app

View Source

 1from flask import Flask, request, render_template, jsonify
 2from asyncio import new_event_loop
 3from get_tweets import get_tweet
 4app = Flask(__name__, template_folder='../frontend/templates', static_folder='../frontend/static')
 5from ML import NaiveBayes, NeuralNetwork
 6
 7@app.route('/')
 8def index():
 9    """
10    Flask route to return the main search page.
11    """
12    return render_template('index.html')
13
14@app.route('/search')
15def search():
16    """
17    Search page that visualizes the sentiment data of the tweet.
18    """
19    q = request.args.get('query')
20    loop = new_event_loop()
21    tweet = loop.run_until_complete(get_tweet(q))
22    return render_template('search.html', tweet=tweet)
23
24@app.route('/naivebayes', methods=['GET'])
25def naivebayes():
26    """
27    Uses the ML NaiveBayes Module to perform sentiment analysis.
28    Args:
29        query (str) : The url of the tweet
30    Returns:
31        JSON: 'p' or 'n' value dictating sentiment.
32    """
33    data = request.args.get('query')
34    loop = new_event_loop()
35    data = loop.run_until_complete(NaiveBayes.NaiveBayes().predict(data))
36    return jsonify(data)
37
38@app.route('/neuralnetwork', methods=['GET'])
39def neuralnetwork():
40    """
41    Uses the ML Neural Netowrk Module to perform sentiment analysis.
42    Args:
43        query (str) : The url of the tweet
44    Returns:
45        JSON: 'p' or 'n' value dictating sentiment.
46    """
47    data = request.args.get('query')
48    loop = new_event_loop()
49    data = loop.run_until_complete(NeuralNetwork.NeuralNetwork().predict(data))
50    return jsonify(data)
51
52if __name__ == '__main__':
53    app.run(debug=True, port=5000)

app = <Flask 'app'>

@app.route('/')

def index(): View Source

 8@app.route('/')
 9def index():
10    """
11    Flask route to return the main search page.
12    """
13    return render_template('index.html')

Flask route to return the main search page.

@app.route('/search')

def search(): View Source

15@app.route('/search')
16def search():
17    """
18    Search page that visualizes the sentiment data of the tweet.
19    """
20    q = request.args.get('query')
21    loop = new_event_loop()
22    tweet = loop.run_until_complete(get_tweet(q))
23    return render_template('search.html', tweet=tweet)

Search page that visualizes the sentiment data of the tweet.

@app.route('/naivebayes', methods=['GET'])

def naivebayes(): View Source

25@app.route('/naivebayes', methods=['GET'])
26def naivebayes():
27    """
28    Uses the ML NaiveBayes Module to perform sentiment analysis.
29    Args:
30        query (str) : The url of the tweet
31    Returns:
32        JSON: 'p' or 'n' value dictating sentiment.
33    """
34    data = request.args.get('query')
35    loop = new_event_loop()
36    data = loop.run_until_complete(NaiveBayes.NaiveBayes().predict(data))
37    return jsonify(data)

Uses the ML NaiveBayes Module to perform sentiment analysis. Args: query (str) : The url of the tweet Returns: JSON: 'p' or 'n' value dictating sentiment.

@app.route('/neuralnetwork', methods=['GET'])

def neuralnetwork(): View Source

39@app.route('/neuralnetwork', methods=['GET'])
40def neuralnetwork():
41    """
42    Uses the ML Neural Netowrk Module to perform sentiment analysis.
43    Args:
44        query (str) : The url of the tweet
45    Returns:
46        JSON: 'p' or 'n' value dictating sentiment.
47    """
48    data = request.args.get('query')
49    loop = new_event_loop()
50    data = loop.run_until_complete(NeuralNetwork.NeuralNetwork().predict(data))
51    return jsonify(data)

Uses the ML Neural Netowrk Module to perform sentiment analysis. Args: query (str) : The url of the tweet Returns: JSON: 'p' or 'n' value dictating sentiment.