Generative AI-Powered Storyteller: A Python Project for Creative Writing
1. Objective
The objective of this project is to build a generative AI storyteller using Python. By employing deep learning techniques and natural language processing (NLP), this project will allow users to generate creative stories based on prompts. Users can input a seed sentence or topic, and the model will generate a coherent and contextually relevant narrative. This project will deepen your understanding of machine learning workflows, text generation using AI, and the application of Python in creative avenues.
2. Learning Outcomes
By the end of this project, you will be able to:
- Implement a generative model using deep learning techniques.
- Utilize Python libraries for data processing and model training.
- Understand the architecture and functionality of language models.
- Create an interactive user interface for generating stories.
- Evaluate the qualitative aspects of generated outputs.
3. Pre-requisite Skills
- Basic Python Programming: Familiarity with Python syntax and basic programming concepts.
- Introduction to Machine Learning: Understanding basic machine learning concepts and workflows.
- Deep Learning Fundamentals: Knowledge of neural networks and deep learning frameworks (particularly TensorFlow or PyTorch).
- NLP Basics: Understanding text processing and tokenization techniques.
4. Skills Gained
- Mastery over generative adversarial networks (GANs) or recurrent neural networks (RNNs) for text generation.
- Proficiency in utilizing NLP libraries such as NLTK or SpaCy.
- Experience with training, validating, and fine-tuning deep learning models.
- Skills in building user-friendly interfaces using Python.
- Insights into user experience design in AI-driven applications.
5. Tools Explored
- Python: The primary programming language for this project.
- TensorFlow/PyTorch: Deep learning frameworks for building and training the model.
- NLTK/SpaCy: Libraries for natural language processing.
- Flask: A web framework for creating the user interface.
- Jupyter Notebook: For interactive prototyping and visualization of results.
6. Steps and Tasks
Step 1: Setting Up the Environment
To get started, we need to set up our working environment and install the necessary libraries.
Task
- Install the required libraries using pip.
pip install tensorflow nltk flask
- Download necessary NLTK datasets.
import nltk
nltk.download('punkt')
nltk.download('gutenberg')
Step 2: Data Collection and Pre-processing
For training our generative model, we need a dataset. A great choice might be a collection of literary works, such as those available from Project Gutenberg.
Task
- Collect and preprocess the dataset.
from nltk.corpus import gutenberg
import numpy as np
# Load the dataset and prepare a list of texts
texts = [gutenberg.raw(fileid) for fileid in gutenberg.fileids()]
# Combine texts
corpus = ' '.join(texts)
# Tokenize the corpus into sentences
from nltk.tokenize import sent_tokenize
sentences = sent_tokenize(corpus)
print(f"Number of sentences: {len(sentences)}")
Step 3: Text Vectorization
Convert the text data into a format suitable for model training.
Task
- Tokenization and word embedding using TensorFlow/Keras.
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Tokenize the sentences
tokenizer = Tokenizer()
tokenizer.fit_on_texts(sentences)
total_words = len(tokenizer.word_index) + 1
# Prepare input sequences
input_sequences = []
for line in sentences:
token_list = tokenizer.texts_to_sequences([line])[0]
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i + 1]
input_sequences.append(n_gram_sequence)
# Pad sequences
max_sequence_length = max(len(x) for x in input_sequences)
input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')
Step 4: Creating the Model
Build and compile a Sequential model for text generation.
Task
- Define and compile the model architecture.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
model = Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_length - 1))
model.add(LSTM(150, return_sequences=True))
model.add(LSTM(150))
model.add(Dense(total_words, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
Step 5: Training the Model
Train the model on the preprocessed data.
Task
- Train the LSTM model and evaluate its performance.
X, y = input_sequences[:, :-1], input_sequences[:, -1]
from tensorflow.keras.utils import to_categorical
y = to_categorical(y, num_classes=total_words)
model.fit(X, y, epochs=50, batch_size=64)
Step 6: Generating Text
After training, you can generate new story content based on a prompt.
Task
- Create a function to generate text from the model.
def generate_text(seed_text, next_words, model, max_sequence_length):
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_length - 1, padding='pre')
predicted = model.predict(token_list, verbose=0)
predicted_word_index = np.argmax(predicted, axis=-1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted_word_index:
output_word = word
break
seed_text += " " + output_word
return seed_text
print(generate_text("Once upon a time", 20, model, max_sequence_length))
Step 7: Building the User Interface
Create a simple web application using Flask for user interaction.
Task
- Set up the Flask application for story generation.
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html') # create an index.html with a form
@app.route('/generate', methods=['POST'])
def generate():
seed_text = request.form['seed_text']
generated_story = generate_text(seed_text, 20, model, max_sequence_length)
return render_template('index.html', generated_story=generated_story)
if __name__ == "__main__":
app.run(debug=True)
Conclusion
This project integrates multiple domains such as natural language processing and deep learning to create a generative AI system capable of storytelling. You can further enhance the project by experimenting with different architectures, incorporating user feedback, and implementing a better user interface. Engage with your community, get feedback, and continue to refine your approach to generative AI!