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feat: try to get demo working after 2 years

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nobody 2025-10-30 14:16:04 -07:00
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python3/.gitignore vendored Normal file
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
.venv/
venv/
ENV/
# NLTK Data (uncomment if you want to download on each deployment)
nltk_data/

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uv run flask --app salience run

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[project]
name = "salience"
version = "0.0.0"
description = ""
authors = [
{ name = "Matt Neary", email = "neary.matt@gmail.com" }
]
license = { text = "MIT" }
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"flask>=2.3.2,<3.0.0",
"transformers>=4.30.2,<5.0.0",
"nltk>=3.8.1,<4.0.0",
"sentence-transformers>=2.2.2,<3.0.0",
"numpy>=1.25.0,<2.0.0",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

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from flask import Flask
import numpy as np
from .salience import extract
import json
app = Flask(__name__)
with open('./transcript.txt', 'r') as file:
source_text = file.read().strip()
sentence_ranges, adjacency = extract(source_text)
@app.route("/salience")
def salience_view():
return json.dumps({
'source': source_text,
'intervals': sentence_ranges,
'adjacency': np.nan_to_num(adjacency.numpy()).tolist(),
})

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import numpy as np
import torch
from sentence_transformers import SentenceTransformer
import nltk.data
import nltk
import os
# Set NLTK data path to project directory
PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
NLTK_DATA_DIR = os.path.join(PROJECT_DIR, 'nltk_data')
# Add to NLTK's search path
nltk.data.path.insert(0, NLTK_DATA_DIR)
# Download to the custom location
nltk.download('punkt', download_dir=NLTK_DATA_DIR)
model = SentenceTransformer('all-mpnet-base-v2')
sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
def cos_sim(a, b):
sims = a @ b.T
a_norm = np.linalg.norm(a, axis=-1)
b_norm = np.linalg.norm(b, axis=-1)
a_normalized = (sims.T / a_norm.T).T
sims = a_normalized / b_norm
return sims
def degree_power(A, k):
degrees = np.power(np.array(A.sum(1)), k).ravel()
D = np.diag(degrees)
return D
def normalized_adjacency(A):
normalized_D = degree_power(A, -0.5)
return torch.from_numpy(normalized_D.dot(A).dot(normalized_D))
def get_sentences(source_text):
sentence_ranges = list(sent_detector.span_tokenize(source_text))
sentences = [source_text[start:end] for start, end in sentence_ranges]
return sentences, sentence_ranges
def text_rank(sentences):
vectors = model.encode(sentences)
adjacency = torch.tensor(cos_sim(vectors, vectors)).fill_diagonal_(0.)
adjacency[adjacency < 0] = 0
return normalized_adjacency(adjacency)
def terminal_distr(adjacency, initial=None):
sample = initial if initial is not None else torch.full((adjacency.shape[0],), 1.)
scores = sample.matmul(torch.matrix_power(adjacency, 10)).numpy().tolist()
return scores
def extract(source_text):
sentences, sentence_ranges = get_sentences(source_text)
adjacency = text_rank(sentences)
return sentence_ranges, adjacency
def get_results(sentences, adjacency):
scores = terminal_distr(adjacency)
for score, sentence in sorted(zip(scores, sentences), key=lambda xs: xs[0]):
if score > 1.1:
print('{:0.2f}: {}'.format(score, sentence))

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CTYPE HTML>
<html>
<head>
<meta charset="utf8" />
<title>Salience</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjs/11.8.0/math.js" integrity="sha512-VW8/i4IZkHxdD8OlqNdF7fGn3ba0+lYqag+Uy4cG6BtJ/LIr8t23s/vls70pQ41UasHH0tL57GQfKDApqc9izA==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<style>
body {
display: flex;
flex-direction: column;
height: 100vh;
margin: 0;
}
p {
width: 700px;
margin: 1em auto;
color: #4d4d4d;
font-family: sans-serif;
font-size: 15px;
line-height: 1.33em;
flex: 1;
overflow-y: scroll;
}
h1 {
width: 700px;
text-align: left;
margin: 15px auto;
margin-bottom: 0;
color: #000;
font-family: sans-serif;
font-size: 24px;
}
h1 span {
display: block;
font-size: 0.7em;
font-weight: normal;
color: #a0a0a0;
}
span.sentence {
--salience: 1;
background-color: rgba(249, 239, 104, var(--salience));
}
span.highlight {
background-color: rgb(185, 225, 244);
}
::selection {
background: transparent;
}
</style>
</head>
<body>
<h1>
Salience
<span>automatic sentence highlights based on their significance to the document</span>
</h1>
<p id="content"></p>
<script type="text/javascript">
const content = document.querySelector('#content')
let adjacency = null
function scale(score) {
return Math.max(0, Math.min(1, score ** 3 - 0.95))
}
let exponent = 5
const redraw = () => {
if (!adjacency) return
const sentences = document.querySelectorAll('span.sentence')
if (!window.getSelection().isCollapsed) {
const sel = window.getSelection()
const fromNode = sel.anchorNode.parentNode
const toNode = sel.extentNode.parentNode
const fromIdx = Array.from(sentences).indexOf(fromNode)
const toIdx = Array.from(sentences).indexOf(toNode)
const range = [fromIdx, toIdx]
console.log('range', range)
range.sort((a, b) => a - b)
const vec = adjacency.map((x, i) => (i >= range[0] && i <= range[1]) ? 1 : 0)
const vec_sum = vec.reduce((a, x) => a + x, 0)
const scores = math.multiply(vec, adjacency).map(x => x * adjacency.length / vec_sum)
Array.from(sentences).forEach((node, i) => {
node.style.setProperty('--salience', scale(scores[i]))
if (i >= range[0] && i <= range[1]) node.classList.add('highlight')
else node.classList.remove('highlight')
})
} else {
const initial = adjacency.map(() => 1)
const scores = math.multiply(initial, math.pow(adjacency, exponent))
Array.from(sentences).forEach((node, i) => {
node.style.setProperty('--salience', scale(scores[i]))
node.classList.remove('highlight')
})
}
}
// Disabled functionality to center highlights on a selected fragment
// document.addEventListener('mousemove', redraw)
// document.addEventListener('mouseup', redraw)
fetch('/salience').then(async res => {
const data = await res.json()
console.log(data)
const source = data.source
const intervals = data.intervals
const tokens = intervals.map(([start, end]) => source.substr(start, end - start))
adjacency = data.adjacency
tokens.forEach((t, i) => {
const token = document.createElement('span')
token.innerText = t
token.classList.add('sentence')
content.appendChild(token)
if (tokens[i+1] && intervals[i+1][0] > intervals[i][1]) {
const intervening = document.createElement('span')
const start = intervals[i][1]
intervening.innerText = source.substr(start, intervals[i+1][0] - start)
content.appendChild(intervening)
}
})
redraw()
})
</script>
</body>
</html>

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