first commit

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+tokenizer = AutoTokenizer.from_pretrained("yuewu/T5_abstract2title")
+model = T5ForConditionalGeneration.from_pretrained("yuewu/T5_abstract2title")
+
+def title2abstract(text):
+
+    input_ids = tokenizer(
+        text, 
+        padding='max_length',
+        max_length=512,
+        return_tensors="pt").input_ids
+
+    generated_ids = model.generate(
+        input_ids, 
+        max_length=128, 
+        # num_beams=3,
+        # no_repeat_ngram_size=2,
+        num_return_sequences=3,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        early_stopping=True)
+
+    generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+
+    output = f'1. {generated_text[0]}\n\n2. {generated_text[1]}\n\n3. {generated_text[2]}'
+
+    # output = generated_text
+
+    return output
+
+demo = gr.Interface(fn=title2abstract, inputs="text", outputs="text",
+                    title="Abstract to title generator",
+                    description="Give a chemistry paper abstract and the model will suggest 3 titles.")
+demo.launch()

requirements.txt

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+transformers >= 4.0.0
+torch >= 1.6.0