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app.py
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app.py
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from flask import Flask, render_template, jsonify, request
import json
import numpy as np
from prior import prior
from liklihood import liklihood
from posterior import posterior
import graph_image_generator
app = Flask(__name__)
@app.route('/generatePosteriorDistributionWithObsevation')
def generatePosteriorDistributionWithObsevation():
observation = {}
pos_args_string = request.args.get('pos_args')
observation['pos_args'] = json.loads(pos_args_string)
neg_args_string = request.args.get('neg_args')
observation['neg_args'] = json.loads(neg_args_string)
observation['rating'] = int(request.args.get('rating'))
observation['observationNo'] = int(request.args.get('observationNo'))
attacks_string = request.args.get('attacks')
attacks_raw = json.loads(attacks_string)
observation['attacks'] = [tuple(attack) for attack in attacks_raw]
currentPriorString = request.args.get('currentPrior')
currentPrior = np.array(json.loads(currentPriorString))
# Will need to rebuild all of the important graph space data as this is needed for the liklihood construction
graphSpaceSummaryString = request.args.get('graphSpaceSummary')
graphSpaceSummary = json.loads(graphSpaceSummaryString)
p_G = prior(graphSpaceSummary['pos_args'], graphSpaceSummary['neg_args'])
p_G.rating = graphSpaceSummary['rating'] # This is possibly reckless coding
p_G_T = liklihood(p_G, observation)
liklihood_distribution = p_G_T.buildLiklihoodDistribution()
p_T_G = posterior(currentPrior, liklihood_distribution)
posterior_distribution = p_T_G.buildPosteriorDistribution()
distributions = {}
distributions['liklihood_distribution'] = list(liklihood_distribution)
distributions['posterior_distribution'] = list(posterior_distribution)
return jsonify(distributions)
@app.route('/generateObservationGraph')
def generateObservationGraph():
pos_args_string = request.args.get('pos_args')
pos_args = json.loads(pos_args_string)
neg_args_string = request.args.get('neg_args')
neg_args = json.loads(neg_args_string)
rating = int(request.args.get('rating'))
observationNo = int(request.args.get('observationNo'))
attacks_string = request.args.get('attacks')
attacks = json.loads(attacks_string)
graph_location = graph_image_generator.create_observation_graph(pos_args, neg_args, attacks, observationNo)
return jsonify(graph_location)
@app.route('/generateGraphSpacewithPrior')
def generateGraphSpacewithPrior():
pos_args_string = request.args.get('pos_args')
pos_args = json.loads(pos_args_string)
neg_args_string = request.args.get('neg_args')
neg_args = json.loads(neg_args_string)
rating = int(request.args.get('rating'))
# generate the graph images
p_G = prior(pos_args, neg_args)
graph_image_generator.create_graphs(pos_args, neg_args, p_G.arg_matrices)
# generate the prior distribution
prior_distribution = p_G.getDistribution(rating)
return jsonify(prior_distribution)
@app.route('/generateGraphSpacewithUniformPrior')
def generateGraphSpacewithUniformPrior():
pos_args_string = request.args.get('pos_args')
pos_args = json.loads(pos_args_string)
neg_args_string = request.args.get('neg_args')
neg_args = json.loads(neg_args_string)
rating = int(request.args.get('rating'))
# generate the graph images
p_G = prior(pos_args, neg_args)
graph_image_generator.create_graphs(pos_args, neg_args, p_G.arg_matrices)
# generate the uniform distribution
prior_distribution = [1/len(p_G.arg_matrices)] * len(p_G.arg_matrices)
return jsonify(prior_distribution)
@app.route('/')
def index():
return render_template('index.html')
if __name__ == "__main__":
app.run()