Create app.py

app.py

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+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)