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# -*- 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}"}
# Global variable to control debug printing
DEBUG_MODE = True
def debug_print(*args, **kwargs):
if DEBUG_MODE:
print(*args, **kwargs)
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):
debug_print("Translating... ", input_text)
sentences = split_sentences_ginza(input_text) # split into sentences
translated_sentences = []
debug_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")
debug_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( # get translation from GPT
messages=[
{
"role": "user",
"content": prompt,
}
],
model="gpt-3.5-turbo",
temperature=0 # should be the same translation every time
)
translation = response.choices[0].message.content
debug_print("GPT translation:", translation)
return translation
def assess(original_japanese, student_translation):
try:
# get the English translation
generated_translation = translate_hf(original_japanese)
debug_print("Generated translation:", generated_translation)
except Exception as e:
return "Error in processing translation.", str(e)
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.")
debug_print(prompt)
# Evaluating the student's translation attempt
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt,
}
],
model="gpt-3.5-turbo",
)
debug_print("Full GPT response:", response)
debug_print("Generated translation:", generated_translation)
evaluation_feedback = response.choices[0].message.content
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)
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