Update app.py

app.py

@@ -27,27 +27,6 @@ def get_translator(language):
         return pipeline("translation", model=model_name)
     return None
 
-# Helper function to generate bullet points
-def generate_bullet_points(text):
-    print("Original Text:", text)
-    sentences = sent_tokenize(text)
-    print("Sentences:", sentences)
-    
-    model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
-    embeddings = model.encode(sentences, convert_to_tensor=True)
-    clusters = util.community_detection(embeddings, threshold=0.75)
-    
-    bullet_points = []
-    for cluster in clusters:
-        cluster_sentences = [sentences[idx] for idx in cluster]
-        main_sentence = cluster_sentences[0] if cluster_sentences else ""
-        bullet_points.append(main_sentence.strip())
-    
-    result = "\n".join(f"- {point}" for point in bullet_points)
-    print("Bullet Points:", result)
-    
-    return result
-
 # Helper function to split text into chunks
 def split_text(text, max_tokens=1024):
     inputs = tokenizer(text, return_tensors='pt', truncation=False)
@@ -99,6 +78,20 @@ def process_text(input_text, language):
     print(f"Translated Text: {translated_text}")
     return bullet_points, translated_text
 
+def generate_bullet_points(text):
+    print("Original Text:", text)
+    sentences = sent_tokenize(text)
+    print("Sentences:", sentences)
+    
+    bullet_points = []
+    for sentence in sentences:
+        bullet_points.append(sentence.strip())
+    
+    result = "\n".join(f"- {point}" for point in bullet_points)
+    print("Bullet Points:", result)
+    
+    return result
+
 # Create Gradio interface
 iface = gr.Interface(
     fn=process_text,
@@ -122,3 +115,4 @@ iface.launch()
 
 
 
+