Revert "Split in app & library"

This reverts commit a3cb5cc4a75e472669bb6f4837e80edaadfb220d.

app.py

@@ -1,10 +1,17 @@
 import copy
+import functools
+import itertools
+import logging
+import random
+import string
+from typing import List, Optional
 
+import requests
+import numpy as np
+import tensorflow as tf
 import streamlit as st
-from rhyme_with_ai.rhyme import query_rhyme_words
-from rhyme_with_ai.rhyme_generator import RhymeGenerator
-from rhyme_with_ai.utils import color_new_words, sanitize
-from transformers import TFAutoModelForMaskedLM, AutoTokenizer
+from gazpacho import Soup, get
+from transformers import AutoTokenizer, TFAutoModelForMaskedLM
 
 
 DEFAULT_QUERY = "Machines will take over the world soon"
@@ -93,6 +100,300 @@ def display_output(status_text, query, current_sentences, previous_sentences):
         query + ",<br>" + "".join(print_sentences), unsafe_allow_html=True
     )
 
+class TokenWeighter:
+    def __init__(self, tokenizer):
+        self.tokenizer_ = tokenizer
+        self.proba = self.get_token_proba()
+
+    def get_token_proba(self):
+        valid_token_mask = self._filter_short_partial(self.tokenizer_.vocab)
+        return valid_token_mask
+
+    def _filter_short_partial(self, vocab):
+        valid_token_ids = [v for k, v in vocab.items() if len(k) > 1 and "#" not in k]
+        is_valid = np.zeros(len(vocab.keys()))
+        is_valid[valid_token_ids] = 1
+        return is_valid
+
+
+class RhymeGenerator:
+    def __init__(
+        self,
+        model: TFAutoModelForMaskedLM,
+        tokenizer: AutoTokenizer,
+        token_weighter: TokenWeighter = None,
+    ):
+        """Generate rhymes.
+
+        Parameters
+        ----------
+        model : Model for masked language modelling
+        tokenizer : Tokenizer for model
+        token_weighter : Class that weighs tokens
+        """
+
+        self.model = model
+        self.tokenizer = tokenizer
+        if token_weighter is None:
+            token_weighter = TokenWeighter(tokenizer)
+        self.token_weighter = token_weighter
+        self._logger = logging.getLogger(__name__)
+
+        self.tokenized_rhymes_ = None
+        self.position_probas_ = None
+
+        # Easy access.
+        self.comma_token_id = self.tokenizer.encode(",", add_special_tokens=False)[0]
+        self.period_token_id = self.tokenizer.encode(".", add_special_tokens=False)[0]
+        self.mask_token_id = self.tokenizer.mask_token_id
+
+    def start(self, query: str, rhyme_words: List[str]) -> None:
+        """Start the sentence generator.
+
+        Parameters
+        ----------
+        query : Seed sentence
+        rhyme_words : Rhyme words for next sentence
+        """
+        # TODO: What if no content?
+        self._logger.info("Got sentence %s", query)
+        tokenized_rhymes = [
+            self._initialize_rhymes(query, rhyme_word) for rhyme_word in rhyme_words
+        ]
+        # Make same length.
+        self.tokenized_rhymes_ = tf.keras.preprocessing.sequence.pad_sequences(
+            tokenized_rhymes, padding="post", value=self.tokenizer.pad_token_id
+        )
+        p = self.tokenized_rhymes_ == self.tokenizer.mask_token_id
+        self.position_probas_ = p / p.sum(1).reshape(-1, 1)
+
+    def _initialize_rhymes(self, query: str, rhyme_word: str) -> List[int]:
+        """Initialize the rhymes.
+
+        * Tokenize input
+        * Append a comma if the sentence does not end in it (might add better predictions as it
+            shows the two sentence parts are related)
+        * Make second line as long as the original
+        * Add a period
+
