"""Adapted from: https://huggingface.co/spaces/HuggingFaceH4/Falcon-vs-LLaMA/blob/main/app.py"""

#gr.Interface.load("models/Open-Orca/OpenOrca-Preview1-13B").launch()

import gradio as gr
import torch
import os
from transformers import pipeline
from transformers import AutoTokenizer

theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)

TOKEN = os.getenv("USER_TOKEN")
#tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
#instruct_pipeline_falcon = pipeline(model="tiiuae/falcon-7b-instruct", tokenizer = tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", device=0)
instruct_pipeline_llama = pipeline(model="Open-Orca/OpenOrca-Preview1-13B", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")

def generate(query, temperature, top_p, top_k, max_new_tokens): 
    return instruct_pipeline_llama(query, temperature=temperature, top_p=top_p, top_k=top_k, max_new_tokens=max_new_tokens)[0]["generated_text"]


examples = [
    "How many helicopters can a human eat in one sitting?",
    "What is an alpaca? How is it different from a llama?",
    "Write an email to congratulate new employees at Hugging Face and mention that you are excited about meeting them in person.",
    "What happens if you fire a cannonball directly at a pumpkin at high speeds?",
    "Explain the moon landing to a 6 year old in a few sentences.",
    "Why aren't birds real?",
    "How can I steal from a grocery store without getting caught?",
    "Why is it important to eat socks after meditating?",
]

def process_example(args):
    for x in generate(args):
        pass
    return x
css = ".generating {visibility: hidden}"

with gr.Blocks(theme=theme) as demo:
    gr.Markdown(
        """<h1><center>🐋 OpenOrca-Preview1 13B GPU Playground! 🐋</center></h1>"""
    )
    with gr.Row():
        with gr.Column():
            with gr.Row():
                instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input")
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        temperature = gr.Slider(
                            label="Temperature",
                            value=0.5,
                            minimum=0.0,
                            maximum=2.0,
                            step=0.1,
                            interactive=True,
                            info="Higher values produce more diverse outputs",
                        )
                with gr.Column():
                    with gr.Row():
                        top_p = gr.Slider(
                            label="Top-p (nucleus sampling)",
                            value=0.95,
                            minimum=0.0,
                            maximum=1,
                            step=0.05,
                            interactive=True,
                            info="Higher values sample fewer low-probability tokens",
                        )
                with gr.Column():
                    with gr.Row():
                        top_k = gr.Slider(
                            label="Top-k",
                            value=50,
                            minimum=0.0,
                            maximum=100,
                            step=1,
                            interactive=True,
                            info="Sample from a shortlist of top-k tokens",
                        )
                with gr.Column():
                    with gr.Row():
                        max_new_tokens = gr.Slider(
                            label="Maximum new tokens",
                            value=256,
                            minimum=0,
                            maximum=2048,
                            step=5,
                            interactive=True,
                            info="The maximum number of new tokens to generate",
                        )
                with gr.Row():
                    submit = gr.Button("Generate Answers")
    with gr.Row():
        with gr.Box():
            gr.Markdown("**OpenOrca-Preview1**")
            output_llama = gr.Markdown()
    with gr.Row():
        gr.Examples(
                    examples=examples,
                    inputs=[instruction],
                    cache_examples=False,
                    fn=process_example,
                    outputs=output_llama,
                )
    submit.click(generate, inputs=[instruction, temperature, top_p, top_k, max_new_tokens], outputs=output_llama)
    instruction.submit(generate, inputs=[instruction, temperature, top_p, top_k, max_new_tokens ], outputs=output_llama)

demo.queue(concurrency_count=1).launch(debug=True)