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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# Load the model and tokenizer
model_name = "vennify/t5-base-grammar-correction"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def correct_text(text, max_length, max_new_tokens, min_length, num_beams, temperature, top_p):
inputs = tokenizer.encode("grammar: " + text, return_tensors="pt")
if max_new_tokens > 0:
outputs = model.generate(
inputs,
max_length=max_length,
max_new_tokens=max_new_tokens,
min_length=min_length,
num_beams=num_beams,
temperature=temperature,
top_p=top_p,
early_stopping=True
)
else:
outputs = model.generate(
inputs,
max_length=max_length,
min_length=min_length,
num_beams=num_beams,
temperature=temperature,
top_p=top_p,
early_stopping=True
)
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return corrected_text
def respond(message, history, max_length, min_length, max_new_tokens, num_beams, temperature, top_p):
response = correct_text(message, max_length, max_new_tokens, min_length, num_beams, temperature, top_p)
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
fn=respond,
examples=[{"text": "hello"}, {"text": "hola"}, {"text": "merhaba"}],
title="Echo Bot",
additional_inputs=[
#gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=256, value=100, step=1, label="Max Length"),
gr.Slider(minimum=1, maximum=256, value=0, step=1, label="Min Length"),
gr.Slider(minimum=0, maximum=256, value=0, step=1, label="Max New Tokens (optional)"),
gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Num Beams"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch() |