Building a chatbot with Gemma

import os
from google.colab import userdata

# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env
# vars as appropriate for your system.
os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

# Install Keras 3 last. See https://keras.io/getting_started/ for more details.
pip install -q tensorflow-cpu
pip install -q -U keras-nlp tensorflow-hub
pip install -q -U keras>=3
pip install -q -U tensorflow-text

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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.3.3 which is incompatible.
tensorflow 2.15.0 requires ml-dtypes~=0.2.0, but you have ml-dtypes 0.3.2 which is incompatible.
tensorflow 2.15.0 requires tensorboard<2.16,>=2.15, but you have tensorboard 2.16.2 which is incompatible.
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-cpu 2.16.1 requires keras>=3.0.0, but you have keras 2.15.0 which is incompatible.
tensorflow-cpu 2.16.1 requires ml-dtypes~=0.3.1, but you have ml-dtypes 0.2.0 which is incompatible.
tensorflow-cpu 2.16.1 requires tensorboard<2.17,>=2.16, but you have tensorboard 2.15.2 which is incompatible.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.3.3 which is incompatible.
tensorflow-cpu 2.16.1 requires ml-dtypes~=0.3.1, but you have ml-dtypes 0.2.0 which is incompatible.
tensorflow-cpu 2.16.1 requires tensorboard<2.17,>=2.16, but you have tensorboard 2.15.2 which is incompatible.
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 589.8/589.8 MB 2.7 MB/s eta 0:00:00
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.16.1 which is incompatible.

import os

# Select JAX as the backend
os.environ["KERAS_BACKEND"] = "jax"

# Pre-allocate 100% of TPU memory to minimize memory fragmentation
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "1.0"

import keras
import keras_nlp

# for reproducibility
keras.utils.set_random_seed(42)

gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_1.1_instruct_2b_en")

Attaching 'metadata.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'metadata.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'task.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'config.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'metadata.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'metadata.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'config.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'config.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'metadata.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'metadata.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'preprocessor.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...
Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_1.1_instruct_2b_en/3' to your Colab notebook...

gemma_lm.summary()

from IPython.display import Markdown
import textwrap

def display_chat(prompt, text):
  formatted_prompt = "<font size='+1' color='brown'>🙋‍♂️<blockquote>" + prompt + "</blockquote></font>"
  text = text.replace('•', '  *')
  text = textwrap.indent(text, '> ', predicate=lambda _: True)
  formatted_text = "<font size='+1' color='teal'>🤖\n\n" + text + "\n</font>"
  return Markdown(formatted_prompt+formatted_text)

def to_markdown(text):
  text = text.replace('•', '  *')
  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))

<start_of_turn>user\n  ... <end_of_turn>\n
<start_of_turn>model\n ... <end_of_turn>\n

class ChatState():
  """
  Manages the conversation history for a turn-based chatbot
  Follows the turn-based conversation guidelines for the Gemma family of models
  documented at https://ai.google.dev/gemma/docs/formatting
  """

  __START_TURN_USER__ = "<start_of_turn>user\n"
  __START_TURN_MODEL__ = "<start_of_turn>model\n"
  __END_TURN__ = "<end_of_turn>\n"

  def __init__(self, model, system=""):
    """
    Initializes the chat state.

    Args:
        model: The language model to use for generating responses.
        system: (Optional) System instructions or bot description.
    """
    self.model = model
    self.system = system
    self.history = []

  def add_to_history_as_user(self, message):
      """
      Adds a user message to the history with start/end turn markers.
      """
      self.history.append(self.__START_TURN_USER__ + message + self.__END_TURN__)

  def add_to_history_as_model(self, message):
      """
      Adds a model response to the history with start/end turn markers.
      """
      self.history.append(self.__START_TURN_MODEL__ + message + self.__END_TURN__)

  def get_history(self):
      """
      Returns the entire chat history as a single string.
      """
      return "".join([*self.history])

  def get_full_prompt(self):
    """
    Builds the prompt for the language model, including history and system description.
    """
    prompt = self.get_history() + self.__START_TURN_MODEL__
    if len(self.system)>0:
      prompt = self.system + "\n" + prompt
    return prompt

  def send_message(self, message):
    """
    Handles sending a user message and getting a model response.

