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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
def predict(input, history=[]):
# tokenize the new input sentence
new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
# generate a response
history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
# convert the tokens to text, and then split the responses into the right format
response = tokenizer.decode(history[0]).split("<|endoftext|>")
response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list
return response, history
import gradio as gr
demo = gr.Blocks()
with demo:
with gr.Row():
output_chatbot = gr.outputs.Chatbot()
output_state = gr.outputs.State()
with gr.Row():
input_text = gr.inputs.Textbox(label="write some text")
input_state = gr.inputs.State()
submit_button = gr.Button("Send")
submit_button.click(predict, inputs=[input_text, input_state], outputs=[output_chatbot, output_state])
demo.launch()
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