import json
import os
import shutil

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("H4_TOKEN", None)
# HF_TOKEN = os.environ.get("TRL_TOKEN", None)
API_URL = os.environ.get("API_URL")

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"],
)
if HF_TOKEN:
    try:
        shutil.rmtree("./data/")
    except:
        pass

    repo = Repository(
        local_dir="./data/", clone_from="h4/star-chat-prompts", use_auth_token=HF_TOKEN, repo_type="dataset"
    )
    repo.git_pull()

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

PROMPT_TEMPLATE = """Question: {prompt}\n\nAnswer:"""


def save_inputs_and_outputs(inputs, outputs, generate_kwargs):
    with open(os.path.join("data", "prompts.jsonl"), "a") as f:
        json.dump({"inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}, f, ensure_ascii=False)
        f.write("\n")
        commit_url = repo.push_to_hub()


def generate(instruction, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,  do_save=True):
    formatted_instruction = PROMPT_TEMPLATE.format(prompt=instruction)

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

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        truncate=999,
        seed=42,
        stop_sequences=["</s>"],
    )

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

    output = ""
    for response in stream:
        output += response.token.text
        yield output
    if HF_TOKEN and do_save:
        try:
            print("Pushing prompt and completion to the Hub")
            save_inputs_and_outputs(formatted_instruction, output, generate_kwargs)
        except Exception as e:
            print(e)
            
    return output


examples = [
    "A llama is in my lawn. How do I get rid of him?",
    "What are the various algorithms to sort a list?",
    "How can I sort a list in Python?",
    "How do I ask a question in StackOverflow?",
    "How to beat a Hitmonlee in a Pokemon battle?",
    "How can I write a Java function to generate the nth Fibonacci number?",
]


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

css = ".generating {visibility: hidden}" + share_btn_css

with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
    with gr.Column():
        gr.Markdown(
            """![](https://huggingface.co/spaces/trl-lib/stack-llama/resolve/main/stackllama_logo.png)


            StackLLaMa is a 7 billion parameter language model based on [Meta's LLaMA model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) that has been trained on pairs of questions and answers from [Stack Exchange](https://stackexchange.com) using Reinforcement Learning from Human Feedback (RLHF) with the [TRL library](https://github.com/lvwerra/trl). For more details, check out our [blog post](https://huggingface.co/blog/stackllama).

            Type in the box below and click the button to generate answers to your most pressing questions!

            ⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/trl-lib/llama-7b-se-rl-peft) are provided as educational tools to explain RLHF with the TRL library; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](https://huggingface.co/trl-lib/llama-7b-se-rl-peft#intended-uses--limitations)
            
            ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below:
      """
        )
        with gr.Row():
            with gr.Column(scale=3):
                do_save = gr.Checkbox(
                        value=True,
                        label="Store data",
                        info="You agree to the storage of your prompt and generated text for research and development purposes:")
                instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input")

                
                with gr.Box():
                    gr.Markdown("**Answer**")
                    output = gr.Markdown(elem_id="q-output")
                submit = gr.Button("Generate", variant="primary")
                with gr.Group(elem_id="share-btn-container"):
                    community_icon = gr.HTML(community_icon_html, visible=True)
                    loading_icon = gr.HTML(loading_icon_html, visible=True)
                    share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
                gr.Examples(
                    examples=examples,
                    inputs=[instruction],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output],
                )

            with gr.Column(scale=1):
                
                temperature = gr.Slider(
                    label="Temperature",
                    value=0.9,
                    minimum=0.0,
                    maximum=2.0,
                    step=0.1,
                    interactive=True,
                    info="Higher values produce more diverse outputs",
                )
                max_new_tokens = gr.Slider(
                    label="Max new tokens",
                    value=256,
                    minimum=0,
                    maximum=512,
                    step=4,
                    interactive=True,
                    info="The maximum numbers of new tokens",
                )
                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=1.2,
                    minimum=1.0,
                    maximum=2.0,
                    step=0.05,
                    interactive=True,
                    info="Penalize repeated tokens",
                )

    submit.click(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, do_save], outputs=[output])
    instruction.submit(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty], outputs=[output])
    share_button.click(None, [], [], _js=share_js)

demo.queue(concurrency_count=16).launch(debug=True)