Create app.py

app.py

@@ -0,0 +1,53 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from itertools import chain
+import gradio as gr
+import torch 
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(device)
+
+tokenizer = AutoTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
+model = AutoModelForCausalLM.from_pretrained("uer/gpt2-chinese-cluecorpussmall").to(device)
+
+def generate_text(prompt,length=1000):
+  inputs = tokenizer(prompt,add_special_tokens=False, return_tensors="pt").to(device)
+
+  txt = tokenizer.decode(model.generate(inputs["input_ids"],
+                       max_length=length, 
+                       num_beams=2, 
+                       no_repeat_ngram_size=2,
+                       early_stopping=True,
+                       pad_token_id = 0
+                      )[0])
+  
+  #Replace text 
+  replacements = {
+    '[': "",
+    ']': "",
+    'S': "",
+    'E': "",
+    'P': "",
+    'U': "",
+    'N': "",
+    'K': ""
+                }
+
+
+  new_text = ''.join(chain.from_iterable(replacements.get(word, [word]) for word in txt))
+
+
+  return new_text
+
+with gr.Blocks() as web:
+  gr.Markdown("<h1><center>Andrew Lim Chinese stories </center></h1>")
+  gr.Markdown("""<h2><center>让人工智能讲故事:<br><br>
+ <img src=https://images.unsplash.com/photo-1550450339-e7a4787a2074?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1252&q=80></center></h2>""")
+  gr.Markdown("""<center>******</center>""")
+  
+
+  input_text = gr.Textbox(label="故事的开始", lines=6)  
+  buton = gr.Button("Submit ")  
+  output_text = gr.Textbox(lines=6, label="人工智能讲一个故事 :")
+  buton.click(generate_text, inputs=[input_text], outputs=output_text)  
+   
+web.launch()