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EvaLingo / app.py

steve7909

clean up code, add global debug print

90deb69 3 months ago

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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)