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from transformers import AutoTokenizer
from transformers import pipeline
from transformers import TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments,AutoModelWithLMHead
import gradio as gr
chef = pipeline('text-generation', model="./en_gpt2-medium_rachel_replics/en_gpt2-medium_rachel_replics", tokenizer="gpt2-medium")
# gradio part
def echo(message, history, model):
#chef = pipeline('text-generation', model="./models/en_gpt2-medium_rachel_replics", tokenizer=model_type)
if model=="gpt2-medium":
answer = chef(f"<s>NOTFRIEND: {message}\nRACHEL:")[0]['generated_text']
answer = answer[answer.find(f"RACHEL: ") + len("RACHEL") + 2 : answer.find('</s>')]
return answer
elif model=="gpt2-medium":
answer = chef(f"<s>NOTFRIEND: {message}\nRACHEL:")[0]['generated_text']
answer = answer[answer.find(f"RACHEL: ") + len("RACHEL") + 2 : answer.find('</s>')]
return answer
elif model=="gpt2-medium":
answer = chef(f"<s>NOTFRIEND: {message}\nRACHEL:")[0]['generated_text']
answer = answer[answer.find(f"RACHEL: ") + len("RACHEL") + 2 : answer.find('</s>')]
return answer
title = "Chatbot who speaks like Rachel from Friends"
description = "You have a good opportunity to have a dialog with actress from Friends - Rachel Green"
model = gr.Dropdown(["gpt2", "gpt2-medium", "gpt2-large"], label="LLM", info="What model do you want to use?", value="gpt2-medium")
with gr.Blocks() as demo:
gr.ChatInterface(
fn=echo,
title=title,
description=description,
additional_inputs=[model],
retry_btn=None,
undo_btn=None,
clear_btn=None,
)
demo.launch(debug=False, share=True) |