Upload app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+import torch
+from transformers import pipeline, T5TokenizerFast as T5Tokenizer
+
+MODEL_ID = 'lityops/Style-Summarizer'
+
+tokenizer = T5Tokenizer.from_pretrained(MODEL_ID)
+summarizer = pipeline(
+    "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"]
+
+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."
+)
+
+demo.launch()