Update app.py

app.py

@@ -5,57 +5,57 @@ MODEL_ID = 'lityops/Style-Summarizer'
 
 tokenizer = T5Tokenizer.from_pretrained(MODEL_ID)
 summarizer = pipeline(
-    "summarization",
-    model=MODEL_ID,
-    tokenizer=tokenizer,
-    device=-1
+  "summarization",
+  model=MODEL_ID,
+  tokenizer=tokenizer,
+  device=-1
 )
 
 def generate_summary(text, style):
-    if not text or len(text.strip()) < 50:
-        return "Input must at least be 50 words long"
-
-    input_text = f"Summarize {style}: {text}"
-    input_words = len(text.split())
-
-    if style == 'Harsh':
-        max_len = int(input_words * 0.35)
-        min_len = 5
-        rep_penalty = 2.5
-        beam_size = 4
-    elif style == 'Balanced':
-        max_len = int(input_words * 0.50)
-        min_len = 20
-        rep_penalty = 1.5
-        beam_size = 4
-    else:
-        max_len = int(input_words * 0.70)
-        min_len = 50
-        rep_penalty = 1.2
-        beam_size = 4
-
-    max_len = min(max_len, 256)
-    
-    output = summarizer(
-        input_ids=input_text,
-        max_length=max_len, 
-        min_length=min_len, 
-        num_beams=beam_size,
-        repetition_penalty=rep_penalty,
-        no_repeat_ngram_size=3,
-        early_stopping=True
-    )
-    return output[0]["summary_text"]
+  if not text or len(text.strip()) < 50:
+    return "Input must at least be 50 words long"
+
+  input_text = f"summarize {style}: {text}"
+  input_words = len(text.split())
+
+  if style == 'harsh':
+    max_len = int(input_words * 0.35)
+    min_len = 5
+    rep_penalty = 2.5
+    beam_size = 4
+  elif style == 'standard':
+    max_len = int(input_words * 0.50)
+    min_len = 20
+    rep_penalty = 1.5
+    beam_size = 4
+  else:
+    max_len = int(input_words * 0.70)
+    min_len = 50
+    rep_penalty = 1.2
+    beam_size = 4
+
+  max_len = min(max_len, 256)
+  
+  output = summarizer(
+    input_ids=input_text,
+    max_length=max_len, 
+    min_length=min_len, 
+    num_beams=beam_size,
+    repetition_penalty=rep_penalty,
+    no_repeat_ngram_size=3,
+    early_stopping=True
+  )
+  return output[0]["summary_text"]
 
 demo = gr.Interface(
-    fn=generate_summary,
-    inputs=[
-        gr.Textbox(label="Input Text", lines=10, placeholder="Paste text here..."),
-        gr.Radio(["Harsh", "Balanced", "Detailed"], label="Summary Style", value="Balanced")
-    ],
-    outputs=gr.Textbox(label="Summary"),
-    title="Style Summarizer",
-    description="A custom Flan-T5-Base model fine-tuned to generate summaries in different styles."
+  fn=generate_summary,
+  inputs=[
+    gr.Textbox(label="Input Text", lines=10, placeholder="Paste text here..."),
+    gr.Radio(["harsh", "standard", "detailed"], label="Summary Style", value="standard")
+  ],
+  outputs=gr.Textbox(label="Summary"),
+  title="Style Summarizer",
+  description="A custom Flan-T5-Base model fine-tuned to generate summaries in different styles."
 )
 
 demo.launch()