+        Parameters
+        ----------
+        query : First line
+        rhyme_word : Last word for second line
+
+        Returns
+        -------
+        Tokenized rhyme lines
+        """
+
+        query_token_ids = self.tokenizer.encode(query, add_special_tokens=False)
+        rhyme_word_token_ids = self.tokenizer.encode(
+            rhyme_word, add_special_tokens=False
+        )
+
+        if query_token_ids[-1] != self.comma_token_id:
+            query_token_ids.append(self.comma_token_id)
+
+        magic_correction = len(rhyme_word_token_ids) + 1  # 1 for comma
+        return (
+            query_token_ids
+            + [self.tokenizer.mask_token_id] * (len(query_token_ids) - magic_correction)
+            + rhyme_word_token_ids
+            + [self.period_token_id]
+        )
+
+    def mutate(self):
+        """Mutate the current rhymes.
+
+        Returns
+        -------
+        Mutated rhymes
+        """
+        self.tokenized_rhymes_ = self._mutate(
+            self.tokenized_rhymes_, self.position_probas_, self.token_weighter.proba
+        )
+
+        rhymes = []
+        for i in range(len(self.tokenized_rhymes_)):
+            rhymes.append(
+                self.tokenizer.convert_tokens_to_string(
+                    self.tokenizer.convert_ids_to_tokens(
+                        self.tokenized_rhymes_[i], skip_special_tokens=True
+                    )
+                )
+            )
+        return rhymes
+
+    def _mutate(
+        self,
+        tokenized_rhymes: np.ndarray,
+        position_probas: np.ndarray,
+        token_id_probas: np.ndarray,
+    ) -> np.ndarray:
+
+        replacements = []
+        for i in range(tokenized_rhymes.shape[0]):
+            mask_idx, masked_token_ids = self._mask_token(
+                tokenized_rhymes[i], position_probas[i]
+            )
+            tokenized_rhymes[i] = masked_token_ids
+            replacements.append(mask_idx)
+
+        predictions = self._predict_masked_tokens(tokenized_rhymes)
+
+        for i, token_ids in enumerate(tokenized_rhymes):
+            replace_ix = replacements[i]
+            token_ids[replace_ix] = self._draw_replacement(
+                predictions[i], token_id_probas, replace_ix
+            )
+            tokenized_rhymes[i] = token_ids
+
+        return tokenized_rhymes
+
+    def _mask_token(self, token_ids, position_probas):
+        """Mask line and return index to update."""
+        token_ids = self._mask_repeats(token_ids, position_probas)
+        ix = self._locate_mask(token_ids, position_probas)
+        token_ids[ix] = self.mask_token_id
+        return ix, token_ids
+
+    def _locate_mask(self, token_ids, position_probas):
+        """Update masks or a random token."""
+        if self.mask_token_id in token_ids:
+            # Already masks present, just return the last.
+            # We used to return thee first but this returns worse predictions.
+            return np.where(token_ids == self.tokenizer.mask_token_id)[0][-1]
+        return np.random.choice(range(len(position_probas)), p=position_probas)
+
+    def _mask_repeats(self, token_ids, position_probas):
+        """Repeated tokens are generally of less quality."""
+        repeats = [
+            ii for ii, ids in enumerate(pairwise(token_ids[:-2])) if ids[0] == ids[1]
+        ]
+        for ii in repeats:
+            if position_probas[ii] > 0:
+                token_ids[ii] = self.mask_token_id
+            if position_probas[ii + 1] > 0:
+                token_ids[ii + 1] = self.mask_token_id
+        return token_ids
+
+    def _predict_masked_tokens(self, tokenized_rhymes):
