Update app.py

app.py

@@ -7,8 +7,13 @@ device = 0 if torch.cuda.is_available() else -1
 text_summary = pipeline("summarization", model="Falconsai/text_summarization", device=device, torch_dtype=torch.bfloat16)
 
 def summary(input):
-    # Adjust max_length and min_length for better summarization
-    output = text_summary(input, max_length=200, min_length=50, truncation=True)
+    # Calculate the number of tokens based on input length
+    input_length = len(input.split())
+    max_output_tokens = max(20, input_length // 2)  # Ensure output is less than half of the input
+    min_output_tokens = max(10, input_length // 4)  # Ensure a meaningful summary
+
+    # Generate summary with dynamic token limits
+    output = text_summary(input, max_length=max_output_tokens, min_length=min_output_tokens, truncation=True)
     return output[0]['summary_text']
 
 gr.close_all()
@@ -24,3 +29,4 @@ demo = gr.Interface(
 
 demo.launch()
 
+