Create app.py

app.py

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+import gradio as gr
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Download required NLTK resources
+nltk.download('vader_lexicon')
+
+# Load the pre-trained sentiment intensity analyzer
+sia = SentimentIntensityAnalyzer()
+
+def get_sentiment(tweet):
+    # Preprocess the tweet
+    processed_tweet = preprocess([tweet])
+    
+    # Get the sentiment score using VADER sentiment analyzer
+    sentiment_score = sia.polarity_scores(processed_tweet[0])
+    
+    # Determine the sentiment label based on the compound score
+    compound_score = sentiment_score['compound']
+    if compound_score >= 0.05:
+        sentiment = 'Positive'
+    elif compound_score <= -0.05:
+        sentiment = 'Negative'
+    else:
+        sentiment = 'Neutral'
+    
+    return sentiment
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=get_sentiment,
+    inputs='text',
+    outputs='text',
+    title='Tweet Sentiment Analyzer',
+    description='Enter a tweet and get the sentiment prediction.',
+    examples=[['I love this movie!', 'This weather is terrible.']],
+    theme='default'
+)
+
+# Launch the interface
+iface.launch()