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main_file_bjp.py
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main_file_bjp.py
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from tkinter import *
from tkinter import Label
from tkinter import ttk
from tkinter.ttk import *
from tweepy import Stream
from tweepy import OAuthHandler
from tweepy.streaming import StreamListener
from pandas import DataFrame
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from PIL import Image, ImageTk
import tkinter
import nltk
import pickle
import random
import tkinter as tk
import json
import sentiment_mod as s
import matplotlib.pyplot as plt
class Window(Frame):
def __init__(self, master=None):
Frame.__init__(self, master)
load = Image.open("img/background.jpg")
load=load.resize((1100,600),Image.ANTIALIAS)
render = ImageTk.PhotoImage(load)
img = Label(self, image=render)
img.image = render
img.place(x=0, y=0)
self.master = master
self.init_window()
def init_window(self):
self.master.title("Political Affairs Analysis")
self.pack(fill=BOTH, expand=1)
T=Text(height=2, width=15)
T.place(x=720,y=100)
def callback():
def hi():
print("hiii")
win = tk.Toplevel()
win.title("Fetched tweets")
win.geometry("1100x600")
#win.configure(bg='#036CB5')
lod = Image.open("img/background_main.jpg")
lod=lod.resize((1400,1000),Image.ANTIALIAS)
render = ImageTk.PhotoImage(lod)
imag = Label(win,image=render)
imag.image = render
imag.place(x=0, y=0)
menubar = Menu(win)
graphmenu=Menu(menubar)
menubar.add_cascade(label="Graph", menu=graphmenu)
graphmenu.add_command(label="Graph1",font = ('Courier', 14), command=hi)
graphmenu.add_command(label="Graph2",font = ('Courier', 14), command=hi)
win.config(menu=menubar)
def tr():
print("called")
s = tk.Scrollbar(win)
T1 = tkinter.Text(win, height=150, width=100, font=("Courier", 14))
T1.focus_set()
s.pack(side=tk.RIGHT, fill=tk.Y)
T1.pack(fill=tk.Y)
s.config(command=T1.yview)
T1.config(yscrollcommand=s.set)
enc = 'iso-8859-15'
file = open("/text/tweets.txt", 'r', encoding=enc)
data = file.read()
file.close()
print(data)
T1.insert(tk.END,data)
T1.config(state=DISABLED)
def showwBar():
mGui=Tk()
mGui.geometry('470x460')
mGui.title('Comparison')
featuresetss = open("/pickle_files/featuresets.pickle","rb")
featuresets = pickle.load(featuresetss)
featuresetss.close()
random.shuffle(featuresets)
print(len(featuresets))
testing_set = featuresets[250:]
s = ttk.Style()
s.theme_use('clam')
ONB = open("/pickle_files/originalnaivebayes5k.pickle","rb")
classifier = pickle.load(ONB)
ONB.close()
ltex = Label(mGui, background="#e6e6e6", font=("Bookman Old Style", 12), text="Original Naive Bayes accuracy percent:")
ltex.pack()
dab = nltk.classify.accuracy(classifier, testing_set)*100
ltex = Label(mGui,background="#e6e6e6",font=("Courier", 12), text=dab)
ltex.pack()
s.configure("green.Horizontal.TProgressbar", foreground='white', background='green')
mpb = ttk.Progressbar(mGui,style="green.Horizontal.TProgressbar",orient ="horizontal",length = 200, mode ="determinate")
mpb.pack()
mpb["maximum"] = 100
mpb["value"] = dab
Logistic = open("/pickle_files/LogisticRegression_classifier5k.pickle","rb")
LogisticRegression_classifier = pickle.load(Logistic)
Logistic.close()
ltex = Label(mGui,background="#e6e6e6",font=("Bookman Old Style", 12), text="LogisticRegression_classifier accuracy percent:")
ltex.pack()
dab = nltk.classify.accuracy(LogisticRegression_classifier, testing_set)*100
ltex = Label(mGui,background="#e6e6e6",font=("Courier", 12), text=dab)
ltex.pack()
s.configure("purple.Horizontal.TProgressbar", foreground='white', background='purple')
mpb = ttk.Progressbar(mGui,style="purple.Horizontal.TProgressbar",orient ="horizontal",length = 200, mode ="determinate")
