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from transformers import TFAutoModelForCausalLM, AutoTokenizer
import tensorflow as tf
import gradio as gr
# configuration params
TITLE = "<center><h1>Talk with an AI</h1></center>"
# Loading necessary NLP models
checkpoint = "elapt1c/ElapticAI-1a" # tf
model_gtp2 = TFAutoModelForCausalLM.from_pretrained(checkpoint)
tokenizer_gtp2 = AutoTokenizer.from_pretrained(checkpoint)
# test-to-test : chatting function -- GPT2
def chat_with_bot(user_input, chat_history_and_input=[]):
"""Text generation using GPT2"""
emb_user_input = tokenizer_gtp2.encode(
user_input + tokenizer_gtp2.eos_token, return_tensors="tf"
)
if chat_history_and_input == []:
bot_input_ids = emb_user_input # first iteration
else:
bot_input_ids = tf.concat(
[chat_history_and_input, emb_user_input], axis=-1
) # other iterations
chat_history_and_input = model_gtp2.generate(
bot_input_ids, max_length=50, pad_token_id=tokenizer_gtp2.eos_token_id
).numpy()
bot_response = tokenizer_gtp2.decode(
chat_history_and_input[:, bot_input_ids.shape[-1] :][0],
skip_special_tokens=True,
)
# Limit history to last 500 characters
chat_history_and_input = chat_history_and_input[:, -500:]
return bot_response, chat_history_and_input
# gradio interface
blocks = gr.Blocks()
with blocks:
session_state = gr.State([])
gr.Markdown(TITLE)
user_input = gr.Textbox(label="User Input")
bot_response = gr.Textbox(label="Bot Response")
user_input.change(
chat_with_bot,
inputs=[user_input, session_state],
outputs=[bot_response, session_state],
)
blocks.launch() |