Update app.py

app.py

@@ -2,14 +2,12 @@ import torch
 import gradio as gr
 from transformers import pipeline
 from transformers import T5ForConditionalGeneration, T5Tokenizer
-model_path='Sibinraj/T5-finetuned-dialogue_sumxx'
+
+model_path = 'Sibinraj/T5-finetuned-dialogue_sumxx'
 model = T5ForConditionalGeneration.from_pretrained(model_path)
 tokenizer = T5Tokenizer.from_pretrained(model_path)
 
-
-
-def summarize_text(text,max_length):
-    print(max_length)
+def summarize_text(text, max_length, show_length):
     # Preprocess the text
     inputs = tokenizer.encode(
         "summarize: " + text,
@@ -19,27 +17,30 @@ def summarize_text(text,max_length):
         padding='max_length'
     )
     
-    # Validate max_length
-    # if not isinstance(max_length, int) or max_length <= 0:
-    #     max_length = 50
-    
     # Generate the summary
     summary_ids = model.generate(
         inputs,
-        max_length=max_length+2,
+        max_length=max_length + 2,
         min_length=max_length,
         num_beams=5,
     )
-    print(summary_ids)
     
-    # Decode and return the summary
-    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    # Decode the summary
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    
+    # If show_length is True, append the length of the summary
+    if show_length:
+        summary_length = len(summary.split())
+        summary = f"{summary}\n\n(Summary length: {summary_length} words)"
+    
+    return summary
 
 interface = gr.Interface(
     fn=summarize_text,
     inputs=[
         gr.Textbox(lines=10, placeholder='Enter Text Here...', label='Input text'),
-        gr.Slider(minimum=10, maximum=150, step=1, label='Max Length')
+        gr.Slider(minimum=10, maximum=150, step=1, label='Max Length'),
+        gr.Checkbox(label='Show summary length')
     ],
     outputs=gr.Textbox(label='Summarized Text'),
     title='Text Summarizer using T5-finetuned-dialogue_sumxx',
@@ -50,5 +51,3 @@ interface = gr.Interface(
 )
 
 interface.launch()
-
-