Update app.py

app.py

@@ -16,9 +16,9 @@ translation_models = {
 }
 
 # Initialize summarization pipeline with a specified model
-model_name = "sshleifer/distilbart-cnn-12-6"
-summarizer = pipeline("summarization", model=model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+summarization_model = "facebook/bart-large-cnn"
+summarizer = pipeline("summarization", model=summarization_model, device=0)  # Ensure it runs on GPU for better performance
+tokenizer = AutoTokenizer.from_pretrained(summarization_model)
 
 # Initialize translation pipeline
 def get_translator(language):
@@ -78,19 +78,17 @@ 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)
+def generate_bullet_points(summary):
+    print("Summary Text:", summary)
     
-    bullet_points = []
-    for sentence in sentences:
-        bullet_points.append(sentence.strip())
+    # Extract key sentences
+    sentences = sent_tokenize(summary)
+    key_sentences = sentences[:3]  # Extract the first three sentences as key points
     
-    result = "\n".join(f"- {point}" for point in bullet_points)
-    print("Bullet Points:", result)
+    bullet_points = "\n".join(f"- {sentence}" for sentence in key_sentences)
+    print("Bullet Points:", bullet_points)
     
-    return result
+    return bullet_points
 
 # Create Gradio interface
 iface = gr.Interface(
@@ -116,3 +114,4 @@ iface.launch()
 
 
 
+