Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import nltk
+import matplotlib.pyplot as plt
 from nltk.sentiment.vader import SentimentIntensityAnalyzer
 
 nltk.download("vader_lexicon")
@@ -8,14 +9,27 @@ sid = SentimentIntensityAnalyzer()
 def sentiment_analysis(text):
     scores = sid.polarity_scores(text)
     del scores["compound"]
-    return scores
+    
+    labels = list(scores.keys())
+    values = list(scores.values())
+    colors = ['red' if label == 'neg' else 'green' if label == 'pos' else 'white' for label in labels]
+    
+    plt.figure(figsize=(5, 3))
+    plt.bar(labels, values, color=colors)
+    plt.ylim(0, 1)
+    plt.title("Sentiment Analysis")
+    plt.xlabel("Sentiment")
+    plt.ylabel("Score")
+    
+    plt.savefig('sentiment_chart.png')
+    return gr.Image('sentiment_chart.png')
 
 demo = gr.Interface(
     fn=sentiment_analysis,
     inputs=gr.Textbox(lines=5, placeholder="Enter a positive or negative sentence here..."),
-    outputs=gr.JSON(),
+    outputs="image",
     title="Sentiment Analysis",
-    description="Enter a sentence to analyze its sentiment. The model will return the sentiment scores for positive, negative, and neutral tones.",
+    description="Enter a sentence to analyze its sentiment. The model will return the sentiment scores for positive, negative, and neutral tones as a bar chart.",
     examples=[["This is wonderful!"], ["I hate this!"], ["It's okay, not bad."]]
 )