import json
import os
import shutil
import requests

import gradio as gr
from huggingface_hub import Repository
from text_generation import Client

from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css

HF_TOKEN = os.environ.get("HF_TOKEN", None)

API_URL = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"

file_name = "Project_CodeNet_LangClass.tar.gz"
data_url = f"https://dax-cdn.cdn.appdomain.cloud/dax-project-codenet/1.0.0/{file_name}"

FIM_PREFIX = "<PRE> "
FIM_MIDDLE = " <MID>"
FIM_SUFFIX = " <SUF>"

FIM_INDICATOR = "<FILL_ME>"

EOS_STRING = "</s>"
EOT_STRING = "<EOT>"

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",
    ],
)

client = Client(
    API_URL,
    headers={"Authorization": f"Bearer {HF_TOKEN}"},
)


def generate(
    prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    fim_mode = False

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    if FIM_INDICATOR in prompt:
        fim_mode = True
        try:
            prefix, suffix = prompt.split(FIM_INDICATOR)
        except:
            raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
        prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"

    
    stream = client.generate_stream(prompt, **generate_kwargs)
    

    if fim_mode:
        output = prefix
    else:
        output = prompt

    previous_token = ""
    for response in stream:
        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
            if fim_mode:
                output += suffix
                yield output
                return output
                print("output", output)
            else:
                return output
        else:
            output += response.token.text
        previous_token = response.token.text
        yield output
    return output


examples = [
    "# Use the Project CodeNet model to convert this Java code into Python.\n// Java code to reverse a string\npublic String reverseString(String input) {",
    "# Write a function in C++ to compute the factorial of a number using recursion.\nint factorial(int n) {",
    "# Python function to check if a given number is prime. Complete the function.\ndef is_prime(num):\n    for i in range(2, num):",
    "# JavaScript code to dynamically change the color of a webpage background based on user input.\ndocument.body.style.backgroundColor = ",
    "# Given a list of employee names and their salaries, write a Python code to sort the employees by their salary in descending order.\nemployees = [('John Doe', 120000), ('Jane Smith', 110000), ",
    "# Write a Ruby method to count the number of vowels in a given string.\ndef count_vowels(string)\n    vowels_count = 0\n",
]



def process_example(args):
    for x in generate(args):
        pass
    return x


css = ".generating {visibility: hidden}"

monospace_css = """
#q-input textarea {
    font-family: monospace, 'Consolas', Courier, monospace;
}
"""


css += share_btn_css + monospace_css + ".gradio-container {color: black}"

description = """
<div style="text-align: center;">
    <h1> 🦙 Project Codenet Playground</h1>
</div>
<div style="text-align: left;">
    <p>We have leveraged the expansive dataset from Project CodeNet to enhance our model, CodeLlama. The <a href="https://huggingface.co/datasets/code_search_net">Project CodeNet dataset</a> is an extensive collection specifically curated to spur algorithmic advancements in AI for coding tasks. It supports a wide range of applications including code translation, code similarity assessment, code classification, and code search functionalities.</p>
</div>

"""

with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
    with gr.Column():
        gr.Markdown(description)
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(
                    placeholder="Enter your code here",
                    lines=5,
                    label="Input",
                    elem_id="q-input",
                )
                submit = gr.Button("Generate", variant="primary")
                output = gr.Code(elem_id="q-output", lines=30, label="Output")
                with gr.Row():
                    with gr.Column():
                        with gr.Accordion("Advanced settings", open=False):
                            with gr.Row():
                                column_1, column_2 = gr.Column(), gr.Column()
                                with column_1:
                                    temperature = gr.Slider(
                                        label="Temperature",
                                        value=0.1,
                                        minimum=0.0,
                                        maximum=1.0,
                                        step=0.05,
                                        interactive=True,
                                        info="Higher values produce more diverse outputs",
                                    )
                                    max_new_tokens = gr.Slider(
                                        label="Max new tokens",
                                        value=256,
                                        minimum=0,
                                        maximum=8192,
                                        step=64,
                                        interactive=True,
                                        info="The maximum numbers of new tokens",
                                    )
                                with column_2:
                                    top_p = gr.Slider(
                                        label="Top-p (nucleus sampling)",
                                        value=0.90,
                                        minimum=0.0,
                                        maximum=1,
                                        step=0.05,
                                        interactive=True,
                                        info="Higher values sample more low-probability tokens",
                                    )
                                    repetition_penalty = gr.Slider(
                                        label="Repetition penalty",
                                        value=1.05,
                                        minimum=1.0,
                                        maximum=2.0,
                                        step=0.05,
                                        interactive=True,
                                        info="Penalize repeated tokens",
                                    )
                                    
                gr.Examples(
                    examples=examples,
                    inputs=[instruction],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output],
                )

    submit.click(
        generate,
        inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty],
        outputs=[output],
    )
demo.queue(concurrency_count=16).launch(debug=True)