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app .py
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app .py
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import streamlit as st
#import streamlit.components.v1 as comp
import tweepy
from textblob import TextBlob
from wordcloud import WordCloud, STOPWORDS
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
from PIL import Image
import seaborn as sns
#comp.html("<html><body background="back.jpg"></body></html>")
page_bg_img = '''
<style>
body {
background-color: lightblue;
background-repeat: no-repeat;
background-image: url("http://jaykadam.rf.gd/back.jpg");
background-size: cover;
}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)
consumerKey = "PmpUrUVJJ8enACNHMNmLydg8T"
consumerSecret = "QTRRR1vwJyjAg5ZsXk0hgToMZz3zMG4VoSRwJ3R8ro5wibwSrm"
accessToken = "985205126175404032-tLLZvtgJxSsioklQXORNCqij2XiAEKt"
accessTokenSecret = "yam77qumqgtzzI2xUHsYJJfLz7t9n9WjmUUzq3uMVvCDi"
#Dev. Account: @KADAMJAYV
#Create the authentication object
authenticate = tweepy.OAuthHandler(consumerKey, consumerSecret)
# Set the access token and access token secret
authenticate.set_access_token(accessToken, accessTokenSecret)
# Creating the API object while passing in auth information
api = tweepy.API(authenticate, wait_on_rate_limit = True)
#plt.style.use('fivethirtyeight')
def app():
st.title("News Analyzer from Tweets")
activities=["Tweet Analyzer","Generate Twitter Data","Text analysis"]
choice = st.sidebar.selectbox("Select Your Activity",activities)
if choice=="Tweet Analyzer":
st.subheader("Analyze the tweets of your favourite Personalities")
st.subheader("This tool performs the following tasks :")
st.write("1. Fetches the 5 most recent tweets from the given twitter handel")
st.write("2. Generates a Word Cloud")
st.write("3. Performs Sentiment Analysis a displays it in form of a Bar Graph")
raw_text = st.text_area("Enter the exact twitter handle of the user(without @)")
st.markdown("")
Analyzer_choice = st.selectbox("Select the Activities", ["Show Recent Tweets","Generate WordCloud" ,"Visualize the Sentiment Analysis"])
if st.button("Analyze"):
if Analyzer_choice == "Show Recent Tweets":
st.success("Fetching last 5 Tweets")
def Show_Recent_Tweets(raw_text):
# Extract 100 tweets from the twitter user
posts = api.user_timeline(screen_name=raw_text, count = 100, lang ="en", tweet_mode="extended")
def get_tweets():
l=[]
i=1
for tweet in posts[:5]:
l.append(tweet.full_text)
i= i+1
return l
recent_tweets=get_tweets()
return recent_tweets
recent_tweets= Show_Recent_Tweets(raw_text)
st.write(recent_tweets)
elif Analyzer_choice=="Generate WordCloud":
st.success("Generating Word Cloud")
def gen_wordcloud():
posts = api.user_timeline(screen_name=raw_text, count = 100, lang ="en", tweet_mode="extended")
# Create a dataframe with a column called Tweets
df = pd.DataFrame([tweet.full_text for tweet in posts], columns=['Tweets'])
# word cloud visualization
allWords = ' '.join([twts for twts in df['Tweets']])
stopwords = set(STOPWORDS)
stopwords.update(["https","t","co","RT","S"])
wordCloud = WordCloud(stopwords=stopwords, width=500, height=300, random_state=21, max_font_size=110, background_color="white").generate(allWords)
plt.imshow(wordCloud, interpolation="bilinear")
plt.axis('off')
plt.savefig('WC.jpg')
img= Image.open("WC.jpg")
return img
img=gen_wordcloud()
st.image(img)
else:
def Plot_Analysis():
st.success("Generating Visualisation for Sentiment Analysis")
posts = api.user_timeline(screen_name=raw_text, count = 100, lang ="en", tweet_mode="extended")
df = pd.DataFrame([tweet.full_text for tweet in posts], columns=['Tweets'])
# Create a function to clean the tweets
def cleanTxt(text):
text = re.sub('@[A-Za-z0–9]+', '', text) #Removing @mentions
text = re.sub('#', '', text) # Removing '#' hash tag
text = re.sub('RT[\s]+', '', text) # Removing RT
