Spaces:
No application file
No application file
File size: 8,975 Bytes
efb524b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 |
import numpy as np
from django.shortcuts import render, redirect
from django.db.models import Count
from .models import *
import matplotlib.pyplot as plt
# Create your views here.
def home(request):
return render(request, 'index.html')
def alogin(request):
return render(request, 'admin.html')
def ulogin(request):
return render(request, 'user.html')
def usrreg(request):
return render(request, 'signup.html')
def signupaction(request):
email = request.POST['mail']
pwd = request.POST['pwd']
zip = request.POST['zip']
name = request.POST['name']
age = request.POST['age']
gen = request.POST['gen']
d1 = user.objects.filter(email__exact=email).count()
if d1 > 0:
return render(request, 'signup.html', {'msg': "Email ALSTMeady Registered"})
else:
d = user(name=name, email=email, pwd=pwd, zip=zip, gender=gen, age=age)
d.save()
return render(request, 'signup.html', {'msg': "Register Success, You can Login.."})
return render(request, 'signup.html', {'msg': "Register Success, You can Login.."})
def uloginaction(request):
if request.method == 'POST':
uid = request.POST['mail']
pwd = request.POST['pwd']
d = user.objects.filter(email__exact=uid).filter(pwd__exact=pwd).count()
if d > 0:
d = user.objects.filter(email__exact=uid)
request.session['email'] = uid
request.session['name'] = d[0].name
return render(request, 'user_home.html', {'data': d[0]})
else:
return render(request, 'user.html', {'msg': "Login Fail"})
else:
return render(request, 'user.html')
def adminlogindef(request):
if request.method == 'POST':
uid = request.POST['uid']
pwd = request.POST['pwd']
if uid == 'admin' and pwd == 'admin':
request.session['adminid'] = 'admin'
return render(request, 'admin_home.html')
else:
return render(request, 'admin.html', {'msg': "Login Fail"})
else:
return render(request, 'admin.html')
def uhome(request):
if "email" in request.session:
uid = request.session["email"]
d = user.objects.filter(email__exact=uid)
return render(request, 'user_home.html', {'data': d[0]})
else:
return render(request, 'user.html')
def ulogout(request):
try:
del request.session['email']
except:
pass
return render(request, 'user.html')
def adminhome(request):
if "adminid" in request.session:
return render(request, 'admin_home.html')
else:
return render(request, 'user.html')
def trainingpage(request):
return render(request, 'trainingpage.html')
def cnn(request):
from .Train_CNN import dl_evaluation_process
acc, precsn, recall, f1score=dl_evaluation_process()
d=accuracysc.objects.filter(algo='CNN')
d.delete()
d=accuracysc(algo='CNN', accuracyv=acc, prec=precsn, recall=recall, f1sc=f1score)
d.save()
return render(request, 'trainingpage.html', {'msg': "CNN Classifier Training & Testing Completed Successfully"})
def ann(request):
from .Train_ANN import dl_evaluation_process
acc, precsn, recall, f1score=dl_evaluation_process()
d=accuracysc.objects.filter(algo='ANN')
d.delete()
d=accuracysc(algo='ANN', accuracyv=acc, prec=precsn, recall=recall, f1sc=f1score)
d.save()
return render(request, 'trainingpage.html', {'msg': "ANN Classifier Training & Testing Completed Successfully"})
def lstm(request):
from .Train_LSTM import dl_evaluation_process
acc, precsn, recall, f1score=dl_evaluation_process()
d=accuracysc.objects.filter(algo='LSTM')
d.delete()
d=accuracysc(algo='LSTM', accuracyv=acc, prec=precsn, recall=recall, f1sc=f1score)
