Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+MODEL_ID = "daniB2112/bart-news-summarizer"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
+
+def summarize(text, max_length=150, min_length=40):
+    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        max_length=max_length,
+        min_length=min_length,
+        length_penalty=2.0,
+        num_beams=4,
+        early_stopping=True
+    )
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=summarize,
+    inputs=[
+        gr.Textbox(lines=10, placeholder="Paste news article here...", label="Article"),
+        gr.Slider(50, 300, value=150, label="Max Summary Length"),
+        gr.Slider(10, 100, value=40,  label="Min Summary Length"),
+    ],
+    outputs=gr.Textbox(label="Summary"),
+    title="BART News Summarizer",
+    description="Summarize news articles using daniB2112/bart-news-summarizer"
+)
+
+demo.launch()