Delete app.py

app.py

@@ -1,46 +0,0 @@
-import gradio as gr
-import torch 
-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 summarize long text.\nIt focus 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()