hai i have a problem with my progress bar I want to how my bar like this
However I already got to make the progress bar the issues here when I run test not in the app the it has percentage like this
When I implemented in the streamlit code it does not show the percentage
and here my coding:
import pandas as pd
import streamlit as st
import cleantext
import numpy as np
import re
import nltk
import gensim.utils
import time
nltk.download('wordnet')
nltk.download('stopwords')
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet
from nltk.corpus import stopwords
from gensim.utils import simple_preprocess
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from textblob import TextBlob
from PIL import Image
from streamlit_option_menu import option_menu
im = Image.open(r"C:\Users\Administrator\Documents\UiTM\D3\S6\CSP\image\carat.ico")
st.set_page_config(page_title="FeelTech",page_icon=im,layout="wide")
st.header('Twitter Sentiment Analysis')
st.markdown("##")
df = None
#side bar
st.sidebar.image(r"C:\Users\Administrator\Documents\UiTM\D3\S6\CSP\image\1.jpg",caption="Developed and Maintaned by: Hidayah Athira")
#switcher
st.sidebar.header("Twitter Analysis")
def Home():
with st.expander('Analyze tweets'):
tweets = st.text_input('tweets here: ')
if tweets:
blob = TextBlob(tweets)
st.write('Polarity: ', round(blob.sentiment.polarity,2))
st.write('Subjectivity: ', round(blob.sentiment.subjectivity,2))
pre = st.text_input('Clean tweets: ')
if pre:
st.write(cleantext.clean(pre, clean_all= False, extra_spaces=True ,
stopwords=True ,lowercase=True ,numbers=True , punct=True))
with st.expander('Analyze CSV'):
upl = st.file_uploader('Upload file')
if upl:
df = pd.read_csv(upl)
st.dataframe(df, use_container_width=True)
positive_percentage = 0 # Initialize the variables before the if block
negative_percentage = 0
neutral_percentage = 0
if st.button('Clean Data'):
#convert all tweet into lowercase
df['tweets'] = df['tweets'].str.lower()
#Removing Twitter Handles(@User)
def remove_users(tweets):
remove_user = re.compile(r"@[A-Za-z0-9]+")
return remove_user.sub(r"", tweets)
df['tweets'] = df['tweets'].apply(remove_users)
#Remove links
def remove_links(tweets):
remove_no_link = re.sub(r"http\S+","",tweets)
return remove_no_link
df['tweets'] = df['tweets'].apply(remove_links)
#Remove Punctuations, Numbers, and Special Characters
df['tweets'] = df['tweets'].str.replace("[^a-zA-Z#]", " ")
#Remove short words
df['tweets'] = df['tweets'].apply(lambda x: ' '.join([w for w in x.split() if len(w)>3]))
#Remove hashtag
def remove_hashtags(tweets, pattern):
r = re.findall(pattern, tweets)
for i in r:
tweets = re.sub(i, '', tweets)
return tweets
df['tweets'] = np.vectorize(remove_hashtags)(df['tweets'],"#[\W]*")
#Emoji removal
def remove_emojis(string):
remove_emoji = re.compile("["u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
"]+", flags=re.UNICODE)
return remove_emoji.sub(r'', string)
df['tweets'] = df['tweets'].apply(remove_emojis)
#Lemmatization
lemmatizer = WordNetLemmatizer()
wordnet_map = {"N":wordnet.NOUN, "V":wordnet.VERB, "R":wordnet.ADV}
def lemmatize_words(tweets):
pos_tagged_tweets = nltk.pos_tag(tweets.split())
return " ".join([lemmatizer.lemmatize(word, wordnet_map.get(pos[0], wordnet.NOUN)) for word, pos in pos_tagged_tweets])
#Prepare Stop words
stop_words = stopwords.words('english')
stop_words.extend(['from', 'https', 'twitter', 'still'])
def remove_stopwords(tweets):
return [[word for word in simple_preprocess(str(tweets)) if word not in stop_words]for tweetss in tweets]
df['stop_word'] = remove_stopwords(df['tweets'])
#Tokenize Word
def tokenize(tweets):
for word in tweets:
yield(gensim.utils.simple_preprocess(str(word), deacc=True))
df['token'] = list(tokenize(df['tweets']))
# Function to get sentiment label using TextBlob
def label_sentiment(tweets):
analysis = TextBlob(tweets)
sentiment_score = analysis.sentiment.polarity
if sentiment_score > 0:
return "positive"
elif sentiment_score < 0:
return "negative"
else:
return "neutral"
# Apply the labeling function to your DataFrame
df['sentiment'] = df['tweets'].apply(label_sentiment)
# Calculate percentages
total_samples = len(df)
positive_percentage = (df['sentiment'] == 'positive').sum() / total_samples *100
negative_percentage = (df['sentiment'] == 'negative').sum() / total_samples *100
neutral_percentage = (df['sentiment'] == 'neutral').sum() / total_samples *100
st.dataframe(df, use_container_width=True)
if st.button('Visualize'):
st.markdown("""<style>.stProgress > div > div > div > div { background-image: linear-gradient(to right, #99ff99 , #FFFF00)}</style>""",unsafe_allow_html=True,)
# Create progress bars for each sentiment category
# Display progress bars
st.write("Positive Sentiment:")
bar = st.progress(100)
time.sleep(0.1)
bar.progress(positive_percentage)
st.write(f"{positive_percentage:.2f}%")
st.write("Negative Sentiment:")
bar = st.progress(100)
time.sleep(0.1)
bar.progress(negative_percentage)
st.write(f"{negative_percentage:.2f}%")
st.write("Neutral Sentiment:")
bar = st.progress(100)
time.sleep(0.1)
bar.progress(neutral_percentage)
st.write(f"{neutral_percentage:.2f}%")
def sideBar():
with st.sidebar:
selected=option_menu(
menu_title="Main Menu",
options=["Home","Dashboard"],
icons=["house","eye"],
menu_icon="cast",
default_index=0
)
if selected=="Home":
#st.subheader(f"Page: {selected}")
Home()
sideBar()
can you help me find where is the error with my coding that does not show the progress?
