Update app.py

app.py

@@ -2,8 +2,10 @@ 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
 
-# Fetch and verify the Hugging Face token
 huggingface_token = os.getenv('NJOGERERA_TOKEN')
 if not huggingface_token:
     raise ValueError("Hugging Face token is not set in the environment variables.")
@@ -15,21 +17,55 @@ try:
 except Exception as e:
     raise ValueError("Failed to authenticate with the provided Hugging Face token.")
 
-# Load tokenizer and model manually with 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)
 
-# Create the pipeline
 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']
-    ksl_path = "KSL/abandon.mp4"
+    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
@@ -37,12 +73,13 @@ gr.Interface(
     fn=translate_lg_to_en,
     inputs=gr.Text(),
     outputs=[gr.Textbox(label="English Translation"), gr.Video(label="KSL Sign Language Animation")],
-    # outputs=gr.Textbox(label="English Translation"),
     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()