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bioWeb.py
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bioWeb.py
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"""
@author__ = "Juan Francisco Illan"
@license__ = "GPL"
@version__ = "1.0.1"
@email__ = "juanfrancisco.illan@gmail.com"
"""
from flask import Flask, request, render_template
from flask_debugtoolbar import DebugToolbarExtension
import sqlite3
import matplotlib.pyplot as plt
import random
from classes import *
from blast_process import *
from neuronal_process import *
app = Flask(__name__)
app.config['SECRET_KEY'] = "lailolailo"
app.debug = True
toolbar = DebugToolbarExtension(app)
# Controller to index
@app.route('/')
def home():
return render_template('index.html')
# Controller to blast access
@app.route('/blast_nucleotide', methods=["GET"])
def blast_nucleotide():
cb = ConfigBlast()
return render_template('blast_nucleotide.html',configBlast=cb)
# Controller to blast request
@app.route('/blast_nucleotide', methods=["POST"])
def blast_nucleotide_post():
# valores indicados en el formulario
cb = ConfigBlast()
cb.querry_seq = request.form['querry_seq'] #The querry sequence we search for
cb.k = int(request.form['k']) #Word length
cb.match_score = int(request.form['match_score']) #Score added on match occurunce in alignment
cb.mismatch_score = int(request.form['mismatch_score']) #Score added on mismatch occurunce in alignment
cb.gap_score = int(request.form['gap_score']) #Score added on gap occurunce in alignment
cb.seed_threshold = int(request.form['seed_threshold']) #The minimum score to considered a seed
#cb.mode = int(request.form['mode']) # Mode of execution
cb.num_secuences = int(request.form['num_secuences']) # Num of secuences to read from bd
# read sequences in database
db_sequences = read_data('database/db_seq.csv')
# execute blast
blastResult = blast_execute(cb,db_sequences[:cb.num_secuences+1])
# consultamos ahora todas las stadisticas para mostrar un resumen de las ejeciones realizadas
lse = get_statistic_execution('')
listSeedTimes = []
listExtendsTimes = []
for se in lse:
listSeedTimes.append(se.time_seed)
listExtendsTimes.append(se.time_extends)
dataStats = {'seedTimes':listSeedTimes[len(listSeedTimes)-15:],
'extendsTimes':listExtendsTimes[len(listExtendsTimes)-15:]}
hash = random.getrandbits(32)
plt.figure()
df = pd.DataFrame(dataStats)
df.plot()
plt.xlabel('Nº of execution (last 15 execution)')
plt.ylabel('Time of execution (sg.)')
plt.savefig('static/images/temp/plot_' + str(hash) + '.png')
plt.close()
# response html with blastResult
return render_template('blast_nucleotide.html', configBlast=cb, blastResult=blastResult, listStatisticExecution=lse, plotImage='plot_' + str(hash) + '.png')
# Controller to blast access
@app.route('/blast_protein', methods=["GET"])
def blast_protein():
cbp = ConfigBlastProtein()
return render_template('blast_protein.html',configBlastProtein=cbp)
# Controller to blastProtein request
@app.route('/blast_protein', methods=["POST"])
def blast_protein_post():
# valores indicados en el formulario
cbp = ConfigBlastProtein()
cbp.querry_seq = request.form['querry_seq'] #The querry sequence we search for
cbp.mode = int(request.form['mode']) # Mode of execution
if (app.countVectorizer == None or app.classifierMNB == None) :
seq_data = pd.read_csv('database/db_seq.csv', sep=';', engine='python')
app.countVectorizer, app.classifierMNB, app.statsMNB = createClassifierMNB(seq_data)
## mode process the secuences
blastResultProtein, statsPrediction = clasiffierMNB(cbp, app.countVectorizer, app.classifierMNB)
clasiffierStats = app.classifierMNB
# response html with blastResult
return render_template('blast_protein.html', configBlastProtein=cbp, blastResultProtein=blastResultProtein, statsMNB=app.statsMNB, statsPrediction=statsPrediction)
# Controller to blast access
@app.route('/blast_pathogen', methods=["GET"])
def blast_pathogen():
cbp = ConfigBlastPathogen()
return render_template('blast_pathogen.html',configBlastPathogen=cbp)
# Controller to blastProtein request
@app.route('/blast_pathogen', methods=["POST"])
def blast_pathogen_post():
# valores indicados en el formulario
cbp = ConfigBlastPathogen()
