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import os
os.environ["STREAMLIT_NO_ALT"] = "true"
import streamlit as st
import matplotlib.pyplot as plt
# Install dependencies
st.write("Installing dependencies...")
streamlit_deps = """
streamlit
textblob
vadersentiment
flair
matplotlib
""".strip().split('\n')
for lib in streamlit_deps:
os.system(f"pip install {lib}")
# Now you can import them
from textblob import TextBlob
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from flair.models import TextClassifier
from flair.data import Sentence
# Function to perform sentiment analysis using TextBlob model
def textblob_sentiment(text):
blob = TextBlob(text)
return blob.sentiment.polarity
# Function to perform sentiment analysis using VADER model
def vader_sentiment(text):
analyzer = SentimentIntensityAnalyzer()
scores = analyzer.polarity_scores(text)
return scores['compound']
# Function to perform sentiment analysis using Flair model
def flair_sentiment(text):
classifier = TextClassifier.load('en-sentiment')
sentence = Sentence(text)
classifier.predict(sentence)
if len(sentence.labels) > 0:
if sentence.labels[0].value == 'POSITIVE':
return 1.0
elif sentence.labels[0].value == 'NEGATIVE':
return -1.0
return 0.0
# Set up the Streamlit app
st.title('Sentiment Analysis App')
# Get user input
text = st.text_input('Enter text to analyze')
# Perform sentiment analysis using each model
textblob_score = textblob_sentiment(text)
vader_score = vader_sentiment(text)
flair_score = flair_sentiment(text)
# Display the sentiment scores
st.write('TextBlob score:', textblob_score)
st.write('VADER score:', vader_score)
st.write('Flair score:', flair_score)
# Create a graph of the sentiment scores
fig, ax = plt.subplots()
ax.bar(['TextBlob', 'VADER', 'Flair'], [textblob_score, vader_score, flair_score])
ax.axhline(y=0, color='gray', linestyle='--')
ax.set_title('Sentiment Scores')
st.pyplot(fig)
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