'''
        Created By Lewis Kamau Kimaru 
        Sema translator api backend
        January 2024
        Docker deployment
'''

from fastapi import FastAPI, HTTPException, Request
from fastapi_middleware import Middleware
from fastapi_middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
import gradio as gr
import ctranslate2
import sentencepiece as spm
import fasttext
import uvicorn
import pytz
from datetime import datetime
import os

app = FastAPI()

origins = ["*"]

app.add_middleware(
    Middleware(
        CORSMiddleware,
        allow_origins=origins,
        allow_credentials=False,
        allow_methods=["*"],
        allow_headers=["*"],
    )
)

fasttext.FastText.eprint = lambda x: None

# Get time of request

def get_time():
    nairobi_timezone = pytz.timezone('Africa/Nairobi')
    current_time_nairobi = datetime.now(nairobi_timezone)
    
    curr_day = current_time_nairobi.strftime('%A')
    curr_date = current_time_nairobi.strftime('%Y-%m-%d')
    curr_time = current_time_nairobi.strftime('%H:%M:%S')
    
    full_date = f"{curr_day} | {curr_date} | {curr_time}"
    return full_date, curr_time
    
# Load the model and tokenizer ..... only once!
beam_size = 1  # change to a smaller value for faster inference
device = "cpu"  # or "cuda"

# Language Prediction model
print("\nimporting Language Prediction model")
lang_model_file = "lid218e.bin"
lang_model_full_path = os.path.join(os.path.dirname(__file__), lang_model_file)
lang_model = fasttext.load_model(lang_model_full_path)


# Load the source SentencePiece model
print("\nimporting SentencePiece model")
sp_model_file = "spm.model"
sp_model_full_path = os.path.join(os.path.dirname(__file__), sp_model_file)
sp = spm.SentencePieceProcessor()
sp.load(sp_model_full_path)

# Import The Translator model
print("\nimporting Translator model")
ct_model_file = "sematrans-3.3B"
ct_model_full_path = os.path.join(os.path.dirname(__file__), ct_model_file)
translator = ctranslate2.Translator(ct_model_full_path, device)

print('\nDone importing models\n')

    
def translate_detect(userinput: str, target_lang: str):
    source_sents = [userinput]
    source_sents = [sent.strip() for sent in source_sents]
    target_prefix = [[target_lang]] * len(source_sents)

    # Predict the source language
    predictions = lang_model.predict(source_sents[0], k=1)
    source_lang = predictions[0][0].replace('__label__', '')

    # Subword the source sentences
    source_sents_subworded = sp.encode(source_sents, out_type=str)
    source_sents_subworded = [[source_lang] + sent + ["</s>"] for sent in source_sents_subworded]

    # Translate the source sentences
    translations = translator.translate_batch(
        source_sents_subworded,
        batch_type="tokens",
        max_batch_size=2024,
        beam_size=beam_size,
        target_prefix=target_prefix,
    )
    translations = [translation[0]['tokens'] for translation in translations]

    # Desubword the target sentences
    translations_desubword = sp.decode(translations)
    translations_desubword = [sent[len(target_lang):] for sent in translations_desubword]

    # Return the source language and the translated text
    return source_lang, translations_desubword

def translate_enter(userinput: str, source_lang: str, target_lang: str):  
  source_sents = [userinput]
  source_sents = [sent.strip() for sent in source_sents]
  target_prefix = [[target_lang]] * len(source_sents)

  # Subword the source sentences
  source_sents_subworded = sp.encode(source_sents, out_type=str)
  source_sents_subworded = [[source_lang] + sent + ["</s>"] for sent in source_sents_subworded]

  # Translate the source sentences
  translations = translator.translate_batch(source_sents_subworded, batch_type="tokens", max_batch_size=2024, beam_size=beam_size, target_prefix=target_prefix)
  translations = [translation[0]['tokens'] for translation in translations]

  # Desubword the target sentences
  translations_desubword = sp.decode(translations)
  translations_desubword = [sent[len(target_lang):] for sent in translations_desubword]

  # Return the source language and the translated text
  return translations_desubword[0]


@app.get("/")
async def read_root():
    gradio_interface = """
    <html>
    <meta name="viewport" content="width=device-width, height=device-height, initial-scale=1.0">
    <head>
        <title>Sema</title>
    </head>
    <frameset>
        <frame src=https://kamau1-semaapi-frontend.hf.space/?embedded=true'>
    </frameset>
    </html>
    """
    return HTMLResponse(content=gradio_interface)


@app.post("/translate_detect/")
async def translate_detect_endpoint(request: Request):
    datad = await request.json()
    userinputd = datad.get("userinput")
    target_langd = datad.get("target_lang")
    dfull_date = get_time()[0]
    print(f"\nrequest: {dfull_date}\nTarget Language; {target_langd}, User Input: {userinputd}\n")

    if not userinputd or not target_langd:
        raise HTTPException(status_code=422, detail="Both 'userinput' and 'target_lang' are required.")

    source_langd, translated_text_d = translate_detect(userinputd, target_langd)
    dcurrent_time = get_time()[1]
    print(f"\nresponse: {dcurrent_time}; ... Source_language: {source_langd}, Translated Text: {translated_text_d}\n\n")
    return {
        "source_language": source_langd,
        "translated_text": translated_text_d[0],
    }


@app.post("/translate_enter/")
async def translate_enter_endpoint(request: Request):
    datae = await request.json()
    userinpute = datae.get("userinput")
    source_lange = datae.get("source_lang")
    target_lange = datae.get("target_lang")
    efull_date = get_time()[0]
    print(f"\nrequest: {efull_date}\nSource_language; {source_lange}, Target Language; {target_lange}, User Input: {userinpute}\n")

    if not userinpute or not target_lange:
        raise HTTPException(status_code=422, detail="'userinput' 'sourc_lang'and 'target_lang' are required.")

    translated_text_e = translate_enter(userinpute, source_lange, target_lange)
    ecurrent_time = get_time()[1]
    print(f"\nresponse: {ecurrent_time}; ... Translated Text: {translated_text_e}\n\n")
    return {
        "translated_text": translated_text_e,
    }


print("\nAPI starting .......\n")