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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)))
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