Update app.py

app.py

@@ -19,7 +19,7 @@ def forward_gpu(ps, ref_s, speed):
     return models[True](ps, ref_s, speed)
 
 def generate_first(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
-    text = text
+    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
     pipeline = pipelines[voice[0]]
     pack = pipeline.load_voice(voice)
     use_gpu = use_gpu and CUDA_AVAILABLE
@@ -50,7 +50,7 @@ def tokenize_first(text, voice='af_heart'):
     return words  # Return a list of words
 
 def generate_all(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
-    text = text
+    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
     pipeline = pipelines[voice[0]]
     pack = pipeline.load_voice(voice)
     use_gpu = use_gpu and CUDA_AVAILABLE
@@ -163,7 +163,9 @@ with gr.Blocks() as generate_tab:
         predict_btn = gr.Button('Predict', variant='secondary', visible=False)
 
 BANNER_TEXT = '''
-SureTalk AI U8-tts
+[***Kokoro*** **is an open-weight TTS model with 82 million parameters.**](https://huggingface.co/hexgrad/Kokoro-82M)
+This is our work on Kokoro TTS [**V1 Model GPU**](https://shukdevdatta123-kokoro-tts-translate-gpu.hf.space) and the next version Kokoro TTS [**V2 Model CPU**](https://shukdevdatta123-kokoro-tts.hf.space).
+If you would like to use our V2 Model with GPU, then go to this [link](https://colab.research.google.com/drive/1DIpBzJSBBeTcpkyxkHcpngLumMapEWQz?usp=sharing).
 '''
 
 API_OPEN = os.getenv('SPACE_ID') != 'hexgrad/Kokoro-TTS'
@@ -173,38 +175,22 @@ with gr.Blocks() as app:
         gr.Markdown(BANNER_TEXT, container=True)
     with gr.Row():
         with gr.Column():
-            text = gr.Textbox(
-                label='Input Text', 
-                info=f"Up to ~5000 characters per Generate"
-            )
+            text = gr.Textbox(label='Input Text', info=f"Up to ~500 characters per Generate, or {'∞' if CHAR_LIMIT is None else CHAR_LIMIT} characters per Stream")
             with gr.Row():
-                voice = gr.Dropdown(
-                    list(CHOICES.items()), 
-                    value='af_heart', 
-                    label='Voice', 
-                    info='Choose from our wide range of natural-sounding voices'
+                voice = gr.Dropdown(list(CHOICES.items()), value='af_heart', label='Voice', info='Quality and availability vary by language')
+                use_gpu = gr.Dropdown(
+                    [('ZeroGPU 🚀', True), ('CPU 🐌', False)],
+                    value=CUDA_AVAILABLE,
+                    label='Hardware',
+                    info='GPU is usually faster, but has a usage quota',
+                    interactive=CUDA_AVAILABLE
                 )
-                # Remove the GPU choice dropdown and always use GPU if available
-                use_gpu = CUDA_AVAILABLE  # This will be used internally
-            speed = gr.Slider(
-                minimum=0.5, 
-                maximum=2, 
-                value=1, 
-                step=0.1, 
-                label='Speech Rate'
-            )
-            
+            speed = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.1, label='Speed')
+            random_btn = gr.Button('Random Text', variant='secondary')
         with gr.Column():
-           gr.TabbedInterface([generate_tab], ['Generate Audio'])
-
-    # Keep the button click handlers but remove use_gpu from the visible inputs
-    
-    generate_btn.click(
-        fn=generate_first, 
-        inputs=[text, voice, speed], 
-        outputs=[out_audio, out_ps], 
-        api_name=API_NAME
-    )
+            gr.TabbedInterface([generate_tab], ['Generate'])
+    random_btn.click(fn=get_random_text, inputs=[voice], outputs=[text], api_name=API_NAME)
+    generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps], api_name=API_NAME)
     tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps], api_name=API_NAME)
     predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio], api_name=API_NAME)