# -*- coding: utf-8 -*-
"""translation practice.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1KrnodZGBZrUFdaJ9FIn8IhtWtCL7peoE
"""
import requests
import gradio as gr
from dotenv import load_dotenv
import os
from openai import OpenAI
import spacy

# Load environment variables from .env file
load_dotenv()

# Access the env
HF_TOKEN = os.getenv('HUGGING_FACE_TOKEN')

# openai setup
client = OpenAI(
  api_key=os.getenv('OPENAI_API_KEY')
)

# hugging face setup
#model_name = "mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf"
API_URL = f"https://api-inference.huggingface.co/models/"
#API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
headers = {"Authorization": f"Bearer {HF_TOKEN}"}

example_Japanese = '''こんにちは！

みなさん、私たちのプレゼンテーションにワクワクしていますか？
おなかがペコペコではありませんか？
では、ニコニコして、聞いてください！

今日のプレゼンテーションのテーマはオノマトペのくりかえすことばです。

そのようなことばをみなさん何か知っていますか？

日本語はくりかえすことばを毎日つかいます。気持ちやようすをよくひょうげんできるし、わかりやすいし、いんしょうにのこり やすいからです。たとえば、「ぴかぴか」と聞いたら、どうおもいますか？どんなイメージですか？やっぱりきれいやでんきのひかりですね。'''

example_English = '''Hello!

Are you all excited about our presentation?
Aren't you hungry?
So, smile and listen!

The theme of today's presentation is onomatopoeic repetition.

Do you know any such words?

In Japanese, we use repeated words every day. It's easy to understand, easy to understand, and easy to follow. For example, what do you think when you hear the word "pikapika"? What kind of image do you have? After all, it is a beautiful and electric light.'''

def split_sentences_ginza(input_text):
    nlp = spacy.load("ja_core_news_sm")
    doc = nlp(input_text)
    sentences = [sent.text for sent in doc.sents]
    return sentences

def query_hf(payload, model_name):
    # HTTP POST Request
    response = requests.post(API_URL+model_name, headers=headers, json=payload)

    return response.json()

def translate_hf(input_text):
    print("Translating... ", input_text)

    sentences = split_sentences_ginza(input_text)  # split into sentences
    translated_sentences = []

    print("Split sentences... ", sentences)

    for sentence in sentences:
        if sentence.strip():  # Ensure sentence is not empty
            # API Request for each sentence:
            response = query_hf({
                "inputs": sentence.strip(),
                "options": {"wait_for_model": True}
            }, "Helsinki-NLP/opus-mt-ja-en")

            print("response: ", response)
            translated_sentence = response[0]["translation_text"]
            translated_sentences.append(translated_sentence)

    # Join the translated sentences
    translation = ' '.join(translated_sentences)

    return translation


def translate_openai(input_text):

    prompt = "Translate the following text into Japanese language: " + input_text

    response = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="gpt-3.5-turbo",
        temperature=0 # should be the same every time
      )

    translation = response.choices[0].message.content

    print("GPT translation:", translation)

    return translation

def assess(original_japanese, student_translation):

    try:
      # get the English translation
      generated_translation = translate_hf(original_japanese)
    except Exception as e:
      return "Error in processing translation.", str(e)


      print("Generated translation:", generated_translation)
    try:
      prompt = (f"Evaluate the student's English translation of Japanese for accuracy and naturalness. "
            f"Original: {original_japanese}, "
            f"Reference Translation: {generated_translation}, "
            f"Student Translation: {student_translation}. "
            "Highlight errors, suggest improvements, and note any nuances. Provide concise and very simple feedback for an English language learner aimed at improving their translation skills. Where possible, give concrete examples.")

      print(prompt)

      # Evaluating the student's translation attempt
      response = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="gpt-3.5-turbo",
      )
      print("Full GPT response:", response)

      evaluation_feedback = response.choices[0].message.content

      print("GPT feedback:", evaluation_feedback)
      return generated_translation, evaluation_feedback
    except Exception as e:
      return "Error in processing evaluation.", str(e)

assessor = gr.Interface(fn=assess,
                          inputs=[
                              gr.Textbox(label="Japanese Sentence Input", placeholder="Input text to be translated", lines=1, value="これは例です"),#example_Japanese),#"
                              gr.Textbox(label="Student's Translation Attempt", placeholder="Input your English translation", lines=1, value="This is an example")#"This is an example")
                          ],
                          outputs=[
                              gr.Textbox(label="Machine Generated Translation"),
                              gr.Textbox(label="Evaluation Feedback")
                          ],
                          title="Translation Practice",
                          description="Enter a Japanese sentence and your English translation attempt to receive evaluation feedback."
                          )

assessor.launch(debug=True, share=True)

#assessor.launch(debug=True)