Create app.py

app.py

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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()