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import gradio as gr
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
from peft import PeftModel
ADAPTER_HUB = "lityops/Abstractive-Style-Summarizer"
BASE_MODEL_NAME = "google/flan-t5-base"
base_model = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL_NAME)
model = PeftModel.from_pretrained(base_model, ADAPTER_HUB)
tokenizer = AutoTokenizer.from_pretrained(ADAPTER_HUB)
summarizer = pipeline(
"summarization",
model=model,
tokenizer=tokenizer
)
def generate_summary(text, style):
if not text or len(text.split()) < 100:
return "Input must at least be 100 words long"
if len(text.split()) > 512:
return "Input must at most be 512 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
length_penalty = 1.5
beam_size = 4
max_cap = 120
elif style == 'Balanced':
max_len = int(input_words * 0.50)
min_len = 20
rep_penalty = 1.5
length_penalty = 1.2
beam_size = 4
max_cap = 180
else:
max_len = int(input_words * 0.70)
min_len = 50
rep_penalty = 1.2
length_penalty = 0.8
beam_size = 4
max_cap = 256
max_len = min(max_len, max_cap)
output = summarizer(
input_text,
max_length=max_len,
min_length=min_len,
num_beams=beam_size,
length_penalty=length_penalty,
repetition_penalty=rep_penalty,
no_repeat_ngram_size=3,
early_stopping=True
)
return output[0]["summary_text"]
custom_css = """
#header {text-align: center; margin-bottom: 25px;}
.gradio-container {max-width: 1000px !important;}
footer {display: none !important;}
"""
custom_css = """
#header {text-align: center; margin-bottom: 25px;}
.gradio-container {max-width: 95% !important;}
footer {display: none !important;}
"""
with gr.Blocks() as demo:
with gr.Column(elem_id="header"):
gr.Markdown("# Abstractive Style Summarizer")
gr.Markdown("Fine-tuned Flan-T5 model for abstractive multi-style document summarization.")
with gr.Row():
with gr.Column(scale=1):
input_box = gr.Textbox(
label="Input Text",
placeholder="Enter text to be summarized...",
lines=15
)
style_radio = gr.Radio(
choices=["Harsh", "Balanced", "Detailed"],
label="Summary Type",
value="Balanced"
)
with gr.Row():
clear_btn = gr.Button("Clear Input")
submit_btn = gr.Button("Generate Summary", variant="primary")
with gr.Column(scale=1):
output_box = gr.Textbox(
label="Output Summary",
lines=18,
interactive=False
)
copy_btn = gr.Button("Copy to Clipboard")
submit_btn.click(
fn=generate_summary,
inputs=[input_box, style_radio],
outputs=output_box
)
clear_btn.click(
fn=lambda: "",
inputs=None,
outputs=input_box
)
copy_btn.click(
fn=None,
inputs=[output_box],
js="(v) => { navigator.clipboard.writeText(v); }"
)
demo.launch(css=custom_css, theme=gr.themes.Base()) |