import gradio as gr
from huggingface_hub import Repository, InferenceClient
import os
import json
import re

API_TOKEN = os.environ.get("API_TOKEN")
API_ENDPOINT = os.environ.get("API_ENDPOINT")

KEY = os.environ.get("KEY")

SPECIAL_SYMBOLS = ["‹", "›"]

API_ENDPOINTS = {
    "Falcon": "tiiuae/falcon-180B-chat",
    "Llama": "meta-llama/Llama-2-70b-chat-hf"
}

CHOICES = []
CLIENTS = {}

for model_name, model_endpoint in API_ENDPOINTS.items():
    CHOICES.append(model_name)
    CLIENTS[model_name] = InferenceClient(model_endpoint, headers = { "Authorization": f"Bearer {API_TOKEN}" })

def format(instruction = "", history = "", input = "", preinput = ""):
    sy_l, sy_r = SPECIAL_SYMBOLS[0], SPECIAL_SYMBOLS[1]
    formatted_history = '\n'.join(f"{sy_l}{message}{sy_r}" for message in history)
    formatted_input = f"System: {sy_l}{instruction}{sy_r}\n{formatted_history}\n{sy_l}{input}{sy_r}\n{preinput}"
    return formatted_input
    
def predict(instruction, history, input, preinput, access_key, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed):

    if (access_key != KEY):
        print(">>> MODEL FAILED: Input: " + input + ", Attempted Key: " + access_key)
        return ("[UNAUTHORIZED ACCESS]", input);
        
    stops = json.loads(stop_seqs)

    formatted_input = format(instruction, history, input, preinput)

    history = history + [[input, ""]]
    
    response = CLIENTS[model].text_generation(
        formatted_input,
        temperature = temperature,
        max_new_tokens = max_tokens,
        top_p = top_p,
        top_k = top_k,
        repetition_penalty = rep_p,
        stop_sequences = stops,
        do_sample = True,
        seed = seed,
        stream = False,
        details = False,
        return_full_text = False
    )

    sy_l, sy_r = SPECIAL_SYMBOLS[0], SPECIAL_SYMBOLS[1]
    pre_result = f"{sy_l}{response}{sy_r}{''.join(SPECIAL_SYMBOLS)}"
    pattern = re.compile(f"{sy_l}(.*?){sy_r}", re.DOTALL)
    match = pattern.search(pre_result)
    get_result = match.group(1).strip() if match else ""

    history = history + [get_result]
    
    print(f"---\nUSER: {input}\nBOT: {get_result}\n---")

    return (get_result, input)

def clear():
    return []
     
def maintain_cloud():
    print(">>> SPACE MAINTAINED!")
    return ("SUCCESS!", "SUCCESS!")

with gr.Blocks() as demo:
    with gr.Row(variant = "panel"):
        gr.Markdown("🔯 This is a private LLM CHAT Space owned within STC Holdings!\n\n\nhttps://discord.gg/6JRtGawz7B")
            
    with gr.Row():
        with gr.Column():
            history = gr.Chatbot(elem_id = "chatbot")
            input = gr.Textbox(label = "Input", lines = 2)
            preinput = gr.Textbox(label = "Pre-Input", lines = 1)
            instruction = gr.Textbox(label = "Instruction", lines = 4)
            access_key = gr.Textbox(label = "Access Key", lines = 1)
            run = gr.Button("▶")
            clear = gr.Button("🗑️")
            cloud = gr.Button("☁️")
            
        with gr.Column():
            model = gr.Dropdown(choices = CHOICES, value = next(iter(API_ENDPOINTS)), interactive = True, label = "Model")
            temperature = gr.Slider( minimum = 0, maximum = 2, value = 1, step = 0.01, interactive = True, label = "Temperature" )
            top_p = gr.Slider( minimum = 0.01, maximum = 0.99, value = 0.95, step = 0.01, interactive = True, label = "Top P" )
            top_k = gr.Slider( minimum = 1, maximum = 2048, value = 50, step = 1, interactive = True, label = "Top K" )
            rep_p = gr.Slider( minimum = 0.01, maximum = 2, value = 1.2, step = 0.01, interactive = True, label = "Repetition Penalty" )
            max_tokens = gr.Slider( minimum = 1, maximum = 2048, value = 32, step = 64, interactive = True, label = "Max New Tokens" )
            stop_seqs = gr.Textbox(label = "Stop Sequences ( JSON Array / 4 Max )", lines = 1, value = '["‹", "›"]')
            seed = gr.Slider( minimum = 0, maximum = 8192, value = 42, step = 1, interactive = True, label = "Seed" )
            
    with gr.Row():
        with gr.Column():
            output = gr.Textbox(label = "Output", value = "", lines = 50)

    run.click(predict, inputs = [instruction, history, input, preinput, access_key, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed], outputs = [output, input])
    clear.click(clear, [], history)
    cloud.click(maintain_cloud, inputs = [], outputs = [input, output])
    
demo.queue(concurrency_count = 500, api_open = True).launch(show_api = True)