diff --git a/demo/api.py b/demo/api.py
new file mode 100644
index 0000000..8bbd9c2
--- /dev/null
+++ b/demo/api.py
@@ -0,0 +1,127 @@
+from typing import List, Union
+from math import pi
+import argparse
+import json
+import re
+
+from flask import Flask, request, Response, send_from_directory
+from flask_cors import cross_origin
+from tornado.log import enable_pretty_logging
+from tornado.httpserver import HTTPServer
+from tornado.wsgi import WSGIContainer
+from tornado.ioloop import IOLoop
+import tornado.autoreload
+# from tornado import web
+import tornado
+
+import tensorflow as tf
+from tensorflow.keras import models, Model
+from tokenizers import Tokenizer
+
+app = Flask(__name__, static_url_path="", static_folder="./")
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_dir", type=str, help="Directory containing serialized model and tokenizer", required=True)
+args = parser.parse_args()
+
+model: Model = models.load_model(f"{args.model_dir}/saved_model")
+tokenizer: Tokenizer = Tokenizer.from_file(f"{args.model_dir}/tokenizer.json")
+
+
+def responder(results, error, message):
+ """Boilerplate Flask response item.
+
+ Parameters
+ ----------
+ results : dict
+ API response
+ error : int
+ Error code
+ message : str
+ Message to send to the client
+
+ Returns
+ -------
+ flask.Reponse
+ """
+
+ assert isinstance(results, dict)
+ results["message"] = message
+ results = json.dumps(results, indent=2)
+
+ return Response(
+ response=results,
+ status=error,
+ mimetype="application/json"
+ )
+
+
+def tokenize(text: Union[str, List[str]]) -> List[str]:
+ if isinstance(text, str):
+ return [' '.join(tokenizer.encode(text).tokens)]
+ return [' '.join(batch.tokens) for batch in tokenizer.encode_batch(text)]
+
+
+def get_summaries(paragraphs: List[str]):
+ context = tf.concat([
+ model.embed(batch)["attention"]
+ for batch in tf.data.Dataset.from_tensor_slices(paragraphs).batch(32)
+ ], axis=0)
+ doc_vector = model.embed(tf.strings.reduce_join(paragraphs, separator=' ', keepdims=True))["attention"]
+ cosine = tf.tensordot(tf.math.l2_normalize(context, axis=1), tf.math.l2_normalize(doc_vector, axis=1), axes=[-1, 1])
+ cosine = tf.clip_by_value(cosine, -1, 1)
+ likelihoods = tf.nn.softmax(180 - tf.math.acos(cosine) * (180 / pi), axis=0)
+ return likelihoods
+
+
+@app.route("/")
+def root():
+ return send_from_directory(directory="./html/", path="index.html")
+
+
+@app.route("/summarize", methods=["GET", "POST"])
+# @cross_origin(origins=['*'], allow_headers=['Content-Type', 'Authorization'])
+def summarize():
+ if request.is_json:
+ payload = request.json
+ else:
+ payload = request.values
+
+ text = payload.get("text", "")
+ if not text:
+ return responder(results={}, error=400, message="No text provided")
+
+ paragraphs = [p for p in re.split(r"\n{1,}", text) if p.strip()]
+ if len(paragraphs) < 2:
+ return responder(results={"text": paragraphs}, error=400, message="Insufficient amount of text provided")
+
+ tokenized = tokenize(paragraphs)
+ likelihoods = get_summaries(tokenized)
+ likelihoods = tf.squeeze(likelihoods)
+ cond = tf.where(likelihoods > tf.math.reduce_mean(likelihoods) + tf.math.reduce_std(likelihoods)).numpy().flatten()
+ output = [{
+ "text": paragraphs[idx],
+ "score": float(likelihoods[idx])
+ } for idx in cond]
+
+ results = {"data": output}
+ return responder(results=results, error=200, message="Success")
+
+
+def serve(port: int = 9090, debug: bool = False):
+ http_server = HTTPServer(WSGIContainer(app))
+ http_server.listen(port)
+ enable_pretty_logging()
+
+ io_loop = IOLoop.current()
+ if debug:
+ tornado.autoreload.start(check_time=500)
+ print("Listening to port", port, flush=True)
+
+ try:
+ io_loop.start()
+ except KeyboardInterrupt:
+ pass
+
+
+if __name__ == '__main__':
+ serve()
diff --git a/demo/context.html b/demo/html/index.html
similarity index 78%
rename from demo/context.html
rename to demo/html/index.html
index 66b26f7..0c9353f 100644
--- a/demo/context.html
+++ b/demo/html/index.html
@@ -1,14 +1,14 @@
-
+
@@ -17,7 +17,7 @@ Contextual condenser demo
-
+
diff --git a/demo/js/condense.js b/demo/js/condense.js
index 90f56b1..9f39b6d 100644
--- a/demo/js/condense.js
+++ b/demo/js/condense.js
@@ -10,16 +10,19 @@ function formatOutput (ajaxData, container) {
for (var i = 0; i < data.length; i++) {
let text = data[i].text
- let score = data[i].relevanceScore
- let lightness = (1 - score) * 100.0
+ let score = data[i].score
+ let lightness = score * 100.
- outputHtml += `${text}
`
+ if (score > 0.5)
+ outputHtml += `${text}
`
+ else
+ outputHtml += `${text}
`
}
container.append(outputHtml)
}
}
-$(document).ready(function () {
+$(function () {
$('#clear').on('click', function () {
$('.text-input.main').val('')
$('.response-container').empty()
@@ -27,12 +30,12 @@ $(document).ready(function () {
$('#go').on('click', function () {
let text = $('.text-input.main').val()
- let data = { body: text }
+ let data = { text: text }
$('.response-container').empty()
$.ajax({
- url: 'http://localhost:8008/condense',
+ url: '/summarize',
data: data,
type: 'POST',
dataType: 'json',
diff --git a/demo/package.json b/demo/package.json
deleted file mode 100644
index 077b0ac..0000000
--- a/demo/package.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
- "name": "text2vec-demo",
- "version": "1.0.0",
- "description": "Demonstrates the ability of text2vec to condense large bodies of text using the contextual embeddings.",
- "main": "index.js",
- "scripts": {
- "test": "echo \"Error: no test specified\" && exit 1"
- },
- "author": "Dave Hollander",
- "license": "BSD-2-Clause",
- "devDependencies": {
- "eslint": "^5.7.0",
- "eslint-config-standard": "^12.0.0",
- "eslint-plugin-import": "^2.14.0",
- "eslint-plugin-node": "^7.0.1",
- "eslint-plugin-promise": "^4.0.1",
- "eslint-plugin-standard": "^4.0.0"
- }
-}