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import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name = "anezatra/gpt2_openassistant_guanaco"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
def respond(message, history):
prompt = "\n".join(history + [message])
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(
inputs,
temperature=0.8,
max_new_tokens=200,
top_k=1,
num_return_sequences=1,
no_repeat_ngram_size=2,
do_sample=True,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = response.split("### Assistant:")[-1].strip()
if "### Human:" in response:
response = response.split("### Human:")[1].strip()
return response
banner = gr.HTML("""
<h1 style="color: #000; font-weight: bold; text-align: center;">
OPENASSISTANT
</h1>
<p style="color: #000; font-weight: bold;">GPT-2 MEDIUM CHATBOT</p>
""")
with gr.Blocks(theme=gr.Theme.from_hub('gradio/monochrome')) as demo:
banner.render()
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Write your message")
with gr.Row():
clear = gr.Button("Clear chat")
submit = gr.Button("Send message")
def user_input(user_message, history):
response = respond(user_message, history)
return "", history + [[user_message, response]]
msg.submit(user_input, [msg, chatbot], [msg, chatbot], queue=False)
clear.click(lambda: None, None, chatbot, queue=False)
submit.click(lambda: msg.submit(), None, chatbot, queue=False)
if __name__ == "__main__":
demo.launch(share=True)
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