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