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NYTimes-homepage-rearranged / app.py

radames HF staff

change logic to enable different feeds

7f708bd over 2 years ago

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3.49 kB

	from flask import Flask, request, jsonify
	from flask_cors import CORS
	import os
	from dotenv import load_dotenv
	from transformers import pipeline
	import feedparser
	import json
	from dateutil import parser
	import re
	load_dotenv()
	# Load Setiment Classifier
	sentiment_analysis = pipeline(
	"sentiment-analysis", model="siebert/sentiment-roberta-large-english")
	app = Flask(__name__, static_url_path='/static')

	CORS(app)


	@app.route('/')
	def index():
	return app.send_static_file('index.html')


	@app.route('/news')
	def get_news():
	feed_url = request.args.get('feed_url')
	# check if string is a valid

	# file name for cache
	file_name = "".join(re.split(r"https://\|\.\|/", feed_url))

	feed_entries = get_feed(feed_url)
	# filter only titles for sentiment analysis
	try:
	with open(f'{file_name}_cache.json') as file:
	cache = json.load(file)
	except:
	cache = {}

	# if new homepage is newer than cache, update cache and return
	print("new date", feed_entries['last_update'])
	print("old date", cache['last_update']
	if 'last_update' in cache else "None")
	if not cache or parser.parse(feed_entries['last_update']) > parser.parse(cache['last_update']):
	print("Updating cache with new preditions")
	titles = [entry['title'] for entry in feed_entries['entries']]
	# run sentiment analysis on titles
	predictions = [sentiment_analysis(sentence) for sentence in titles]
	# parse Negative and Positive, normalize to -1 to 1
	predictions = [-prediction[0]['score'] if prediction[0]['label'] ==
	'NEGATIVE' else prediction[0]['score'] for prediction in predictions]
	# merge rss data with predictions
	entries_predicitons = [{**entry, 'sentiment': prediction}
	for entry, prediction in zip(feed_entries['entries'], predictions)]
	output = {'entries': entries_predicitons,
	'last_update': feed_entries['last_update']}
	# update last precitions cache
	with open(f'{file_name}_cache.json', 'w') as file:
	json.dump(output, file)
	# send back json
	return jsonify(output)
	else:
	print("Returning cached predictions")
	return jsonify(cache)


	@ app.route('/predict', methods=['POST'])
	def predict():
	# get data from POST
	if request.method == 'POST':
	# get current news
	# get post body data
	data = request.get_json()
	if data.get('sentences') is None:
	return jsonify({'error': 'No text provided'})
	# get post expeceted to be under {'sentences': ['text': '...']}
	sentences = data.get('sentences')
	# prencit sentiments
	predictions = [sentiment_analysis(sentence) for sentence in sentences]
	# parse Negative and Positive, normalize to -1 to 1
	predictions = [-prediction[0]['score'] if prediction[0]['label'] ==
	'NEGATIVE' else prediction[0]['score'] for prediction in predictions]
	output = [dict(sentence=sentence, sentiment=prediction)
	for sentence, prediction in zip(sentences, predictions)]
	# send back json
	return jsonify(output)


	def get_feed(feed_url):
	feed = feedparser.parse(feed_url)
	return {'entries': feed['entries'], 'last_update': feed["feed"]['updated']}


	if __name__ == '__main__':
	app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))