text = re.sub('https?:\/\/\S+', '', text) # Removing hyperlink
return text
# Clean the tweets
df['Tweets'] = df['Tweets'].apply(cleanTxt)
def getSubjectivity(text):
return TextBlob(text).sentiment.subjectivity
# Create a function to get the polarity
def getPolarity(text):
return TextBlob(text).sentiment.polarity
# Create two new columns 'Subjectivity' & 'Polarity'
df['Subjectivity'] = df['Tweets'].apply(getSubjectivity)
df['Polarity'] = df['Tweets'].apply(getPolarity)
def getAnalysis(score):
if score < 0:
return 'Negative'
elif score == 0:
return 'Neutral'
else:
return 'Positive'
df['Analysis'] = df['Polarity'].apply(getAnalysis)
return df
df= Plot_Analysis()
st.write(sns.countplot(x=df["Analysis"],data=df))
st.set_option('deprecation.showPyplotGlobalUse', False)
st.pyplot(use_container_width=True)
elif choice=="Generate Twitter Data":
st.subheader("This tool fetches the last 100 tweets from the twitter handel & Performs the following tasks")
st.write("1. Converts it into a DataFrame")
st.write("2. Cleans the text")
st.write("3. Analyzes Subjectivity of tweets and adds an additional column for it")
st.write("4. Analyzes Polarity of tweets and adds an additional column for it")
st.write("5. Analyzes Sentiments of tweets and adds an additional column for it")
user_name = st.text_area("Enter the exact twitter handle of the Personality (without @)")
st.markdown("<-------- Also Do checkout the another cool tool from the sidebar")
def get_data(user_name):
posts = api.user_timeline(screen_name=user_name, count = 100, lang ="en", tweet_mode="extended")
df = pd.DataFrame([tweet.full_text for tweet in posts], columns=['Tweets'])
def cleanTxt(text):
text = re.sub('@[A-Za-z0–9]+', '', text) #Removing @mentions
text = re.sub('#', '', text) # Removing '#' hash tag
text = re.sub('RT[\s]+', '', text) # Removing RT
text = re.sub('https?:\/\/\S+', '', text) # Removing hyperlink
return text
# Clean the tweets
df['Tweets'] = df['Tweets'].apply(cleanTxt)
def getSubjectivity(text):
return TextBlob(text).sentiment.subjectivity
# Create a function to get the polarity
def getPolarity(text):
return TextBlob(text).sentiment.polarity
# Create two new columns 'Subjectivity' & 'Polarity'
df['Subjectivity'] = df['Tweets'].apply(getSubjectivity)
df['Polarity'] = df['Tweets'].apply(getPolarity)
def getAnalysis(score):
if score < 0:
return 'Negative'
elif score == 0:
return 'Neutral'
else:
return 'Positive'
df['Analysis'] = df['Polarity'].apply(getAnalysis)
return df
if st.button("Show Data"):
st.success("Fetching Last 100 Tweets")
df=get_data(user_name)
st.write(df)
else:
st.subheader("This tool directly analyzes the text")
content = st.text_area("Enter the text")
def get_data(content):
#posts = api.user_timeline(screen_name=user_name, count = 100, lang ="en", tweet_mode="extended")
df = pd.DataFrame([content], columns=['Tweets'])
def cleanTxt(text):
text = re.sub('@[A-Za-z0–9]+', '', text) #Removing @mentions
text = re.sub('#', '', text) # Removing '#' hash tag
text = re.sub('RT[\s]+', '', text) # Removing RT
text = re.sub('https?:\/\/\S+', '', text) # Removing hyperlink
return text
# Clean the tweets
df['Tweets'] = df['Tweets'].apply(cleanTxt)
def getSubjectivity(text):
return TextBlob(text).sentiment.subjectivity
# Create a function to get the polarity
def getPolarity(text):
return TextBlob(text).sentiment.polarity
# Create two new columns 'Subjectivity' & 'Polarity'
df['Subjectivity'] = df['Tweets'].apply(getSubjectivity)
df['Polarity'] = df['Tweets'].apply(getPolarity)
def getAnalysis(score):
if score < 0:
return 'Negative'
elif score == 0:
return 'Neutral'
else:
return 'Positive'
df['Analysis'] = df['Polarity'].apply(getAnalysis)
return df
if st.button("Show Data"):
st.success("Analyzing your text")
df=get_data(content)
st.write(df)
st.subheader('ArtificialMinds from GDEC ||| Smart Gujarat for New India Hackathon')
if __name__ == "__main__":
app()