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import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from huggingface_hub import HfApi
import string
import os
from moviepy.editor import VideoFileClip, concatenate_videoclips, ImageClip
huggingface_token = os.getenv('NJOGERERA_TOKEN')
if not huggingface_token:
raise ValueError("Hugging Face token is not set in the environment variables")
api = HfApi()
try:
user_info = api.whoami(token=huggingface_token)
print(f"Logged in as: {user_info['name']}")
except Exception as e:
raise ValueError("Failed to authenticate with the provided Hugging Face token.")
model_path = "vertigo23/njogerera_translation_model_V003"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token=huggingface_token)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path, use_auth_token=huggingface_token)
translator = pipeline("translation", model=model, tokenizer=tokenizer)
prefix = "translate Luganda to English: "
filler_image_path = "alphabet/break.png"
def clean_and_split(text):
text = text.lower().translate(str.maketrans('', '', string.punctuation))
return text.split()
def map_word_to_media(word):
if os.path.exists(f"KSL/{word}.mp4"):
return [f"KSL/{word}.mp4"]
else:
spelled_word_media = [filler_image_path]
spelled_word_media += [f"alphabet/{letter}.png" for letter in word if os.path.exists(f"alphabet/{letter}.png")]
spelled_word_media.append(filler_image_path)
return spelled_word_media
def stitch_media(media_paths):
clips = []
for path in media_paths:
if path.endswith('.mp4'):
clips.append(VideoFileClip(path))
elif path.endswith('.png'):
image_clip = ImageClip(path).set_duration(0.7)
clips.append(image_clip)
if not clips:
raise ValueError("No media files to stitch.")
final_clip = concatenate_videoclips(clips, method="compose")
final_clip.fps = 24
final_clip_path = "KSL/final_translation.mp4"
final_clip.write_videofile(final_clip_path, codec="libx264", fps=24)
return final_clip_path
def translate_lg_to_en(text):
lg_input = prefix + text
translated_text = translator(lg_input)
english_translation = translated_text[0]['translation_text']
words = clean_and_split(english_translation)
media_paths = []
for word in words:
media_paths.extend(map_word_to_media(word))
ksl_path = stitch_media(media_paths)
return english_translation, ksl_path
# Gradio interface
gr.Interface(
fn=translate_lg_to_en,
inputs=gr.Text(),
outputs=[gr.Textbox(label="English Translation"), gr.Video(label="KSL Sign Language Animation")],
title="Njogerera Translation App",
description="Type in a Luganda sentence and see the translation.",
article="Above is some sample text to test the results of the model. Click to see the results.",
examples=[
["Ebikolwa ebitali bya buntu tebikkirizibwa mu kitundu."],
["Olugudo olugenda e Masaka lugadwawo."],
["Abalwadde ba Malaria mu dwaliro lye Nsambya bafunye obujanjabi."],
],
allow_flagging="never"
).launch() |