Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name = "facebook/bart-large-cnn"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+def summarize_text(text):
+    if not text.strip():
+        return "Please enter some text."
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=1024
+    ).to(device)
+
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        max_length=150,
+        min_length=50,
+        num_beams=4,
+        early_stopping=True
+    )
+
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 📄 BART Summarizer")
+
+    text_input = gr.Textbox(lines=10, label="Enter Text")
+    btn = gr.Button("Generate Summary")
+    output = gr.Textbox(label="Summary", lines=6)
+
+    btn.click(summarize_text, inputs=text_input, outputs=output)
+
+demo.launch()