cbp.querry_seq = request.form['querry_seq'] #The querry sequence we search for
cbp.mode = int(request.form['mode']) # Mode of execution
if (app.tokenizerLSTM == None or app.modelLSTM == None) :
seq_data = pd.read_csv('database/pathogen_sars_cov_2.csv', sep=';', engine='python')
app.tokenizerLSTM, app.modelLSTM , app.statsLSTM, app.shortModelSummary = createClasiffierLSTM(seq_data)
## mode process the secuences
blastResultPathogen, statsPrediction = clasiffierLSTM(cbp, app.tokenizerLSTM, app.modelLSTM)
# response html with blastResult
return render_template('blast_pathogen.html', configBlastPathogen=cbp, blastResultPathogen=blastResultPathogen, statsLSTM=app.statsLSTM, shortModelSummary = app.shortModelSummary, statsPrediction=statsPrediction)
# Controller to blast request
@app.route('/openAxisPlot', methods=["POST"])
def openAxisPlot():
axisPlot = request.form['AxisPlot'] # The mode plot to generate
filterPlot = request.form['filterPlot'] # The filter for plot generate
# consultamos ahora todas las stadisticas para mostrar un resumen de las ejeciones realizadas
lse = get_statistic_execution(filterPlot)
listSeedTimes = []
listExtendsTimes = []
listLenQuery = []
listKValue = []
listNumSequences = []
listMode = []
for se in lse:
listSeedTimes.append(se.time_seed)
listExtendsTimes.append(se.time_extends)
listNumSequences.append(se.numSecuencesDb)
listLenQuery.append(len(se.querry_seq))
listKValue.append(se.k)
listMode.append(se.mode)
if int(axisPlot)==1: # 1 - creation_date
dataStats = {'seedTimes':listSeedTimes,
'extendsTimes':listExtendsTimes}
df = pd.DataFrame(dataStats)
df.plot() # x ='time_seed', y='time_extends', kind = 'scatter'
plt.xlabel('Nº of execution')
plt.ylabel('Time of execution (sg.)')
elif int(axisPlot)==2: # 2 - length(querry_seq)
f = plt.figure()
f, axes = plt.subplots(nrows = 1, ncols = 2)
axes[0].scatter(listLenQuery, listSeedTimes, marker = "x", color='blue')
axes[0].set_xlabel('Length(query) of execution')
axes[0].set_ylabel('Time of execution SeedTimes (sg.)')
axes[1].scatter(listLenQuery, listExtendsTimes, marker = 'x', color='red')
axes[1].set_xlabel('Length(query) of execution')
axes[1].set_ylabel('Time of execution ExtendsTimes (sg.)')
axes[1].yaxis.set_label_position("right")
elif int(axisPlot)==3: # 3 - k
f = plt.figure()
f, axes = plt.subplots(nrows = 1, ncols = 2)
axes[0].scatter(listKValue, listSeedTimes, marker = "x", color='blue')
axes[0].set_xlabel('K-parameter of execution')
axes[0].set_ylabel('Time of execution SeedTimes (sg.)')
axes[1].scatter(listKValue, listExtendsTimes, marker = 'x', color='red')
axes[1].set_xlabel('K-parameter of execution')
axes[1].set_ylabel('Time of execution ExtendsTimes (sg.)')
axes[1].yaxis.set_label_position("right")
elif int(axisPlot)==4: # 4 - mode
f = plt.figure()
f, axes = plt.subplots(nrows = 1, ncols = 2)
axes[0].scatter(listMode, listSeedTimes, marker = "x", color='blue')
axes[0].set_xlabel('Mode of execution')
axes[0].set_ylabel('Time of execution SeedTimes (sg.)')
axes[1].scatter(listMode, listExtendsTimes, marker = 'x', color='red')
axes[1].set_xlabel('Mode of execution')
axes[1].set_ylabel('Time of execution ExtendsTimes (sg.)')
axes[1].yaxis.set_label_position("right")
elif int(axisPlot)==5: # 5 - numSecuencesDb
f = plt.figure()
f, axes = plt.subplots(nrows = 1, ncols = 2)
axes[0].scatter(listNumSequences, listSeedTimes, marker = "x", color='blue')
axes[0].set_xlabel('NumSequences in db')
axes[0].set_ylabel('Time of execution SeedTimes (sg.)')
axes[1].scatter(listNumSequences, listExtendsTimes, marker = 'x', color='red')
axes[1].set_xlabel('NumSequences in db')
axes[1].set_ylabel('Time of execution ExtendsTimes (sg.)')
axes[1].yaxis.set_label_position("right")
hash = random.getrandbits(32)
plt.savefig('static/images/temp/plot_' + str(hash) + '.png')
plt.close()
# response html with plotimage
return render_template('plot.html',plotImage='plot_' + str(hash) + '.png', filterPlot=filterPlot)
# Controller to blast access
@app.route('/about', methods=["GET"])
def about():
return render_template('about.html')
# startup HTTP web service
if __name__ == '__main__':
print("Running service ...")
app.classifierMNB = None
app.countVectorizer = None
app.tokenizerLSTM = None
app.modelLSTM = None
app.run(host='0.0.0.0')