Update app.py

app.py

@@ -1,42 +1,108 @@
+
 import os
 import gradio as gr
 import tempfile
 import soundfile as sf
 from models import Tokenizer, Kokoro
-from fastapi import FastAPI, Request
-from fastapi.responses import FileResponse
-import uvicorn
 
-# Initialize Kokoro components
+# Function to fetch available style vectors dynamically
+
+
 def get_style_vector_choices(directory="voices"):
-    if not os.path.exists(directory): return []
     return [file for file in os.listdir(directory) if file.endswith(".pt")]
 
+
 def get_onnx_models(directory="weights"):
-    if not os.path.exists(directory): return []
     return [file for file in os.listdir(directory) if file.endswith(".onnx")]
 
-def local_tts(text: str, model_path: str, style_vector: str, output_file_format: str = "wav", speed: float = 1.0):
+# Function to perform TTS using your local model
+
+
+def local_tts(
+        text: str,
+        model_path: str,
+        style_vector: str,
+        output_file_format: str = "wav",
+        speed: float = 1.0
+):
     if len(text) > 0:
         try:
             tokenizer = Tokenizer()
             style_vector_path = os.path.join("voices", style_vector)
-            model_full_path = os.path.join("weights", model_path)
+            model_path = os.path.join("weights", model_path)
+
+            inference = Kokoro(model_path, style_vector_path, tokenizer=tokenizer, lang='en-us')
 
-            inference = Kokoro(model_full_path, style_vector_path, tokenizer=tokenizer, lang='en-us')
             audio, sample_rate = inference.generate_audio(text, speed=speed)
 
             with tempfile.NamedTemporaryFile(suffix=f".{output_file_format}", delete=False) as temp_file:
                 sf.write(temp_file.name, audio, sample_rate)
-                return temp_file.name
+                temp_file_path = temp_file.name
+
+            return temp_file_path
+
         except Exception as e:
-            print(f"Inference Error: {e}")
-            return None
-    return None
+            raise gr.Error(f"An error occurred during TTS inference: {str(e)}")
+    else:
+        raise gr.Error("Input text cannot be empty.")
+
+
+# Get the list of available style vectors
+style_vector_choices = get_style_vector_choices()
+onnx_models_choices = get_onnx_models()
+
+# sample texts and their corresponding audio
+sample_outputs = [
+    ("Educational Note", "Machine learning models rely on large datasets and complex algorithms to identify patterns and make predictions.", "assets/edu_note.wav"),
+    ("Fun Fact", "Did you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still edible!", "assets/fun_fact.wav"),
+    ("Thanks", "Thank you for listening to this audio. It was generated by the Kokoro TTS model.", "assets/thanks.wav")
+]
+
+example_texts = [
+    ["Machine learning models rely on large datasets and complex algorithms to identify patterns and make predictions."],
+    ["Did you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still edible!"],
+    ["Thank you for listening to this audio. It was generated by the Kokoro TTS model."]
+]
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## <center> Kokoro TTS ONNX Inference | [GitHub Link](https://github.com/yakhyo/kokoro-onnx) </center>")
+
+    # Model-specific inputs
+    with gr.Row(variant="panel"):
+        model_path = gr.Dropdown(choices=onnx_models_choices, label="ONNX Model Path", value=onnx_models_choices[0])
+        style_vector = gr.Dropdown(choices=style_vector_choices, label="Style Vector", value=style_vector_choices[0])
+        output_file_format = gr.Dropdown(choices=["wav", "mp3"], label="Output Format", value="wav")
+        speed = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed")
+
+    # Text input and output
+    text = gr.Textbox(
+        label="Input Text",
+        placeholder="Enter text to convert to speech."
+    )
+    btn = gr.Button("Generate Speech")
+    output_audio = gr.Audio(label="Generated Audio", type="filepath")
+
+    # Link inputs and outputs
+    btn.click(
+        fn=local_tts,
+        inputs=[text, model_path, style_vector, output_file_format, speed],
+        outputs=output_audio
+    )
+
+    # Add example texts
+    gr.Examples(
+        examples=example_texts,
+        inputs=[text],
+        label="Click an example to populate the input text"
+    )
+
+    # Add example texts and audios
+    gr.Markdown("### Sample Texts and Audio")
+    for topic, sample_text, sample_audio in sample_outputs:
+        with gr.Row():
+            gr.Textbox(value=sample_text, label=topic, interactive=False)
+            gr.Audio(value=sample_audio, label="Example Audio", type="filepath", interactive=False)
 
-# --- FASTAPI SETUP ---
-app = FastAPI(title="Basyx TTS Hub")
+demo.launch(server_name="0.0.0.0")
 
-@app.post("/v1/audio/speech")
-async def api_tts(request: Request):
-    data = await request.json()