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from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import torch
title = "🤖AI ChatBot"
description = "Building open-domain chatbots is a challenging area for machine learning research."
examples = [["How are you?"]]
pipe = pipeline("conversational", model="PygmalionAI/pygmalion-6b")
tokenizer = AutoTokenizer.from_pretrained("PygmalionAI/pygmalion-6b")
model = AutoModelForCausalLM.from_pretrained("PygmalionAI/pygmalion-6b")
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=4000, pad_token_id=tokenizer.eos_token_id
).tolist()
# convert the tokens to text, and then split the responses into lines
response = tokenizer.decode(history[0]).split("<|endoftext|>")
# print('decoded_response-->>'+str(response))
response = [
(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
] # convert to tuples of list
# print('response-->>'+str(response))
return response, history
gr.Interface(
fn=predict,
title=title,
description=description,
examples=examples,
inputs=["text", "state"],
outputs=["chatbot", "state"],
theme="finlaymacklon/boxy_violet",
).launch()
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