updates

app.py

@@ -2,6 +2,9 @@ import cv2
 import numpy as np
 import gradio as gr
 
+# import os
+# os.chdir('modeling')
+
 import tensorflow as tf, tf_keras
 import tensorflow_hub as hub
 from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
@@ -68,11 +71,11 @@ def translate(video_file):
     
     translation = tokenizer.batch_decode(tokens, skip_special_tokens=True)
 
+    # Return dict {label:pred}
     return {"translation":translation}
 
 # Gradio App config
 title = "ASL Translation (MoViNet + T5)"
-
 examples = [
         ["videos/My_second_ASL_professors_name_was_Will_White.mp4",],
         ['videos/You_are_my_sunshine.mp4'],
@@ -81,31 +84,19 @@ examples = [
         ['videos/all.mp4']
         ['videos/white.mp4']
     ]
-
 # examples = [
-#         ["videos/My_second_ASL_professors_name_was_Will_White.mp4", "My second ASL professor's name was Will White"],
-#         ['videos/You_are_my_sunshine.mp4', 'You are my sunshine'],
-#         ['videos/scrub_your_hands_for_at_least_20_seconds.mp4', 'scrub your hands for at least 20 seconds'],
+#         ['videos/all.mp4', 'all'],
+#         ['videos/white.mp4', 'white'],
+#         ['videos/before.mp4', 'before'],
+#         ['videos/blue.mp4', 'blue'],
 #         ['videos/no.mp4', 'no'],
-#         ['videos/all.mp4', 'all']
-#         ['videos/white.mp4', 'white']
+#         ['videos/accident2.mp4', 'accident']
 #     ]
 
-description =   "Gradio demo of word-level sign language classification using I3D model pretrained on the WLASL video dataset. " \
-                "WLASL is a large-scale dataset containing more than 2000 words in American Sign Language. " \
-                "Examples used in the demo are videos from the the test subset. "  \
-                "Note that WLASL100 contains 100 words while WLASL2000 contains 2000."
-
-
-article =   "More information about the trained models can be found <a href=https://github.com/deanna-emery/ASL-Translator/>here</a>."
-
-
 # Gradio App interface
 gr.Interface(fn=translate,
               inputs="video", 
               outputs="text",
               allow_flagging="never",
               title=title, 
-              description=description,
-              examples=examples,
-              article=article).launch()
+              examples=examples).launch()