+        return self.model(tf.constant(tokenized_rhymes))[0]
+
+    def _draw_replacement(self, predictions, token_probas, replace_ix):
+        """Get probability, weigh and draw."""
+        # TODO (HG): Can't we softmax when calling the model?
+        probas = tf.nn.softmax(predictions[replace_ix]).numpy() * token_probas
+        probas /= probas.sum()
+        return np.random.choice(range(len(probas)), p=probas)
+
+
+
+def query_rhyme_words(sentence: str, n_rhymes: int, language:str="english") -> List[str]:
+    """Returns a list of rhyme words for a sentence.
+
+    Parameters
+    ----------
+    sentence : Sentence that may end with punctuation
+    n_rhymes : Maximum number of rhymes to return
+
+    Returns
+    -------
+        List[str] -- List of words that rhyme with the final word
+    """
+    last_word = find_last_word(sentence)
+    if language == "english":
+       return query_datamuse_api(last_word, n_rhymes)
+    elif language == "dutch":
+        return mick_rijmwoordenboek(last_word, n_rhymes)
+    else:
+        raise NotImplementedError(f"Unsupported language ({language}) expected 'english' or 'dutch'.")
+
+
+def query_datamuse_api(word: str, n_rhymes: Optional[int] = None) -> List[str]:
+    """Query the DataMuse API.
+
+    Parameters
+    ----------
+    word : Word to rhyme with
+    n_rhymes : Max rhymes to return
+
+    Returns
+    -------
+    Rhyme words
+    """
+    out = requests.get(
+        "https://api.datamuse.com/words", params={"rel_rhy": word}
+    ).json()
+    words = [_["word"] for _ in out]
+    if n_rhymes is None:
+        return words
+    return words[:n_rhymes]
+
+
+@functools.lru_cache(maxsize=128, typed=False)
+def mick_rijmwoordenboek(word: str, n_words: int):
+    url = f"https://rijmwoordenboek.nl/rijm/{word}"
+    html = get(url)
+    soup = Soup(html)
+
+    results = soup.find("div", {"id": "rhymeResultsWords"}).html.split("<br />")
+
+    # clean up
+    results = [r.replace("\n", "").replace(" ", "") for r in results]
+
+    # filter html and empty strings
+    results = [r for r in results if ("<" not in r) and (len(r) > 0)]
+
+    return random.sample(results, min(len(results), n_words))
+
+
+def color_new_words(new: str, old: str, color: str = "#eefa66") -> str:
+    """Color new words in strings with a span."""
+
+    def find_diff(new_, old_):
+        return [ii for ii, (n, o) in enumerate(zip(new_, old_)) if n != o]
+
+    new_words = new.split()
+    old_words = old.split()
+    forward = find_diff(new_words, old_words)
+    backward = find_diff(new_words[::-1], old_words[::-1])
+
+    if not forward or not backward:
+        # No difference
+        return new
+
+    start, end = forward[0], len(new_words) - backward[0]
+    return (
+        " ".join(new_words[:start])
+        + " "
+        + f'<span style="background-color: {color}">'
+        + " ".join(new_words[start:end])
+        + "</span>"
+        + " "
+        + " ".join(new_words[end:])
+    )
+
+
+def find_last_word(s):
+    """Find the last word in a string."""
+    # Note: will break on \n, \r, etc.
+    alpha_only_sentence = "".join([c for c in s if (c.isalpha() or (c == " "))]).strip()
+    return alpha_only_sentence.split()[-1]
+
+
+def pairwise(iterable):
+    """s -> (s0,s1), (s1,s2), (s2, s3), ..."""
+    # https://stackoverflow.com/questions/5434891/iterate-a-list-as-pair-current-next-in-python
+    a, b = itertools.tee(iterable)
+    next(b, None)
+    return zip(a, b)
+
+
+def sanitize(s):
+    """Remove punctuation from a string."""
+    return s.translate(str.maketrans("", "", string.punctuation))
+
 
