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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("Adarsh921/indicbart-hindi-summarizer")
+model = AutoModelForSeq2SeqLM.from_pretrained("Adarsh921/indicbart-hindi-summarizer")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# Inference function
+def generate_summary(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        max_length=512,
+        truncation=True,
+        padding="max_length"
+    )
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        num_beams=4,
+        max_length = 128,
+        min_length=30,
+        no_repeat_ngram_size=3,
+        early_stopping=True
+    )
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Gradio UI
+gr.Interface(
+    fn=generate_summary,
+    inputs=gr.Textbox(lines=10, label="Paste Hindi Article"),
+    outputs=gr.Textbox(label="Generated Summary"),
+    title="Hindi Article Summarizer",
+    description="Summarizer fine-tuned on ILSUM 2024 using IndicBART"
+).launch(share=True)