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from umap_reducer import UMAPReducer
from flask import Flask, request, render_template, jsonify, make_response
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
import numpy as np
import gzip
load_dotenv()
# Load Setiment Classifier
# sentiment_analysis = pipeline(
# "sentiment-analysis", model="siebert/sentiment-roberta-large-english")
app = Flask(__name__, static_url_path='/static')
reducer = UMAPReducer()
CORS(app)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/run-umap') # //methods=['POST'])
def run_umap():
data = np.random.rand(512, 4)
# UMAP embeddings
embeddings = reducer.embed(data)
content = gzip.compress(json.dumps(embeddings.tolist()).encode('utf8'), 5)
response = make_response(content)
response.headers['Content-length'] = len(content)
response.headers['Content-Encoding'] = 'gzip'
return response
# @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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