 if __name__ == "__main__":
     main()

requirements.txt

@@ -2,5 +2,4 @@ gazpacho
 numpy
 requests
 tensorflow
-transformers
--e .
+transformers

setup.cfg

@@ -1,17 +0,0 @@
-[aliases]
-test=pytest
-
-[flake8]
-max-line-length = 88
-
-[tool:pytest]
-addopts = --cov=src --cov-report=xml:test-coverage.xml --nunitxml test-output.xml -vv
-
-[bumpversion]
-current_version = 0.1
-commit = True
-tag = True
-
-[bumpversion:file:setup.py]
-search = version='{current_version}'
-replace = version='{new_version}'

setup.py

@@ -1,58 +0,0 @@
-import os
-from setuptools import setup, find_packages
-
-with open("README.md") as readme_file:
-    readme = readme_file.read()
-
-requirements = [
-    "numpy",
-    "pandas",
-    "requests",
-    "tensorflow",
-    "transformers",
-]
-
-extra_requirements = {
-    "dev": [
-        "black",
-        "bump2version",
-        "coverage",
-        "gazpacho",
-        "twine",
-        "pre-commit",
-        "pylint",
-        "pytest",
-    ]
-}
-
-setup_requirements = ["pytest-runner"]
-
-test_requirements = ["pytest", "pytest-cov", "pytest-nunit"]
-
-BUILD_ID = os.environ.get("BUILD_BUILDID", "0")
-
-setup(
-    author="Rens Dimmendaal & Henk Griffioen",
-    author_email="rensdimmendaal@godatadriven.com",
-    classifiers=[
-        "Development Status :: 2 - Pre-Alpha",
-        "Intended Audience :: Developers",
-        "License :: OSI Approved :: MIT License",
-        "Natural Language :: English",
-        "Programming Language :: Python :: 3.7",
-    ],
-    description="Generate text",
-    install_requires=requirements,
-    extras_require=extra_requirements,
-    long_description=readme,
-    include_package_data=True,
-    keywords="rhyme",
-    name="rhyme_with_ai",
-    packages=find_packages(include=["src"]),
-    package_dir={"": "src"},
-    setup_requires=setup_requirements,
-    test_suite="tests",
-    tests_require=test_requirements,
-    version="0.1" + "." + BUILD_ID,
-    zip_safe=False,
-)

src/rhyme_with_ai/rhyme.py

@@ -1,67 +0,0 @@
-import functools
-import random
-from typing import List, Optional
-
-import requests
-from gazpacho import Soup, get
-
-from rhyme_with_ai.utils import find_last_word
-
-
-def query_rhyme_words(sentence: str, n_rhymes: int, language:str="english") -> List[str]:
-    """Returns a list of rhyme words for a sentence.
-
-    Parameters
-    ----------
-    sentence : Sentence that may end with punctuation
-    n_rhymes : Maximum number of rhymes to return
-
-    Returns
-    -------
-        List[str] -- List of words that rhyme with the final word
-    """
-    last_word = find_last_word(sentence)
-    if language == "english":
-       return query_datamuse_api(last_word, n_rhymes)
-    elif language == "dutch":
-        return mick_rijmwoordenboek(last_word, n_rhymes)
-    else:
-        raise NotImplementedError(f"Unsupported language ({language}) expected 'english' or 'dutch'.")
-
-
-def query_datamuse_api(word: str, n_rhymes: Optional[int] = None) -> List[str]:
-    """Query the DataMuse API.
-
-    Parameters
-    ----------
-    word : Word to rhyme with
-    n_rhymes : Max rhymes to return
-
-    Returns
-    -------
-    Rhyme words
-    """
-    out = requests.get(
-        "https://api.datamuse.com/words", params={"rel_rhy": word}
-    ).json()
-    words = [_["word"] for _ in out]
-    if n_rhymes is None:
-        return words
-    return words[:n_rhymes]
-
-
-@functools.lru_cache(maxsize=128, typed=False)
-def mick_rijmwoordenboek(word: str, n_words: int):
-    url = f"https://rijmwoordenboek.nl/rijm/{word}"
-    html = get(url)
-    soup = Soup(html)
-
-    results = soup.find("div", {"id": "rhymeResultsWords"}).html.split("<br />")
-
-    # clean up
-    results = [r.replace("\n", "").replace(" ", "") for r in results]
-
-    # filter html and empty strings
-    results = [r for r in results if ("<" not in r) and (len(r) > 0)]
-
-    return random.sample(results, min(len(results), n_words))

