import spaces
import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

title = """# 👋🏻Welcome To 🌟Tonic's🌐Aya-101"""
description = """The Aya model is a massively multilingual generative language model that follows instructions in 101 languages.  You can build with this endpoint using🌐Aya-101 available here : [CohereForAI/aya-101](https://huggingface.co/CohereForAI/aya-101). Try your own language out !
You can also use 🌐Aya-101 by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/Aya?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> the easiest way to use Aya-101 is to use the Cohere CLI and their playground. Try finetuning the version with open weights !
Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) Math with [introspector](https://huggingface.co/introspector) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [MultiTonic](https://github.com/Tonic-AI/MultiToniic)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""

device = "cuda"
checkpoint = "CohereForAI/aya-101"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map=device)

#Set a the value of the repetition penalty
#The higher the value, the less repetitive the generated text will be
#Note that `repetition_penalty` has to be a strictly positive float
repetition_penalty = 1.8

@spaces.GPU
def aya(text, max_new_tokens):
    model.to(device)
    inputs = tokenizer.encode(text, return_tensors="pt").to(device)
    outputs = model.generate(inputs, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty)
    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translation

def main():
    with gr.Blocks() as demo:
        gr.Markdown(title)
        gr.Markdown(description)
        input_text = gr.Textbox(label="🗣️Input Text")
        max_new_tokens_slider = gr.Slider(minimum=150, maximum=1648, step=1, value=250, label="Size of your inputs and answer")
        submit_button = gr.Button("Use🌐Aya")        
        output_text = gr.Textbox(label="🌐Aya", interactive=False)
        submit_button.click(fn=aya, inputs=[input_text, max_new_tokens_slider], outputs=output_text)
    
    demo.launch()

if __name__ == "__main__":
    main()