File size: 2,985 Bytes
e6d08d8 2d553a1 5d3ead6 2d553a1 5d3ead6 2d553a1 e6d08d8 2d553a1 e6d08d8 5d3ead6 e6d08d8 2d553a1 e6d08d8 2d553a1 e6d08d8 2d553a1 e6d08d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
import gradio as gr
from transformers import (
MBart50TokenizerFast,
MBartForConditionalGeneration,
AutoTokenizer,
AutoModelForSequenceClassification,
)
import torch
# Load the language detection model
lang_detector_name = "Aesopskenya/LanguageDetector"
lang_tokenizer = AutoTokenizer.from_pretrained(lang_detector_name)
lang_model = AutoModelForSequenceClassification.from_pretrained(lang_detector_name)
# Define the language mapping to models
lang_to_model = {
"Gikuyu": "Aesopskenya/translator",
"Kalenjin": "Aesopskenya/KalenjinTranslator",
"Kamba": "Aesopskenya/KambaTranslation",
"Luo": "Aesopskenya/LuoTranslator",
"Sheng": "Aesopskenya/ShengTranslation",
}
# Reverse mapper for language detection
reverse_mapper = {
0: "English",
1: "Sheng",
2: "Other",
3: "Luhya",
4: "Kamba",
5: "Gikuyu",
6: "Kalenjin",
7: "Luo",
}
# Function to detect language
def detect_language(text):
inputs = lang_tokenizer(
text,
max_length=128,
padding=True,
truncation=True,
return_tensors="pt",
)
with torch.no_grad():
outputs = lang_model(**inputs)
logits = outputs.logits
prediction = torch.argmax(logits, dim=-1).item()
return reverse_mapper[prediction]
# Function to load the appropriate model and tokenizer
def load_model_and_tokenizer(language):
model_name = lang_to_model.get(language)
if model_name:
tokenizer = MBart50TokenizerFast.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
return tokenizer, model
return None, None
# Function to translate text
def translate_text(text):
# Detect the language
detected_language = detect_language(text)
print(f"Detected Language: {detected_language}") # Print detected language for the app output
if detected_language not in lang_to_model:
return f"Detected Language: {detected_language}. Language not supported for translation."
# Load the appropriate model and tokenizer
tokenizer, model = load_model_and_tokenizer(detected_language)
if not tokenizer or not model:
return "Error loading the translation model."
# Tokenize input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
# Generate translation
outputs = model.generate(inputs.input_ids, max_length=128)
# Decode output
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
return f"Detected Language: {detected_language}\nTranslation: {translation}"
# Define Gradio interface
iface = gr.Interface(
fn=translate_text,
inputs="text",
outputs="text",
title="Multi-Language Translator",
description="Enter a sentence, and the model will detect its language and translate it into English.",
)
# Launch the app
if __name__ == "__main__":
iface.launch(server_name="0.0.0.0", server_port=7860)
|