mpb.pack()
mpb["maximum"] = 100
mpb["value"] = dab
mGui.mainloop()
def showPie():
mGui=Tk()
mGui.geometry('650x700')
mGui.title('Comparison')
count_positive=0
count_negative=0
count=[]
pullData=open("text/sentiment_value.txt","r").read()
lines=pullData.split("\n")
for l in lines:
if "positive" in l:
count_positive+=1
elif "negative" in l:
count_negative+=1
count.append(count_positive)
count.append(count_negative)
print("Count ",count_positive+count_negative)
data1={'Opinions':['Positive','Negative'],
'No of Responses':[count_positive, count_negative]
}
print(data1)
df=DataFrame(data1,columns=['Opinions','No of Responses'])
df=df[['Opinions','No of Responses']].groupby("Opinions").sum()
print(df)
figure=plt.Figure(figsize=(6,5),dpi=100)
ax1=figure.add_subplot(111)
bar=FigureCanvasTkAgg(figure,mGui)
bar.get_tk_widget().pack(side=tk.LEFT,fill=tk.BOTH)
df.plot(kind='bar',legend=True,ax=ax1)
ax1.set_title("Public opinions")
def showPrediction():
mGui=Tk()
mGui.geometry('650x700')
mGui.title('Prediction')
count_positive=0
count_negative=0
count=[]
pullData=open("text/sentiment_value.txt","r").read()
lines=pullData.split("\n")
for l in lines:
if "positive" in l:
count_positive+=1
elif "negative" in l:
count_negative+=1
count.append(count_positive)
count.append(count_negative)
print("Count ",count_positive+count_negative)
avg_opinions = (count_positive + count_negative) / 2
data1={'Opinions':['Overall Response'],
'No of Responses':[avg_opinions]
}
print(data1)
df=DataFrame(data1,columns=['Opinions','No of Responses'])
df=df[['Opinions','No of Responses']].groupby("Opinions").sum()
print(df)
figure=plt.Figure(figsize=(6,5),dpi=100)
ax1=figure.add_subplot(111)
bar=FigureCanvasTkAgg(figure,mGui)
bar.get_tk_widget().pack(side=tk.LEFT,fill=tk.BOTH)
df.plot(kind='bar',legend=True,ax=ax1)
ax1.set_title("Overall Prediction")
ip=T.get("1.0","end-1c")
B1 = tkinter.Button(win, text ="Show tweets", command=tr)
B1.place(x = 5, y = 5, height=20, width=100)
B2 = tkinter.Button(win, text ="Show Accuracy", command=showwBar)
B2.place(x = 5, y = 35, height=20, width=100)
B3 = tkinter.Button(win, text ="Show Graph", command=showPie)
B3.place(x = 5, y = 55, height=20, width=100)
B4 = tkinter.Button(win, text ="Prediction", command=showPrediction)
B4.place(x = 5, y = 75, height=20, width=100)
ckey="XXXX"
csecret="XXXX"
atoken="XXXX"
asecret="XXXX"
try:
class listener(StreamListener):
tweet_count = 0
def __init__(self, max_tweets):
self.max_tweets = max_tweets
def on_data(self, data):
self.tweet_count += 1
print(self.tweet_count)
print(self.max_tweets)
if self.tweet_count >= self.max_tweets:
return False
all_data=json.loads(data)
tweet=all_data["text"]
char_list = [tweet[j] for j in range(len(tweet)) if ord(tweet[j]) in range(65536)]
tweet=''
for j in char_list:
tweet=tweet+j
sentiment_value, confidence = s.sentiment(tweet)
if confidence *100>=80:
tweetsOutput=open("text/tweets.txt","a")
sentimentOutput=open("text/sentiment_value.txt","a")
sentimentOutput.write(sentiment_value)
sentimentOutput.write("\n")
tweetsOutput.write(sentiment_value)
tweetsOutput.write("\n")
tweetsOutput.write(tweet)
tweetsOutput.write("\n\n")
tweetsOutput.close()
sentimentOutput.close()
print (tweet,sentiment_value,confidence)
return True
def on_error(self, status):
print("In status: ",status)
auth = OAuthHandler(ckey, csecret)
auth.set_access_token(atoken, asecret)
twitterStream = Stream(auth, listener(100))
twitterStream.filter(track=[ip])
except:
return (True)
B = tkinter.Button(text ="Submit",command=callback, bg='#66d9ff')
B.place(x = 850, y = 100, height=40, width=60)
menu = Menu(self.master)
self.master.config(menu=menu)
root = Tk()
root.geometry("1100x600")
app = Window(root)
root.mainloop()