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