from huggingface_hub import InferenceClient
import gradio as gr
import random
import prompts
client = InferenceClient(
    "mistralai/Mixtral-8x7B-Instruct-v0.1"
)

def format_prompt(message, history):
  prompt = "<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt
agents =[
    "WEB_DEV",
    "AI_SYSTEM_PROMPT",
    "PYTHON_CODE_DEV",
    "CODE_REVIEW_ASSISTANT",
    "CONTENT_WRITER_EDITOR",
    #"SOCIAL_MEDIA_MANAGER",
    #"MEME_GENERATOR",
    "QUESTION_GENERATOR",
    #"IMAGE_GENERATOR",
    "HUGGINGFACE_FILE_DEV",

]
def generate(
        prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
    seed = random.randint(1,1111111111111111)


    agent=prompts.WEB_DEV
    if agent_name == "WEB_DEV":
      agent = prompts.WEB_DEV_SYSTEM_PROMPT
    if agent_name == "CODE_REVIEW_ASSISTANT":
      agent = prompts.CODE_REVIEW_ASSISTANT
    if agent_name == "CONTENT_WRITER_EDITOR":
      agent = prompts.CONTENT_WRITER_EDITOR
    if agent_name == "SOCIAL_MEDIA_MANAGER":
      agent = prompts.SOCIAL_MEDIA_MANAGER        
    if agent_name == "AI_SYSTEM_PROMPT":
      agent = prompts.AI_SYSTEM_PROMPT
    if agent_name == "PYTHON_CODE_DEV":
      agent = prompts.PYTHON_CODE_DEV        
    #if agent_name == "MEME_GENERATOR":
    #   agent = prompts.MEME_GENERATOR  
    if agent_name == "QUESTION_GENERATOR":
        agent = prompts.QUESTION_GENERATOR 
    #if agent_name == "IMAGE_GENERATOR":
    #   agent = prompts.IMAGE_GENERATOR      
    if agent_name == "HUGGINGFACE_FILE_DEV":
        agent = prompts.HUGGINGFACE_FILE_DEV          
    
    system_prompt=agent
    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,
        seed=seed,
    )

    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    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
        yield output
    return output


additional_inputs=[
    gr.Dropdown(
        label="Agents",
        choices=[s for s in agents],
        value=agents[0],
        interactive=True,
        ),
    gr.Textbox(
        label="System Prompt",
        max_lines=1,
        interactive=True,
    ),
    gr.Slider(
        label="Temperature",
        value=0.9,
        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=1048*10,
        minimum=0,
        maximum=1048*10,
        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=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    ),


]

examples=[
    ["Create a simple web application using Flask", agents[0], None, None, None, None, ],
    ["Generate a Python script to perform a linear regression analysis", agents[2], None, None, None, None, ],
    ["Create a Dockerfile for a Node.js application", agents[1], None, None, None, None, ],
    ["Write a shell script to automate the deployment of a web application to a server", agents[3], None, None, None, None, ],
    ["Generate a SQL query to retrieve the top 10 most popular products by sales", agents[4], None, None, None, None, ],
    ["Write a Python script to generate a random password with a given length and complexity", agents[2], None, None, None, None, ],
    ["Create a simple game in Unity using C#", agents[0], None, None, None, None, ],
    ["Generate a Java program to implement a binary search algorithm", agents[2], None, None, None, None, ],
    ["Write a shell script to monitor the CPU usage of a server", agents[1], None, None, None, None, ],
    ["Create a simple web application using React and Node.js", agents[0], None, None, None, None, ],
    ["Generate a Python script to perform a sentiment analysis on a given text", agents[2], None, None, None, None, ],
    ["Write a shell script to automate the backup of a MySQL database", agents[1], None, None, None, None, ],
    ["Create a simple game in Unreal Engine using C++", agents[3], None, None, None, None, ],
    ["Generate a Java program to implement a bubble sort algorithm", agents[2], None, None, None, None, ],
    ["Write a shell script to monitor the memory usage of a server", agents[1], None, None, None, None, ],
    ["Create a simple web application using Angular and Node.js", agents[0], None, None, None, None, ],
    ["Generate a Python script to perform a text classification on a given dataset", agents[2], None, None, None, None, ],
    ["Write a shell script to automate the installation of a software package on a server", agents[1], None, None, None, None, ],
    ["Create a simple game in Godot using GDScript", agents[3], None, None, None, None, ],
    ["Generate a Java program to implement a merge sort algorithm", agents[2], None, None, None, None, ],
    ["Write a shell script to automate the cleanup of temporary files on a server", agents[1], None, None, None, None, ],
]


gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    additional_inputs=additional_inputs,
    title="Mixtral 46.7B",
    examples=examples,
    concurrency_limit=20,
).launch(show_api=False)