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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+import torch
+
+model_name = "AGIvan/t5-base-title-generation"
+
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+nltk.download("punkt")
+
+def generate_titles(text, num_titles, temperature):
+
+    inputs = tokenizer(
+        ["summarize: " + text],
+        return_tensors="pt",
+        truncation=True,
+        max_length=512
+    )
+
+    outputs = model.generate(
+        **inputs,
+        do_sample=True,
+        temperature=temperature,
+        num_return_sequences=num_titles
+    )
+
+    decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    titles = [nltk.sent_tokenize(t.strip())[0] for t in decoded]
+
+    return "\n".join(titles)
+
+
+interface = gr.Interface(
+    fn=generate_titles,
+    inputs=[
+        gr.Textbox(lines=15, label="Article text"),
+        gr.Slider(1,10,value=5,step=1,label="Number of titles"),
+        gr.Slider(0.1,1.5,value=0.7,step=0.05,label="Temperature")
+    ],
+    outputs="text",
+    title="Article Title Generator"
+)
+
+interface.launch()

requirements.txt

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+nltk
+torch
+transformers