Create app.py

app.py

@@ -0,0 +1,21 @@
+import transformers
+from huggingface_hub import notebook_login
+from datasets import load_dataset
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+model_checkpoint = "distilgpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
+from transformers import pipeline
+import gradio as gr
+from transformers import pipeline
+
+generator = pipeline('text-generation', model='DioLiu/GPT2_Suggestion',tokenizer=tokenizer)
+def get_suggestion(input_text):
+  answer=generator(input_text, max_length=200, num_return_sequences=1)[0]['generated_text']
+  if answer.find('\n\nGiven')!=-1:
+   output=answer[0:answer.find('\n\nGiven')]
+  else:
+   output=answer[0:answer.rfind('.')+1]
+  return output
+iface = gr.Interface(fn= get_suggestion, inputs="text", outputs = ["text"], title="Suggestions")
+iface.launch()