d.save()
return render(request, 'trainingpage.html', {'msg': "LSTM Classifier Training & Testing Completed Successfully"})
def accuracyview(request):
if "adminid" in request.session:
d = accuracysc.objects.all()
accuracygraph()
precgraph()
recallgraph()
f1graph()
return render(request, 'viewaccuracy.html', {'data': d})
else:
return render(request, 'admin.html')
def viewgraphs(request):
if "adminid" in request.session:
accuracygraph()
precgraph()
recallgraph()
f1graph()
return render(request, 'viewgraph.html')
else:
return render(request, 'admin.html')
def accuracygraph():
if True:
data = {}
row = accuracysc.objects.filter(algo='CNN')
rlist = []
for r in row:
rlist.append(r.accuracyv)
data['CNN']=rlist
row = accuracysc.objects.filter(algo='ANN')
rlist = []
for r in row:
rlist.append(r.accuracyv)
data['ANN']=rlist
row = accuracysc.objects.filter(algo='LSTM')
rlist = []
for r in row:
rlist.append(r.accuracyv)
data['LSTM']=rlist
from .bargraph import bargraph
bargraph.view(data,'acc.jpg', 'Accuracy')
def precgraph():
if True:
data = {}
row = accuracysc.objects.filter(algo='CNN')
rlist = []
for r in row:
rlist.append(r.prec)
data['CNN']=rlist
row = accuracysc.objects.filter(algo='LSTM')
rlist = []
for r in row:
rlist.append(r.prec)
data['LSTM']=rlist
row = accuracysc.objects.filter(algo='ANN')
rlist = []
for r in row:
rlist.append(r.prec)
data['ANN']=rlist
from .bargraph import bargraph
bargraph.view(data,'prec.jpg', 'Precision')
def recallgraph():
if True:
data = {}
row = accuracysc.objects.filter(algo='CNN')
rlist = []
for r in row:
rlist.append(r.recall)
data['CNN']=rlist
row = accuracysc.objects.filter(algo='LSTM')
rlist = []
for r in row:
rlist.append(r.recall)
data['LSTM']=rlist
row = accuracysc.objects.filter(algo='ANN')
rlist = []
for r in row:
rlist.append(r.recall)
data['ANN']=rlist
from .bargraph import bargraph
bargraph.view(data,'recall.jpg', 'Recall')
def f1graph():
if True:
data = {}
row = accuracysc.objects.filter(algo='CNN')
rlist = []
for r in row:
rlist.append(r.f1sc)
data['CNN']=rlist
row = accuracysc.objects.filter(algo='LSTM')
rlist = []
for r in row:
rlist.append(r.f1sc)
data['LSTM']=rlist
row = accuracysc.objects.filter(algo='ANN')
rlist = []
for r in row:
rlist.append(r.f1sc)
data['ANN']=rlist
from .bargraph import bargraph
bargraph.view(data,'f1sc.jpg', 'F1 Score')
def search(request):
import sys,tweepy,re
if request.method=='POST':
keys=request.POST['keys']
from .TweetSearch import TweetSearch
l=TweetSearch.search(keys)
print(l,'><<<<<<<<<<<<<<<<<<<<<<<<')
ii=1
from .LSTM import get_predictions
res=get_predictions(l)
print("res=",res)
t=tweets.objects.all()
t.delete()
print(l)
for l1 in range(len(res)):
try:
r=tweets(sno=ii,tweet=l[l1], sentiment=res[l1])
r.save()
except:
pass
ii=ii+1
data=tweets.objects.all()
print("data=",data)
return render(request, 'tweetsresults.html',{'data':data})
else:
return render(request, 'search.html')
def sentiresults(request):
from .Freq import CountFrequency
from .Graphs import viewg
senti=[]
if "email" in request.session:
data=tweets.objects.all()
for d1 in data:
senti.append(d1.sentiment)
d=CountFrequency(senti)
viewg(d)
return render(request, 'tweetsresults2.html',{'data':data})
else:
return render(request, 'user.html')
def viewgraph2(request):
if "email" in request.session:
from PIL import Image
im = Image.open(r"g1.jpg")
im.show()
return redirect('sentiresults')
|