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import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "ibm-granite/granite-3.3-2b-instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
def summarize(text):
messages = [
{
"role": "system",
"content": "You are an expert assistant. Summarize the given text into clear, concise bullet points."
},
{
"role": "user",
"content": text
}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=200,
temperature=0.3,
do_sample=False
)
summary = tokenizer.decode(
outputs[0][inputs["input_ids"].shape[-1]:],
skip_special_tokens=True
)
return summary
demo = gr.Interface(
fn=summarize,
inputs=gr.Textbox(
lines=10,
placeholder="Paste text to summarize...",
label="Input Text"
),
outputs=gr.Textbox(
lines=8,
label="Summary (Bullet Points)"
),
title="Text Summarizer (Point-wise)",
description="Summarizes input text into clear bullet points using IBM Granite 3.3-2B Instruct."
)
if __name__ == "__main__":
demo.launch()
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