Add application and requirements

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+model_id = 'ksabeh/gavi'
+max_input_length = 512
+max_target_length = 10
+
+model = T5ForConditionalGeneration.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def predict(title, category):
+    input = f"{title} <hl> {category} <hl>"
+    model_input = tokenizer(input, max_length=max_input_length, truncation=True,
+                           padding="max_length")
+    model_input = {k:torch.unsqueeze(torch.tensor(v),dim=0) for k,v in model_input.items()}
+    predictions = model.generate(**model_input, num_beams=8, do_sample=True, max_length=10)
+    return tokenizer.batch_decode(predictions, skip_special_tokens=True)[0]
+
+iface = gr.Interface(
+    predict,
+    inputs=["text", "text"],
+    outputs=['text'],
+    title="Attribute Generation",
+)
+
+iface.launch()

requirements.txt

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+transformers
+torch