Demo: Amazon SageMaker

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Introduction to Amazon SageMaker Amazon SageMaker is a cloud platform that helps users easily build, train, and deploy machine learning models. It's great for beginners and experts alike, simplifying complex tasks that usually require deep machine learning knowledge.

Continuously accepts input from the user. Processes the input. Responds until the user decides to end the conversation by typing "quit". Create A Chatbot in 3 Steps

1. Defining the Responses Dictionary responses = { "hi": "Hello there! How can I assist you today?", "how are you": "I'm a bot, so I don't have feelings, but I'm functioning properly!", "bye": "Goodbye! Have a great day!", "help": "Sure, I can help you. What do you need assistance with?", } responses is a Python dictionary that acts as a simple database for the chatbot. Each key is a possible message that the chatbot can recognize (e.g., "hi", "how are you"), and each value is the chatbot's predefined response to that message.

2. Defining the Function to Get Bot Responses def get_bot_response(user_input): user_input = user_input.lower() return responses.get(user_input, "I'm not sure how to respond to that. Can you try asking something else?") def get_bot_response(user_input): defines a function in Python called get_bot_response, which will process the user's input. user_input = user_input.lower() takes the user's input and converts it to lowercase. This is done so the chatbot can match the input to the keys in the responses dictionary without being case-sensitive. return responses.get(user_input, "I'm not sure how to respond to that. Can you try asking something else?") uses the .get() method to look for the user_input in the responses dictionary. If user_input is found, it returns the associated response. If it's not found, it returns a default message: "I'm not sure how to respond to that. Can you try asking something else?"

3.Running the Main Chat Loop**** while True: user_input = input("You: ") if user_input.lower() == "quit": print("Bot: Goodbye!") break response = get_bot_response(user_input) print(f"Bot: {response}") while True: starts an infinite loop. This allows the chatbot to continue running and responding to user input until it's explicitly told to stop. user_input = input("You: ") displays "You: " to the user and waits for them to enter their message. This message is stored in the variable user_input. if user_input.lower() == "quit": checks if the user has typed "quit" (case-insensitive) as their message. print("Bot: Goodbye!") sends a farewell message to the user if they've decided to quit. break exits the while loop, effectively ending the chatbot session. response = get_bot_response(user_input) calls the get_bot_response function with the user's input to get the appropriate response from the chatbot. print(f"Bot: {response}") prints out the chatbot's response to the console.

4. Code of this chatbox****

Define a dictionary with predefined responses

responses = { "hi": "Hello there! How can I assist you today?", "how are you": "I'm a bot, so I don't have feelings, but I'm functioning properly!", "bye": "Goodbye! Have a great day!", "help": "Sure, I can help you. What do you need assistance with?", }

Function to get the bot response

def get_bot_response(user_input):

Make the input lowercase to match the dictionary keys

user_input = user_input.lower()

# Return the matching response if it exists, otherwise return a default response
return responses.get(user_input, "I'm not sure how to respond to that. Can you try asking something else?")

Main chat loop

while True: user_input = input("You: ") if user_input.lower() == "quit": print("Bot: Goodbye!") break

response = get_bot_response(user_input)
print(f"Bot: {response}")