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Merge pull request #13 from brainsqueeze/dev
Version 2.0 improvements and deprecated removals
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@@ -7,6 +7,7 @@ | |
| **/*.dev.yml | ||
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| wiki_t2v/ | ||
| multi_news_t2v*/ | ||
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| # JavaScript configs and dependencies | ||
| **/.eslintrc.json | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,124 @@ | ||
| from typing import List, Union | ||
| from math import pi | ||
| import argparse | ||
| import json | ||
| import re | ||
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| from flask import Flask, request, Response, send_from_directory | ||
| 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 | ||
| import tornado | ||
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| import tensorflow as tf | ||
| from tensorflow.keras import models, Model | ||
| from tokenizers import Tokenizer | ||
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| 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() | ||
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| model: Model = models.load_model(f"{args.model_dir}/saved_model") | ||
| tokenizer: Tokenizer = Tokenizer.from_file(f"{args.model_dir}/tokenizer.json") | ||
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| 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 | ||
| """ | ||
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| assert isinstance(results, dict) | ||
| results["message"] = message | ||
| results = json.dumps(results, indent=2) | ||
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| return Response( | ||
| response=results, | ||
| status=error, | ||
| mimetype="application/json" | ||
| ) | ||
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| 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)] | ||
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| 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 | ||
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| @app.route("/") | ||
| def root(): | ||
| return send_from_directory(directory="./html/", path="index.html") | ||
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| @app.route("/summarize", methods=["GET", "POST"]) | ||
| def summarize(): | ||
| if request.is_json: | ||
| payload = request.json | ||
| else: | ||
| payload = request.values | ||
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| text = payload.get("text", "") | ||
| if not text: | ||
| return responder(results={}, error=400, message="No text provided") | ||
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| 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") | ||
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| 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] | ||
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| results = {"data": output} | ||
| return responder(results=results, error=200, message="Success") | ||
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| def serve(port: int = 9090, debug: bool = False): | ||
| http_server = HTTPServer(WSGIContainer(app)) | ||
| http_server.listen(port) | ||
| enable_pretty_logging() | ||
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| io_loop = IOLoop.current() | ||
| if debug: | ||
| tornado.autoreload.start(check_time=500) | ||
| print("Listening to port", port, flush=True) | ||
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| try: | ||
| io_loop.start() | ||
| except KeyboardInterrupt: | ||
| pass | ||
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| if __name__ == '__main__': | ||
| serve() |
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