CodeByAidan
commented
3 months ago Was able to implement the AI integration using a neural network and was able to determine the model accuracy is about 100%, with only a model loss of 0.000099275574%. Includes a test as well, and the TensorFlow model. I wasn't able to finish the NLP or AR part, sorry... Please don't fire me, I'm only a freshman in college... 😞
https://gist.github.com/CodeByAidan/3b0b9d0f83396c8fb042f9c3a0eb5588 (includes model in the gist)
fizzbuzzAI.py
import os
from typing import Any, List, Literal, Union
from matplotlib.pylab import Generator
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.layers import Dense, Embedding, Flatten
from tensorflow.keras.models import Sequential
from word2number import w2n
# Data preparation
def preprocess_input(x: Union[int, float]) -> int:
return abs(int(x))
data: np.ndarray = np.abs(np.arange(-1000, 1001))
labels: np.ndarray = np.where(
data % 15 == 0,
"FizzBuzz",
np.where(data % 3 == 0, "Fizz", np.where(data % 5 == 0, "Buzz", data.astype(str))),
)
# Encode labels
label_encoder: LabelEncoder = LabelEncoder()
integer_encoded: np.ndarray = label_encoder.fit_transform(labels)
onehot_encoded: np.ndarray = tf.keras.utils.to_categorical(integer_encoded)
# Model setup
model: Sequential = Sequential(
[
Embedding(input_dim=2001, output_dim=1024),
Flatten(),
Dense(1024, activation="relu"),
Dense(len(label_encoder.classes_), activation="softmax"),
]
)
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
model_path = "fizzbuzzAI.h5"
if os.path.isfile(model_path):
model: Sequential = tf.keras.models.load_model(model_path)
else:
model.fit(data, onehot_encoded, epochs=50, batch_size=32)
model.save(model_path)
def process_input(
user_input: str,
) -> str | Literal["Invalid input, please enter a number."]:
try:
num: int = int(w2n.word_to_num(user_input))
pred: np.ndarray = model.predict(np.array([preprocess_input(num)]))
return label_encoder.inverse_transform([np.argmax(pred)])[0]
except ValueError:
return "Invalid input, please enter a number."
if __name__ == "__main__":
loss, accuracy = model.evaluate(data, onehot_encoded, verbose=0)
print(f"Model accuracy: {accuracy*100:.12f}%")
print(f"Model loss: {loss:.12f}")
rng: Generator = np.random.default_rng(seed=np.random.SeedSequence(1234))
for _ in range(10):
random_input: np.ndarray[Any, np.dtype[Any]] = np.array(
[rng.integers(-1000, 1001)]
)
preprocessed_input: np.ndarray[Any, np.dtype[Any]] = np.array([preprocess_input(random_input[0])])
predictions = model.predict(preprocessed_input)
predicted_label = label_encoder.inverse_transform([np.argmax(predictions)])[0]
print(f"Input: {random_input[0]} | Prediction: {predicted_label}")test_fizzbuzzAI.py
from typing import Literal
import numpy as np
import pytest
from fizzbuzzAI import label_encoder, model, preprocess_input, process_input
@pytest.mark.parametrize(
"input_value, expected_output",
[
(0, 0),
(1, 1),
(-1, 1),
(1000, 1000),
(-1000, 1000),
],
ids=[
"zero",
"positive_one",
"negative_one",
"positive_boundary",
"negative_boundary",
],
)
def test_preprocess_input(
input_value: Literal[0] | Literal[1] | Literal[-1] | Literal[1000] | Literal[-1000],
expected_output: Literal[0] | Literal[1] | Literal[1000],
) -> None:
result: int = preprocess_input(input_value)
assert result == expected_output
@pytest.mark.parametrize(
"user_input, expected_output",
[
("zero", "FizzBuzz"),
("one", "1"),
("three", "Fizz"),
("five", "Buzz"),
("fifteen", "FizzBuzz"),
("negative fifteen", "FizzBuzz"),
("negative one", "1"),
("one thousand", "Buzz"),
("invalid input", "Invalid input, please enter a number."),
],
ids=[
"zero",
"one",
"three",
"five",
"fifteen",
"negative_fifteen",
"negative_one",
"one_thousand",
"invalid_input",
],
)
def test_process_input(
user_input: (
Literal["zero"]
| Literal["one"]
| Literal["three"]
| Literal["five"]
| Literal["fifteen"]
| Literal["negative fifteen"]
| Literal["negative one"]
| Literal["one thousand"]
| Literal["invalid input"]
),
expected_output: (
Literal["FizzBuzz"]
| Literal["1"]
| Literal["Fizz"]
| Literal["Buzz"]
| Literal["Invalid input, please enter a number."]
),
) -> None:
result: str = process_input(user_input)
assert result == expected_output
@pytest.mark.parametrize(
"data, expected_shape",
[
(np.array([0]), (1, 536)),
(np.array([1, 2, 3]), (3, 536)),
],
ids=[
"single_input",
"multiple_inputs",
],
)
def test_model_prediction_shape(
data: np.ndarray,
expected_shape: tuple[Literal[1], Literal[536]] | tuple[Literal[3], Literal[536]],
) -> None:
preprocessed_data: np.ndarray = np.array([preprocess_input(x) for x in data])
predictions = model.predict(preprocessed_data)
assert predictions.shape == expected_shape
@pytest.mark.parametrize(
"data, expected_output",
[
(np.array([0]), "FizzBuzz"),
(np.array([1]), "1"),
(np.array([3]), "Fizz"),
(np.array([5]), "Buzz"),
(np.array([15]), "FizzBuzz"),
],
ids=[
"zero",
"one",
"three",
"five",
"fifteen",
],
)
def test_model_prediction_output(
data: np.ndarray,
expected_output: (
Literal["FizzBuzz"] | Literal["1"] | Literal["Fizz"] | Literal["Buzz"]
),
) -> None:
preprocessed_data: np.ndarray = np.array([preprocess_input(x) for x in data])
predictions = model.predict(preprocessed_data)
predicted_label = label_encoder.inverse_transform([np.argmax(predictions)])[0]
assert predicted_label == expected_output
if __name__ == "__main__":
pytest.main(["-v", "test_fizzbuzzAI.py"])
tori4582
Dear Esteemed Maintainers,
In light of the rapid advancements in artificial intelligence, vector embeddings, and the general future of computational paradigms, I believe it is time to rethink and revolutionize our beloved FizzBuzz algorithm.
As we move towards a world where AI-driven decision-making becomes the norm, it is imperative that our FizzBuzz implementation keeps up with these technological advancements. Below are some proposed enhancements to bring FizzBuzz into the 22nd century:
I am confident that these enhancements will propel our FizzBuzz implementation into a new era of technological supremacy and ensure that it remains relevant in the ever-evolving landscape of computational innovations.
Thank you for considering these forward-thinking proposals. I eagerly await your thoughts and the inevitable PRs that will follow.
Best regards,
A Visionary Developer