import json
import os
import shutil
import requests

import gradio as gr
from huggingface_hub import Repository, InferenceClient

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = "https://api-inference.huggingface.co/models/WizardLM/WizardCoder-Python-34B-V1.0"
BOT_NAME = "Wizard"

STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"]

EXAMPLES = [
    ["what are the benefits of programming in python?"],
    ["explain binary search in java?"],
    ]

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

def format_prompt(message, history, system_prompt):
  prompt = ""
  if system_prompt:
    prompt += f"System: {system_prompt}\n"
  for user_prompt, bot_response in history:
    prompt += f"User: {user_prompt}\n"
    prompt += f"Wizard: {bot_response}\n" # Response already contains "Wizard: "
  prompt += f"""User: {message}
Wizard:"""
  return prompt

seed = 42

def generate(
    prompt, history, system_prompt="", temperature=0.4, max_new_tokens=800, top_p=0.95, repetition_penalty=1.5,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    global seed
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        stop_sequences=STOP_SEQUENCES,
        do_sample=True,
        seed=seed,
    )
    seed = seed + 1
    formatted_prompt = format_prompt(prompt, history, system_prompt)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text

        for stop_str in STOP_SEQUENCES:
            if output.endswith(stop_str):
                output = output[:-len(stop_str)]
                output = output.rstrip()
                yield output
        yield output
    return output


additional_inputs=[
    gr.Textbox("", label="Optional system prompt"),
    gr.Slider(
        label="Temperature",
        value=0.4,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=800,
        minimum=0,
        maximum=8192,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    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",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.5,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]


def vote(data: gr.LikeData):
    if data.liked:
        print("You upvoted this response: " + data.value)
    else:
        print("You downvoted this response: " + data.value)
        

chatbot = gr.Chatbot(avatar_images=('user.png', 'bot.png'),bubble_full_width = False)

chat_interface = gr.ChatInterface(
    generate, 
    chatbot = chatbot,
    examples=EXAMPLES,
    additional_inputs=additional_inputs,
    ) 


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                # Code bot
            )
            
    chatbot.like(vote, None, None)
    chat_interface.render()
    
demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)