Objective
Build an interactive storytelling platform that leverages state-of-the-art generative AI and natural language processing techniques to create engaging, dynamically generated narrative content. Users can interact with the system, steer the storyline, and witness unique narrative branches in real time.
Learning Outcomes
- Understand the process of fine-tuning language models for creative text generation.
- Gain experience in integrating deep learning models with interactive web interfaces.
- Develop skills in using machine learning pipelines for natural language processing tasks.
- Learn methods to evaluate and refine generative outputs for coherence and creativity.
Pre-requisite Skills
- Basic programming in Python
- Familiarity with machine learning concepts and deep learning fundamentals
- Introductory knowledge of natural language processing
- Experience with libraries such as PyTorch and Hugging Face Transformers (beneficial but not mandatory)
Skills Gained
- Fine-tuning pre-trained language models for narrative generation
- Creating interactive applications using web frameworks like Streamlit or Flask
- Data preprocessing for text-based projects
- Evaluating AI-generated text for creativity and consistency
- Integrating generative AI outputs within a user-interactive environment
Tools Explored
- Python (programming language)
- PyTorch & Hugging Face Transformers (for model fine-tuning and inference)
- Streamlit or Flask (for building the interactive front-end)
- Tokenizers and datasets libraries from Hugging Face (for text processing)
- Visualization tools to monitor training metrics (e.g., TensorBoard)
Steps and Tasks
Step 1: Data Acquisition and Preprocessing
Gather a dataset of narrative texts, such as public domain short stories, fairy tales, or creative writing passages. Preprocess the data by cleaning, formatting, and tokenizing to prepare it for model fine-tuning.
# Example: Loading and tokenizing narrative dataset using Hugging Face Tokenizers
from transformers import GPT2Tokenizer
import pandas as pd
# Load dataset (assumed CSV with a column 'text')
df = pd.read_csv('narratives.csv')
texts = df['text'].tolist()
# Initialize tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
tokenized_texts = [tokenizer.encode(text, truncation=True, max_length=512) for text in texts]
# Preview tokenized sample
print(tokenized_texts[0][:50])
Step 2: Fine-Tuning the Language Model
Fine-tune a pre-trained generative model (e.g., GPT-2) on your narrative dataset. Use Hugging Face’s Trainer API to conduct the fine-tuning with custom training arguments.
# Example: Fine-tuning GPT-2 on narrative data using the Trainer API
import torch
from transformers import GPT2LMHeadModel, Trainer, TrainingArguments, DataCollatorForLanguageModeling
from datasets import Dataset
# Prepare dataset
dataset = Dataset.from_dict({'input_ids': tokenized_texts})
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# Load pre-trained GPT-2 model
model = GPT2LMHeadModel.from_pretrained('gpt2')
# Define training arguments
training_args = TrainingArguments(
output_dir='./narrative_model',
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=2,
save_steps=500,
save_total_limit=2,
logging_steps=100,
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
# Start fine-tuning
trainer.train()
Step 3: Developing the Interactive Interface
Create an interactive web interface where users can input story prompts and receive AI-generated narrative continuations. Streamlit is a great tool to rapidly develop this prototype.
# Example: Building a simple interactive interface with Streamlit
import streamlit as st
from transformers import pipeline
# Load fine-tuned model for text generation
generator = pipeline('text-generation', model='./narrative_model', tokenizer=tokenizer)
st.title("Narrative Alchemy")
st.write("Enter a prompt and watch the story unfold!")
user_prompt = st.text_input("Your Prompt:")
if st.button("Generate Story"):
generated_story = generator(user_prompt, max_length=200, num_return_sequences=1)
st.write(generated_story[0]['generated_text'])
Step 4: Enhancing Story Dynamics
Incorporate mechanisms to let users steer the narrative direction by adding choices or allowing for multi-turn dialogue. Connect each turn to the model’s output while preserving context.
# Example: Maintaining context for multi-turn conversations
def interactive_story(prompt, history, generator, max_length=200):
# Combine historical context with the new prompt
full_context = history + "\n" + prompt if history else prompt
generated = generator(full_context, max_length=max_length, num_return_sequences=1)
return generated[0]['generated_text']
# Simulate interaction
history = ""
prompt = "Once upon a time in a mystical forest,"
story_step = interactive_story(prompt, history, generator)
print("Story so far:", story_step)
# Update history for next interaction
history = story_step
Step 5: Evaluation and Deployment
Evaluate the creativity and coherence of the generated narratives through user feedback and automated metrics. Finally, package your application for deployment (e.g., hosting on Heroku, AWS, or any cloud provider).
# Example: Simple evaluation function to check story coherence (placeholder logic)
def evaluate_story(story_text):
# Placeholder: In reality, one might use similarity metrics or human feedback
if len(story_text.split()) > 100:
return "The story is detailed and engaging!"
else:
return "Story may need more content. Consider refining the narrative model."
# Testing evaluation
evaluation_result = evaluate_story(story_step)
print("Evaluation Result:", evaluation_result)
This project not only delves into generative AI with narrative applications but also reinforces integration skills across machine learning, deep learning, generative AI, and natural language processing. Enjoy building your interactive AI-driven narrative generator!