src/rhyme_with_ai/rhyme_generator.py

@@ -1,181 +0,0 @@
-import logging
-from typing import List
-
-import numpy as np
-import tensorflow as tf
-from transformers import TFAutoModelForMaskedLM, AutoTokenizer
-
-from rhyme_with_ai.utils import pairwise
-from rhyme_with_ai.token_weighter import TokenWeighter
-
-
-class RhymeGenerator:
-    def __init__(
-        self,
-        model: TFAutoModelForMaskedLM,
-        tokenizer: AutoTokenizer,
-        token_weighter: TokenWeighter = None,
-    ):
-        """Generate rhymes.
-
-        Parameters
-        ----------
-        model : Model for masked language modelling
-        tokenizer : Tokenizer for model
-        token_weighter : Class that weighs tokens
-        """
-
-        self.model = model
-        self.tokenizer = tokenizer
-        if token_weighter is None:
-            token_weighter = TokenWeighter(tokenizer)
-        self.token_weighter = token_weighter
-        self._logger = logging.getLogger(__name__)
-
-        self.tokenized_rhymes_ = None
-        self.position_probas_ = None
-
-        # Easy access.
-        self.comma_token_id = self.tokenizer.encode(",", add_special_tokens=False)[0]
-        self.period_token_id = self.tokenizer.encode(".", add_special_tokens=False)[0]
-        self.mask_token_id = self.tokenizer.mask_token_id
-
-    def start(self, query: str, rhyme_words: List[str]) -> None:
-        """Start the sentence generator.
-
-        Parameters
-        ----------
-        query : Seed sentence
-        rhyme_words : Rhyme words for next sentence
-        """
-        # TODO: What if no content?
-        self._logger.info("Got sentence %s", query)
-        tokenized_rhymes = [
-            self._initialize_rhymes(query, rhyme_word) for rhyme_word in rhyme_words
-        ]
-        # Make same length.
-        self.tokenized_rhymes_ = tf.keras.preprocessing.sequence.pad_sequences(
-            tokenized_rhymes, padding="post", value=self.tokenizer.pad_token_id
-        )
-        p = self.tokenized_rhymes_ == self.tokenizer.mask_token_id
-        self.position_probas_ = p / p.sum(1).reshape(-1, 1)
-
-    def _initialize_rhymes(self, query: str, rhyme_word: str) -> List[int]:
-        """Initialize the rhymes.
-
-        * Tokenize input
-        * Append a comma if the sentence does not end in it (might add better predictions as it
-            shows the two sentence parts are related)
-        * Make second line as long as the original
-        * Add a period
-
-        Parameters
-        ----------
-        query : First line
-        rhyme_word : Last word for second line
-
-        Returns
-        -------
-        Tokenized rhyme lines
-        """
-
-        query_token_ids = self.tokenizer.encode(query, add_special_tokens=False)
-        rhyme_word_token_ids = self.tokenizer.encode(
-            rhyme_word, add_special_tokens=False
-        )
-
-        if query_token_ids[-1] != self.comma_token_id:
-            query_token_ids.append(self.comma_token_id)
-
-        magic_correction = len(rhyme_word_token_ids) + 1  # 1 for comma
-        return (
-            query_token_ids
-            + [self.tokenizer.mask_token_id] * (len(query_token_ids) - magic_correction)
-            + rhyme_word_token_ids
-            + [self.period_token_id]
-        )
-
-    def mutate(self):
-        """Mutate the current rhymes.
-
-        Returns
-        -------
-        Mutated rhymes
-        """
-        self.tokenized_rhymes_ = self._mutate(
-            self.tokenized_rhymes_, self.position_probas_, self.token_weighter.proba
-        )
-
-        rhymes = []
-        for i in range(len(self.tokenized_rhymes_)):
-            rhymes.append(
-                self.tokenizer.convert_tokens_to_string(
-                    self.tokenizer.convert_ids_to_tokens(
-                        self.tokenized_rhymes_[i], skip_special_tokens=True
-                    )
-                )
-            )
-        return rhymes
-
-    def _mutate(
-        self,
-        tokenized_rhymes: np.ndarray,
-        position_probas: np.ndarray,
-        token_id_probas: np.ndarray,
-    ) -> np.ndarray:
-
-        replacements = []
-        for i in range(tokenized_rhymes.shape[0]):
-            mask_idx, masked_token_ids = self._mask_token(
-                tokenized_rhymes[i], position_probas[i]
-            )
-            tokenized_rhymes[i] = masked_token_ids
-            replacements.append(mask_idx)
-
-        predictions = self._predict_masked_tokens(tokenized_rhymes)
-
-        for i, token_ids in enumerate(tokenized_rhymes):
-            replace_ix = replacements[i]
-            token_ids[replace_ix] = self._draw_replacement(
-                predictions[i], token_id_probas, replace_ix
-            )
-            tokenized_rhymes[i] = token_ids
-
-        return tokenized_rhymes
-
-    def _mask_token(self, token_ids, position_probas):
-        """Mask line and return index to update."""
-        token_ids = self._mask_repeats(token_ids, position_probas)
-        ix = self._locate_mask(token_ids, position_probas)
-        token_ids[ix] = self.mask_token_id
-        return ix, token_ids
-
-    def _locate_mask(self, token_ids, position_probas):
-        """Update masks or a random token."""
-        if self.mask_token_id in token_ids:
-            # Already masks present, just return the last.
-            # We used to return thee first but this returns worse predictions.
-            return np.where(token_ids == self.tokenizer.mask_token_id)[0][-1]
-        return np.random.choice(range(len(position_probas)), p=position_probas)
-
-    def _mask_repeats(self, token_ids, position_probas):
-        """Repeated tokens are generally of less quality."""
-        repeats = [
-            ii for ii, ids in enumerate(pairwise(token_ids[:-2])) if ids[0] == ids[1]
-        ]
-        for ii in repeats:
-            if position_probas[ii] > 0:
-                token_ids[ii] = self.mask_token_id
-            if position_probas[ii + 1] > 0:
-                token_ids[ii + 1] = self.mask_token_id
-        return token_ids
-
-    def _predict_masked_tokens(self, tokenized_rhymes):
-        return self.model(tf.constant(tokenized_rhymes))[0]
-
-    def _draw_replacement(self, predictions, token_probas, replace_ix):
-        """Get probability, weigh and draw."""
-        # TODO (HG): Can't we softmax when calling the model?
-        probas = tf.nn.softmax(predictions[replace_ix]).numpy() * token_probas
-        probas /= probas.sum()
-        return np.random.choice(range(len(probas)), p=probas)

