Upload views.py

views.py

@@ -0,0 +1,392 @@
+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')
+
+