# Importing libraries
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
from llama_cpp import Llama
import gradio as gr
import psutil

# Initing things                
print("! DOWNLOADING TOKENIZER AND SETTING ALL UP !")
translator_tokenizer = M2M100Tokenizer.from_pretrained(            # tokenizer for translator
    "facebook/m2m100_418M", cache_dir="translator/"
)
print("! DOWNLOADING MODEL AND SETTING ALL UP !")
translator_model = M2M100ForConditionalGeneration.from_pretrained( # translator model
    "facebook/m2m100_418M", cache_dir="translator/"
)
print("! SETTING MODEL IN EVALUATION MODE !")
translator_model.eval()
print("! INITING LLAMA MODEL !")
llm = Llama(model_path="./model.bin")                              # LLaMa model
llama_model_name = "TheBloke/WizardLM-1.0-Uncensored-Llama2-13B-GGUF" 
print("! INITING DONE !")

# Preparing things to work
translator_tokenizer.src_lang = "en"
title = "llama.cpp API"
desc = '''<h1>Hello, world!</h1>
This is showcase how to make own server with Llama2 model.<br>
I'm using here 13b model just for example. Also here's only CPU power.<br>
But you can use GPU power as well!<br><br>
<h1>How to GPU?</h1>
Change <code>`CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS`</code> in Dockerfile on <code>`CMAKE_ARGS="-DLLAMA_CUBLAS=on"`</code>. Also you can try <code>`DLLAMA_CLBLAST`</code> or <code>`DLLAMA_METAL`</code>.<br><br>
<h1>How to test it on own machine?</h1>
You can install Docker, build image and run it. I made <code>`run-docker.sh`</code> for ya. To stop container run <code>`docker ps`</code>, find name of container and run <code>`docker stop _dockerContainerName_`</code><br>
Or you can once follow steps in Dockerfile and try it on your machine, not in Docker.<br>
<br>''' + f"Memory used: {psutil.virtual_memory()[2]}<br>" + '''
Powered by <a href="https://github.com/abetlen/llama-cpp-python">llama-cpp-python</a> and <a href="https://www.gradio.app/">Gradio</a>.<br><br>'''

'''
    # Defining languages for translator (i just chose popular on my opinion languages!!!)
    ru - Russian
    uk - Ukranian
    zh - Chinese
    de - German
    fr - French
    hi - Hindi
    it - Italian
    ja - Japanese
    es - Spanish
    ar - Arabic
'''
languages = ["ru", "uk", "zh", "de", "fr", "hi", "it", "ja", "es", "ar"]

# Loading prompt
with open('system.prompt', 'r', encoding='utf-8') as f:
    prompt = f.read()

def generate_answer(request: str, max_tokens: int = 256, language: str = "en", custom_prompt: str = None):
    logs = f"Request: {request}\nMax tokens: {max_tokens}\nLanguage: {language}\nCustom prompt: {custom_prompt}\n"
    try:
        maxTokens = max_tokens if 16 <= max_tokens <= 256 else 64
        if isinstance(custom_prompt, str):
            userPrompt = custom_prompt + "\n\nUSER: " + request + "\nASSISTANT: "
        else:
            userPrompt = prompt + "\n\nUSER: " + request + "\nASSISTANT: "
        logs += f"\nFinal prompt: {userPrompt}\n"
    except:
        return "Not enough data! Check that you passed all needed data.", logs
    
    try:
        # this shitty fix will be until i willnt figure out why sometimes there is empty output
        counter = 1
        while True:
            logs += f"Attempt {counter} to generate answer...\n"
            output = llm(userPrompt, max_tokens=maxTokens, stop=["User:"], echo=False)
            text = output["choices"][0]["text"]
            if len(text.strip()) > 1 and text.strip() not in ['', None, ' ']:
                break
            counter += 1
        logs += f"Final attempt: {counter}\n"
                
        if language in languages:
            logs += f"\nTranslating from en to {language}"
            encoded_input = translator_tokenizer(text, return_tensors="pt")
            generated_tokens = translator_model.generate(
                **encoded_input, forced_bos_token_id=translator_tokenizer.get_lang_id(language)
            )
            translated_text = translator_tokenizer.batch_decode(
                generated_tokens, skip_special_tokens=True
            )[0]
            logs += f"\nTranslated: {translated_text}\nOriginal: {text}"
            return translated_text, logs
        logs += f"\nOriginal: {text}"
        return text, logs
    except Exception as e:
        print(e)
        return "Oops! Internal server error. Check the logs of space/instance.", logs
    print("\n\n\n")

print("! LOAD GRADIO INTERFACE !")
demo = gr.Interface(
    fn=generate_answer,
    inputs=[
        gr.components.Textbox(label="Input"),
        gr.components.Number(value=256),
        gr.components.Dropdown(label="Target Language", value="en", choices=["en"]+languages),
        gr.components.Textbox(label="Custom system prompt"),
    ],
    outputs=[
        gr.components.Textbox(label="Output"),
        gr.components.Textbox(label="Logs")
    ],
    title=title,
    description=desc,
    allow_flagging='never'
)
demo.queue()
print("! LAUNCHING GRADIO !")
demo.launch(server_name="0.0.0.0")