src/rhyme_with_ai/token_weighter.py

@@ -1,17 +0,0 @@
-import numpy as np
-
-
-class TokenWeighter:
-    def __init__(self, tokenizer):
-        self.tokenizer_ = tokenizer
-        self.proba = self.get_token_proba()
-
-    def get_token_proba(self):
-        valid_token_mask = self._filter_short_partial(self.tokenizer_.vocab)
-        return valid_token_mask
-
-    def _filter_short_partial(self, vocab):
-        valid_token_ids = [v for k, v in vocab.items() if len(k) > 1 and "#" not in k]
-        is_valid = np.zeros(len(vocab.keys()))
-        is_valid[valid_token_ids] = 1
-        return is_valid

src/rhyme_with_ai/utils.py

@@ -1,49 +0,0 @@
-import itertools
-import string
-
-
-def color_new_words(new: str, old: str, color: str = "#eefa66") -> str:
-    """Color new words in strings with a span."""
-
-    def find_diff(new_, old_):
-        return [ii for ii, (n, o) in enumerate(zip(new_, old_)) if n != o]
-
-    new_words = new.split()
-    old_words = old.split()
-    forward = find_diff(new_words, old_words)
-    backward = find_diff(new_words[::-1], old_words[::-1])
-
-    if not forward or not backward:
-        # No difference
-        return new
-
-    start, end = forward[0], len(new_words) - backward[0]
-    return (
-        " ".join(new_words[:start])
-        + " "
-        + f'<span style="background-color: {color}">'
-        + " ".join(new_words[start:end])
-        + "</span>"
-        + " "
-        + " ".join(new_words[end:])
-    )
-
-
-def find_last_word(s):
-    """Find the last word in a string."""
-    # Note: will break on \n, \r, etc.
-    alpha_only_sentence = "".join([c for c in s if (c.isalpha() or (c == " "))]).strip()
-    return alpha_only_sentence.split()[-1]
-
-
-def pairwise(iterable):
-    """s -> (s0,s1), (s1,s2), (s2, s3), ..."""
-    # https://stackoverflow.com/questions/5434891/iterate-a-list-as-pair-current-next-in-python
-    a, b = itertools.tee(iterable)
-    next(b, None)
-    return zip(a, b)
-
-
-def sanitize(s):
-    """Remove punctuation from a string."""
-    return s.translate(str.maketrans("", "", string.punctuation))