Update app.py

app.py

@@ -15,6 +15,10 @@ import nltk
 import stanza
 from transformers import BartForConditionalGeneration, BartTokenizer
 from nltk.tokenize import sent_tokenize
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from PIL import Image
+import io
 
 nltk.download("punkt")
 nltk.download("punkt_tab")
@@ -171,6 +175,29 @@ def extract_entities_with_stanza(text, chunk_size=1000):
     return formatted_entities
     return entities
 
+def generate_wordcloud(text):
+    """Generate a word cloud from the given text."""
+    if not text:
+        return None
+    
+    # Generate word cloud
+    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
+    
+    # Convert word cloud to PIL image
+    plt.figure(figsize=(10, 5))
+    plt.imshow(wordcloud, interpolation='bilinear')
+    plt.axis('off')
+    
+    # Save the plot to a BytesIO object
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
+    buf.seek(0)
+    plt.close()
+    
+    # Convert to PIL image
+    image = Image.open(buf)
+    return image
+
 ### 4️⃣ TTS Functionality (KokoroTTS)
 @spaces.GPU(duration=1000)  
 def generate_audio_kokoro(text, lang, selected_voice):
@@ -267,7 +294,8 @@ with gr.Blocks() as demo:
     detected_lang = gr.Textbox(label="Detected Language", visible=False)
     summary_output = gr.Textbox(label="Summary", visible=True, interactive=False)
     full_audio_output = gr.Audio(label="Generated Audio", visible=True)
-    ner_output = gr.Textbox(label="Extracted Entities", visible=True, interactive=False)  # ✅ New output for NER
+    ner_output = gr.Textbox(label="Extracted Entities", visible=False, interactive=False)  
+	wordcloud_output = gr.Image(label="Word Cloud", visible=False)
 
     # Step 1: Fetch Text & Detect Language First
     process_text_button.click(
@@ -276,6 +304,14 @@ with gr.Blocks() as demo:
         
         outputs=[extracted_text, metadata_output, detected_lang, process_summary_button, process_audio_button,process_ner_button, extracted_text, metadata_output]
     )
+	
+	# Automatically generate word cloud when extracted_text changes
+	extracted_text.change(
+        generate_wordcloud,
+        inputs=[extracted_text],
+        outputs=[wordcloud_output],
+        show_progress=False
+    )
     
     process_summary_button.click(hierarchical_summarization, inputs=[extracted_text], outputs=[summary_output])