Initial commit

tlm/cli.py

@@ -0,0 +1,66 @@
+import click
+from .model import TinyLanguageModel
+from .helpers import read_mail_text
+
+
+@click.group()
+def cli():
+    """TinyLM - A simple n-gram language model."""
+    pass
+
+
+@cli.command()
+@click.option("--length", default=50, help="Number of words to generate.")
+@click.option("--n", default=2, help="Number of words in the context window.")
+@click.option("--text", type=click.Path(exists=True), multiple=True, help="Text file(s) to use as training corpus. Can be specified multiple times.")
+@click.option("--gutenberg", multiple=True, help="NLTK Gutenberg corpus key(s). Can be specified multiple times.")
+@click.option("--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.")
+@click.option("--mbox", type=click.Path(exists=True), help="Mbox file to use for training.")
+@click.option("--prompt", help="Prompt to start generation.")
+@click.option("--interact", is_flag=True, help="Drop into interactive shell after generating.")
+def generate(length, n, text, gutenberg, list_gutenberg, mbox, prompt, interact):
+    """Generate text using the language model."""
+    import nltk
+
+    # Handle --list-gutenberg: list available keys
+    if list_gutenberg:
+        nltk.download("gutenberg", quiet=True)
+        from nltk.corpus import gutenberg as gutenberg_corpus
+        click.echo("Available Gutenberg corpus keys:")
+        for key in gutenberg_corpus.fileids():
+            click.echo(f"  {key}")
+        return
+
+    # Determine training corpus
+    corpus = []
+
+    if text:
+        for filepath in text:
+            with open(filepath, "r") as f:
+                corpus.extend(f.read().split())
+    if gutenberg:
+        nltk.download("gutenberg", quiet=True)
+        from nltk.corpus import gutenberg as gutenberg_corpus
+        for key in gutenberg:
+            corpus.extend(gutenberg_corpus.words(key))
+    if mbox:
+        mail_text = read_mail_text(mbox)
+        corpus.extend(mail_text.split())
+
+    if not corpus:
+        raise click.UsageError("Must specify at least one of --text, --gutenberg, or --mbox for training data.")
+
+    # Train and generate
+    model = TinyLanguageModel(n=n)
+    model.train(corpus)
+    prompt_words = prompt.split() if prompt else None
+    output = model.generate(length, prompt=prompt_words)
+    click.echo(output)
+
+    if interact:
+        import code
+        code.interact(local=locals(), banner="Entering interactive shell. 'model' and 'output' are available.")
+
+
+if __name__ == "__main__":
+    cli()

tlm/helpers.py

@@ -0,0 +1,63 @@
+import mailbox
+import email
+from email.policy import default
+from tqdm import tqdm
+
+def rolling_window(iterable, n):
+    """Passes a rolling window over the iterable, yielding each n-length tuple.
+    rolling_window(range(5), 3) -> (0, 1, 2), (1, 2, 3), (2, 3, 4)
+    """
+    it = iter(iterable)
+    try:
+        window = [next(it) for _ in range(n)]
+        while True: 
+            yield tuple(window)
+            window = window[1:] + [next(it)]
+    except StopIteration:
+        return
+
+def read_mail_text(mbox_path):
+    """
+    Extract and concatenate all plaintext content from an mbox file.
+    """
+    texts = []
+    mbox = mailbox.mbox(
+        mbox_path,
+        factory=lambda f: email.message_from_binary_file(f, policy=default)
+    )
+    for msg in tqdm(mbox):
+        if msg.is_multipart():
+            for part in msg.walk():
+                if part.get_content_type() == "text/plain":
+                    try:
+                        text = part.get_content()
+                        if text:
+                            texts.append(text.strip())
+                    except Exception:
+                        pass
+        else:
+            if msg.get_content_type() == "text/plain":
+                try:
+                    text = msg.get_content()
+                    if text:
+                        texts.append(text.strip())
+                except Exception:
+                    pass
+    return "\n\n".join(texts)
+
+def clean_corpus(corpus, max_length=10, remove_numbers=False, exclude=None):
+    result = []
+    for word in corpus:
+        if max_length and len(word) > max_length:
+            continue
+        if remove_numbers and word.isnumeric():
+            continue
+        if exclude and word in exclude:
+            continue
+        result.append(word)
+    return result
+
+def join(tokens, punctuation=".,?!:;'\""):
+    "Joins text, but does not give extra space for punctuation"
+    tokens = [t if t in punctuation else ' ' + t for t in tokens]
+    return ''.join(tokens).strip()

tlm/model.py

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+import json
+import random
+from .helpers import rolling_window, join
+
+
+class TinyLanguageModel:
+    def __init__(self, n=2):
+        "Create a new model that looks at n words at a time."
+        self.n = n
+        self.model = {}
+
+    def train(self, words):
+        "Learn word patterns from a list of words."
+        for sequence in rolling_window(words, self.n + 1):
+            pattern = tuple(sequence[:-1])
+            next_word = sequence[-1]
+            if pattern not in self.model:
+                self.model[pattern] = []
+            self.model[pattern].append(next_word)
+                
+    def generate(self, length, prompt=None):
+        "Create new words based on what the model learned."
+        if not self.model:
+            raise Exception("The model has not been trained")
+        output = list(prompt or self.get_random_pattern())
+        while len(output) < length:
+            pattern = tuple(output[-self.n:])
+            if pattern not in self.model:
+                break
+            next_word = random.choice(self.model[pattern])
+            output.append(next_word)
+        return join(output)
+
+    def get_random_pattern(self):
+        "Randomly chooses one of the observed patterns"
+        return random.choice(list(self.model.keys()))
+
+    def save(self, filepath):
+        "Save the model to a file."
+        model_data = {
+            "n": self.n,
+            "model": {" ".join(k): v for k, v in self.model.items()}
+        }
+        with open(filepath, "w") as f:
+            json.dump(model_data, f)
+
+    def load(self, filepath):
+        "Load a model from a file."
+        with open(filepath, "r") as f:
+            data = json.load(f)
+        self.n = data["n"]
+        self.model = {tuple(k.split()): v for k, v in data["model"].items()}
+