    Args:
        message: The user's message.

    Returns:
        The model's response.
    """
    self.add_to_history_as_user(message)
    prompt = self.get_full_prompt()
    response = self.model.generate(prompt, max_length=1024)
    result = response.replace(prompt, "")  # Extract only the new response
    self.add_to_history_as_model(result)
    return result

chat = ChatState(gemma_lm)
message = "Tell me, in a few words,  how to compute all prime numbers up to 1000?"
display_chat(message, chat.send_message(message))

message = "Now in Python! No numpy, please!"
display_chat(message, chat.send_message(message))

message = "Thank you, it works! Can you explain the code in plain English?"
display_chat(message, chat.send_message(message))

message = "Great! Now add those explanations as comments in the code."
display_chat(message, chat.send_message(message))

def is_prime(n):
  """
  Checks if a number is prime.

  Args:
    n: The number to check.

  Returns:
    True if n is prime, False otherwise.
  """

  # If n is less than or equal to 1, it is not prime.
  if n <= 1:
    return False

  # Iterate through all the numbers from 2 to the square root of n.
  for i in range(2, int(n**0.5) + 1):
    # If n is divisible by any of the numbers in the range from 2 to the square root of n, it is not prime.
    if n % i == 0:
      return False

  # If no divisors are found, n is prime.
  return True


# Initialize an empty list to store prime numbers.
primes = []

# Iterate through all the numbers from 2 to 1000.
for i in range(2, 1001):
  # If the number is prime, add it to the list.
  if is_prime(i):
    primes.append(i)

# Print the prime numbers.
print(primes)

[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997]

print(chat.get_history())

<start_of_turn>user
Tell me, in a few words,  how to compute all prime numbers up to 1000?<end_of_turn>
<start_of_turn>model
The Sieve of Eratosthenes is a widely used method to compute all prime numbers up to a given limit. It involves iteratively marking out multiples of each prime number.<end_of_turn>
<start_of_turn>user
Now in Python! No numpy, please!<end_of_turn>
<start_of_turn>model

```python
def prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True
```<end_of_turn>
<start_of_turn>user
Thank you, it works! Can you explain the code in plain English?<end_of_turn>
<start_of_turn>model
The provided code defines a function `prime` that checks whether a given number is prime or not.

**How it works:**

- The function takes a single argument, `n`, which is the number to check.


- It first checks if `n` is less than or equal to 1. If it is, the number is not prime, so the function returns `False`.


- It then enters a loop that iterates through numbers from 2 to the square root of `n`.


- For each number `i`, it checks if `n` is divisible evenly by `i` (i.e., `n % i == 0`).


- If `n` is divisible by `i`, the function returns `False` because `n` cannot be prime if it has a divisor.


- If the loop completes without finding any divisors for `n`, the function returns `True`, indicating that `n` is a prime number.


**Example Usage:**

```python
>>> prime(2)
True
>>> prime(3)
True
>>> prime(4)
False
>>> prime(5)
True
```

**Benefits of this Code:**

- It is a simple and efficient algorithm for finding prime numbers.
- It is widely used in various computer science and mathematical applications.<end_of_turn>
<start_of_turn>user
Great! Now add those explanations as comments in the code.<end_of_turn>
<start_of_turn>model
```python
def prime(n):
    """
    Checks whether a given number is prime or not.

    Args:
        n: The number to check.

    Returns:
        True if n is prime, False otherwise.
    """

    # Check if n is less than or equal to 1.
    if n <= 1:
        return False

    # Iterate through numbers from 2 to the square root of n.
    for i in range(2, int(n**0.5) + 1):
        # Check if n is divisible by i.
        if n % i == 0:
            return False

    # If the loop completes without finding any divisors for n, then n is prime.
    return True
```<end_of_turn>