Update app.py

app.py

@@ -3,20 +3,20 @@ import gradio as gr
 import tensorflow as tf
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-global tokenizer, model, script_speaker_name, script_responder_name
+global tokenizer, model, script_speaker_name, script_responder_name, convo
 tokenizer = GPT2Tokenizer.from_pretrained("ethzanalytics/ai-msgbot-gpt2-XL-dialogue")
 model = GPT2LMHeadModel.from_pretrained("ethzanalytics/ai-msgbot-gpt2-XL-dialogue", pad_token_id=tokenizer.eos_token_id)
 
 script_speaker_name = "person alpha"
 
 script_responder_name = "person beta"
-
+convo = ''
 def output(prompt):
-    sentence = script_speaker_name + ': ' + prompt + '\n' + script_responder_name + ': '
+    sentence = convo + '\n' + script_speaker_name + ': ' + prompt + '\n' + script_responder_name + ': '
     input_ids = tokenizer.encode(sentence, return_tensors='pt')
     # generate text until the output length (which includes the context length) reaches 50
     output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
-    return(tokenizer.decode(output[0], skip_special_tokens=True))
+    convo = tokenizer.decode(output[0], skip_special_tokens=True))
 
 iface = gr.Interface(fn=output, inputs="text", outputs="text")
 iface.launch()