Add application file

app.py

@@ -0,0 +1,33 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import gradio as gr
+from transformers import BlenderbotTokenizer
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration, BlenderbotConfig
+from transformers import BlenderbotTokenizerFast 
+import contextlib
+
+#tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+#model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
+#tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-3B")
+mname = "facebook/blenderbot-400M-distill"
+#configuration = BlenderbotConfig.from_pretrained(mname)
+tokenizer = BlenderbotTokenizerFast.from_pretrained(mname)
+model = BlenderbotForConditionalGeneration.from_pretrained(mname)
+#tokenizer = BlenderbotTokenizer.from_pretrained(mname)
+#-----------new chat-----------
+print(mname + 'model loaded')
+def predict(input,history=[]):
+  
+    history.append(input)
+    
+    listToStr= '</s> <s>'.join([str(elem)for elem in history[len(history)-3:]])
+    #print('listToStr -->',str(listToStr))
+    input_ids = tokenizer([(listToStr)], return_tensors="pt",max_length=512,truncation=True)
+    next_reply_ids = model.generate(**input_ids,max_length=512, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]
+    history.append(response)
+    response = [(history[i], history[i+1]) for i in range(0, len(history)-1, 2)]  # convert to tuples of list
+    return response, history
+	
+demo = gr.Interface(fn=predict, inputs=["text",'state'], outputs=["chatbot",'state'])
+demo.launch(share=True)