Upload app.py

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/flan-t5-english-summarizer")
+model = AutoModelForSeq2SeqLM.from_pretrained("Adarsh921/flan-t5-english-summarizer")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+MAX_INPUT_LEN = 768
+MAX_TARGET_LEN = 150
+
+
+def summarize(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=MAX_INPUT_LEN
+    ).to(device)
+
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_length=MAX_TARGET_LEN,
+            min_length=40,
+            num_beams=6,
+            no_repeat_ngram_size=3,
+            length_penalty=1.0,
+            early_stopping=True
+        )
+
+    return tokenizer.decode(output[0], skip_special_tokens=True).strip()
+
+# Gradio UI
+gr.Interface(
+    fn=summarize,
+    inputs=gr.Textbox(lines=10, label="Paste english Article"),
+    outputs=gr.Textbox(label="Generated Summary"),
+    title="English Article Summarizer",
+    description="Summarizer fine-tuned on ILSUM 2024 using Flan-T5"
+).launch(share=True)