Create app.py

app.py

@@ -1,52 +0,0 @@
-import streamlit as st
-from textblob import TextBlob
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-from flair.models import TextClassifier
-from flair.data import Sentence
-import matplotlib.pyplot as plt
-
-# 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 sentence.labels[0].value == 'POSITIVE':
-        return 1.0
-    elif sentence.labels[0].value == 'NEGATIVE':
-        return -1.0
-    else:
-        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)