = Sequential()model.add(Dense(128, input_shape=(X_train.shape[1],), activation='relu'))model.add(Dropout(0.5))model.add(Dense(64, activation='relu'))model.add(Dropout(0.5))model.add(Dense(len(label_encoder.classes_), activation='softmax')) # Compile the modelmodel.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # Train the modelmodel.fit(X_train, y_train, epochs=100, batch_size=8, verbose=1, validation_data=(X_test, y_test)) # Save the model and tokenizermodel.save('models/nlp_model.h5')with open('models/vectorizer.pickle', 'wb') as file:    pickle.dump(vectorizer, file)with open('models/label_encoder.pickle', 'wb') as file:    pickle.dump(label_encoder, file)

import requests # Define function to get news updatesdef get_news():    api_key = "your_newsapi_api_key"    url = f"<https://newsapi.org/v2/top-headlines?country=us&apiKey={api_key}>"     response = requests.get(url)    data = response.json()     if data["status"] == "ok":        articles = data["articles"]        news_list = [f"{article['title']} - {article['source']['name']}" for article in articles[:5]]        return "Here are the top news headlines:\\n" + "\\n".join(news_list)    else:        return "Unable to fetch news at the moment." # Test the news API integrationnews_update = get_news()print(f"News Update: {news_update}")

@app.route('/chat', methods=['POST'])def chat():    user_input = request.json.get('message')    intent = predict_intent(user_input)     if intent == "greeting":        response = "Hello! How can I assist you today?"    elif intent == "goodbye":        response = "Goodbye! Have a  great day!"    elif intent == "weather":        city = request.json.get('city')        response = get_weather(city)    elif intent == "reminder":        reminder_text = request.json.get('reminder_text')        reminder_time = request.json.get('reminder_time')        response = add_reminder(reminder_text, reminder_time)    elif intent == "view_reminders":        response = view_reminders()    elif intent == "delete_reminder":        reminder_text = request.json.get('reminder_text')        response = delete_reminder(reminder_text)    elif intent == "news":        response = get_news()    else:        response = "I'm sorry, I don't understand that. Can you please rephrase?"     return jsonify({'response': response})

import logging # Configure logginglogging.basicConfig(filename='chatbot.log', level=logging.INFO) @app.route('/chat', methods=['POST'])def chat():    user_input = request.json.get('message')    intent = predict_intent(user_input)     if intent == "greeting":        response = "Hello! How can I assist you today?"    elif intent == "goodbye":        response = "Goodbye! Have a great day!"    elif intent == "weather":        city = request.json.get('city')        response = get_weather(city)    elif intent == "reminder":        reminder_text = request.json.get('reminder_text')        reminder_time = request.json.get('reminder_time')        response = add_reminder(reminder_text, reminder_time)    elif intent == "view_reminders":        response = view_reminders()    elif intent == "delete_reminder":        reminder_text = request.json.get('reminder_text')        response = delete_reminder(reminder_text)    elif intent == "news":        response = get_news()    else:        response = "I'm sorry, I don't understand that. Can you please rephrase?"     logging.info(f"User Input: {user_input}, Predicted Intent: {intent}, Response: {response}")    return jsonify({'response': response})