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app.py

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+import gradio as gr
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("Armandoliv/t5-small-summarizer-scitldr")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("Armandoliv/t5-small-summarizer-scitldr")
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+def main_summarizer(text):
+  max_input_length = 1024
+  preprocess_text = text.strip().replace("\n"," ").replace("’", "'").strip()
+  tokenized_text = tokenizer.encode(preprocess_text, return_tensors="pt", truncation=True, max_length=max_input_length,).to(device)
+
+  summary_ids = model.generate(
+              tokenized_text,
+              max_length=256, 
+              num_beams=8,
+              repetition_penalty=3.0, 
+              length_penalty=2.5, 
+              early_stopping=False
+          )
+
+  output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+  return output
+  
+inputs = [gr.Textbox(lines=10, placeholder="Text Here...", label="Input")]
+outputs = gr.Text( label="Summary")
+title="Text summarisation app"
+description = "This demo uses AI Models to to summarize long text.\nOt focuses on scientific texts."
+
+io = gr.Interface(fn=main_summarizer, inputs=inputs, outputs=outputs, title=title, description = description,
+
+                  css= """.gr-button-primary { background: -webkit-linear-gradient( 
+                    90deg, #355764 0%, #55a8a1 100% ) !important;     background: #355764;
+                        background: linear-gradient( 
+                    90deg, #355764 0%, #55a8a1 100% ) !important;
+                        background: -moz-linear-gradient( 90deg, #355764 0%, #55a8a1 100% ) !important;
+                        background: -webkit-linear-gradient( 
+                    90deg, #355764 0%, #55a8a1 100% ) !important;
+                    color:white !important}"""
+                  )
+  
+  io.launch()
+