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import gradio as gr
from simplemma import simple_tokenizer
from difflib import Differ
from icecream import ic
from app.webui.patch import model_load,num_tokens_in_string,one_chunk_initial_translation, one_chunk_reflect_on_translation, one_chunk_improve_translation
from app.webui.patch import calculate_chunk_size, multichunk_initial_translation, multichunk_reflect_on_translation, multichunk_improve_translation
from llama_index.core.node_parser import SentenceSplitter
def tokenize(text):
# Use nltk to tokenize the text
words = simple_tokenizer(text)
# Check if the text contains spaces
if ' ' in text:
# Create a list of words and spaces
tokens = []
for word in words:
tokens.append(word)
if not word.startswith("'") and not word.endswith("'"): # Avoid adding space after punctuation
tokens.append(' ') # Add space after each word
return tokens[:-1] # Remove the last space
else:
return words
def diff_texts(text1, text2):
tokens1 = tokenize(text1)
tokens2 = tokenize(text2)
d = Differ()
diff_result = list(d.compare(tokens1, tokens2))
highlighted_text = []
for token in diff_result:
word = token[2:]
category = None
if token[0] == '+':
category = 'added'
elif token[0] == '-':
category = 'removed'
elif token[0] == '?':
continue # Ignore the hints line
highlighted_text.append((word, category))
return highlighted_text
#modified from src.translaation-agent.utils.tranlsate
def translator(
source_lang: str,
target_lang: str,
source_text: str,
country: str,
max_tokens:int = 1000,
):
"""Translate the source_text from source_lang to target_lang."""
num_tokens_in_text = num_tokens_in_string(source_text)
ic(num_tokens_in_text)
if num_tokens_in_text < max_tokens:
ic("Translating text as single chunk")
#Note: use yield from B() if put yield in function B()
init_translation = one_chunk_initial_translation(
source_lang, target_lang, source_text
)
reflection = one_chunk_reflect_on_translation(
source_lang, target_lang, source_text, init_translation, country
)
final_translation = one_chunk_improve_translation(
source_lang, target_lang, source_text, init_translation, reflection
)
return init_translation, reflection, final_translation
else:
ic("Translating text as multiple chunks")
token_size = calculate_chunk_size(
token_count=num_tokens_in_text, token_limit=max_tokens
)
ic(token_size)
#using sentence splitter
text_parser = SentenceSplitter(
chunk_size=token_size,
)
source_text_chunks = text_parser.split_text(source_text)
translation_1_chunks = multichunk_initial_translation(
source_lang, target_lang, source_text_chunks
)
init_translation = "".join(translation_1_chunks)
reflection_chunks = multichunk_reflect_on_translation(
source_lang,
target_lang,
source_text_chunks,
translation_1_chunks,
country,
)
reflection = "".join(reflection_chunks)
translation_2_chunks = multichunk_improve_translation(
source_lang,
target_lang,
source_text_chunks,
translation_1_chunks,
reflection_chunks,
)
final_translation = "".join(translation_2_chunks)
return init_translation, reflection, final_translation
def translator_sec(
endpoint2: str,
model2: str,
api_key2: str,
context_window: int,
num_output: int,
source_lang: str,
target_lang: str,
source_text: str,
country: str,
max_tokens: int = 1000,
):
"""Translate the source_text from source_lang to target_lang."""
num_tokens_in_text = num_tokens_in_string(source_text)
ic(num_tokens_in_text)
if num_tokens_in_text < max_tokens:
ic("Translating text as single chunk")
#Note: use yield from B() if put yield in function B()
init_translation = one_chunk_initial_translation(
source_lang, target_lang, source_text
)
try:
model_load(endpoint2, model2, api_key2, context_window, num_output)
except Exception as e:
raise gr.Error(f"An unexpected error occurred: {e}")
reflection = one_chunk_reflect_on_translation(
source_lang, target_lang, source_text, init_translation, country
)
final_translation = one_chunk_improve_translation(
source_lang, target_lang, source_text, init_translation, reflection
)
return init_translation, reflection, final_translation
else:
ic("Translating text as multiple chunks")
token_size = calculate_chunk_size(
token_count=num_tokens_in_text, token_limit=max_tokens
)
ic(token_size)
#using sentence splitter
text_parser = SentenceSplitter(
chunk_size=token_size,
)
source_text_chunks = text_parser.split_text(source_text)
translation_1_chunks = multichunk_initial_translation(
source_lang, target_lang, source_text_chunks
)
init_translation = "".join(translation_1_chunks)
try:
model_load(endpoint2, model2, api_key2, context_window, num_output)
except Exception as e:
raise gr.Error(f"An unexpected error occurred: {e}")
reflection_chunks = multichunk_reflect_on_translation(
source_lang,
target_lang,
source_text_chunks,
translation_1_chunks,
country,
)
reflection = "".join(reflection_chunks)
translation_2_chunks = multichunk_improve_translation(
source_lang,
target_lang,
source_text_chunks,
translation_1_chunks,
reflection_chunks,
)
final_translation = "".join(translation_2_chunks)
return init_translation, reflection, final_translation |