Fixing nonlocal assignment

app.py

@@ -24,7 +24,7 @@ _PROMPTS: tuple[str] = (
 _TORCH_DEVICE = (
     torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
 )
-_ANSWERS = []
+_ANSWERS: list[tuple[str, str]] = []
 
 _WATERMARK_CONFIG_DICT = dict(
     ngram_len=5,
@@ -116,7 +116,7 @@ def generate_outputs(
 
 with gr.Blocks() as demo:
   gr.Markdown(
-    f'''
+    '''
     # Using SynthID Text in your Genreative AI projects
 
     [SynthID][synthid] is a Google DeepMind technology that watermarks and
@@ -156,12 +156,18 @@ with gr.Blocks() as demo:
     configuration values affect performance.
 
     Watermarks are [configured][synthid-hf-config] to parameterize the
-    _g_-function and how it is applied during generation. We use the following
-    configuration for all demos. It should not be used for any production
-    purposes.
+    _g_-function and how it is applied during generation. The following
+    configuration is used for all demos. It should not be used for any
+    production purposes.
 
     ```json
-    {json.dumps(_WATERMARK_CONFIG_DICT, indent=2)}
+    {
+        "ngram_len": 5,
+        "keys": [654, 400, 836, 123, 340, 443, 597, 160, 57,29, 590, 639, 13,715, 468, 990, 966, 226, 324, 585, 118, 504, 421, 521, 129, 669, 732, 225, 90, 960],
+        "sampling_table_size": 65536,
+        "sampling_table_seed": 0,
+        "context_history_size": 1024
+    }
     ```
 
     Watermarks are applied by initializing a `SynthIDTextWatermarkingConfig`
@@ -297,6 +303,7 @@ with gr.Blocks() as demo:
     reset_btn = gr.Button('Reset', visible=False)
 
   def generate(*prompts):
+    prompts = [p for p in prompts if p]
     standard, standard_detector = generate_outputs(prompts=prompts)
     watermarked, watermarked_detector = generate_outputs(
         prompts=prompts,
@@ -313,10 +320,16 @@ with gr.Blocks() as demo:
       else:
         return 'Not watermarked'
 
-    responses = [(text, decision(score)) for text, score in zip(standard, standard_detector[0])]
-    responses += [(text, decision(score)) for text, score in zip(watermarked, watermarked_detector[0])]
+    responses = [
+        (text, decision(score))
+        for text, score in zip(standard, standard_detector[0])
+    ]
+    responses += [
+        (text, decision(score))
+        for text, score in zip(watermarked, watermarked_detector[0])
+    ]
     random.shuffle(responses)
-    _ANSWERS = responses
+    _ANSWERS.extend(responses)
 
     # Load model
     return {
@@ -370,6 +383,8 @@ with gr.Blocks() as demo:
   )
 
   def reset():
+    _ANSWERS.clear()
+
     return {
         generations_col: gr.Column(visible=False),
         detections_col: gr.Column(visible=False),