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# Advanced BART-based Summarizer (Gradio)
# =========================================
import gradio as gr
import torch
from transformers import BartTokenizer, BartForConditionalGeneration
# -----------------------------
# 1. Load Model & Tokenizer
# -----------------------------
MODEL_NAME = "facebook/bart-large-cnn"
tokenizer = BartTokenizer.from_pretrained(MODEL_NAME)
model = BartForConditionalGeneration.from_pretrained(MODEL_NAME)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# -----------------------------
# 2. Summarization Function
# -----------------------------
def summarize_text(text, max_len, min_len, num_beams):
if not text.strip():
return "Please enter some text."
inputs = tokenizer(
text,
max_length=1024,
return_tensors="pt",
truncation=True
).to(device)
summary_ids = model.generate(
inputs["input_ids"],
max_length=max_len,
min_length=min_len,
num_beams=num_beams,
length_penalty=2.0,
early_stopping=True,
no_repeat_ngram_size=3
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
# -----------------------------
# 3. Gradio UI
# -----------------------------
with gr.Blocks(title="Advanced BART Summarizer") as app:
gr.Markdown("## 🧠 Advanced BART Text Summarizer")
gr.Markdown("Summarize long documents using Facebook BART model")
with gr.Row():
input_text = gr.Textbox(
lines=15,
placeholder="Enter your text here...",
label="Input Text"
)
with gr.Row():
max_len = gr.Slider(50, 300, value=130, step=10, label="Max Length")
min_len = gr.Slider(10, 100, value=30, step=5, label="Min Length")
num_beams = gr.Slider(1, 8, value=4, step=1, label="Beam Size")
summarize_btn = gr.Button("Summarize")
output_text = gr.Textbox(
lines=10,
label="Summary"
)
summarize_btn.click(
summarize_text,
inputs=[input_text, max_len, min_len, num_beams],
outputs=output_text
)
# -----------------------------
# 4. Launch
# -----------------------------
app.launch() |