Create app.py

app.py

@@ -0,0 +1,50 @@
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+
+def predict(input, history=[]):
+
+ instruction = 'Instruction: given a dialog context, you need to response empathically'
+
+ knowledge = ' '
+
+ s = list(sum(history, ()))
+
+ s.append(input)
+
+ #print(s)
+
+ dialog = ' EOS ' .join(s)
+
+ #print(dialog)
+
+ query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+
+ top_p = 0.9
+ min_length = 8
+ max_length = 64
+
+
+ # tokenize the new input sentence
+ new_user_input_ids = tokenizer.encode(f"{query}", return_tensors='pt')
+
+
+ output = model.generate(new_user_input_ids, min_length=int(
+ min_length), max_length=int(max_length), top_p=top_p, do_sample=True).tolist()
+ 
+ 
+ response = tokenizer.decode(output[0], skip_special_tokens=True)
+
+ 
+ history.append((input, response))
+
+ return history, history
+
+import gradio as gr
+
+
+gr.Interface(fn=predict,
+ inputs=["text",'state'],
+ outputs=["chatbot",'state']).launch()