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fix: remove unused imports and apply ruff formatting

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- Remove unused imports: json (llm.py), tempfile (stt.py), base64 (tts.py)
- Apply ruff format to all Python files
This commit is contained in:
Billy Davies
2026-02-18 18:36:16 -05:00
parent 0cc03aa145
commit 8552a02a25
5 changed files with 283 additions and 246 deletions
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58
embeddings.py
View File

@@ -9,6 +9,7 @@ Features:
- MLflow metrics logging - MLflow metrics logging
- Visual embedding dimension display - Visual embedding dimension display
""" """
import os import os
import time import time
import logging import logging
@@ -28,7 +29,7 @@ logger = logging.getLogger("embeddings-demo")
EMBEDDINGS_URL = os.environ.get( EMBEDDINGS_URL = os.environ.get(
"EMBEDDINGS_URL", "EMBEDDINGS_URL",
# Default: Ray Serve Embeddings endpoint # Default: Ray Serve Embeddings endpoint
"http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/embeddings" "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/embeddings",
) )
# ─── MLflow experiment tracking ────────────────────────────────────────── # ─── MLflow experiment tracking ──────────────────────────────────────────
try: try:
@@ -59,7 +60,9 @@ try:
_mlflow_run_id = _mlflow_run.info.run_id _mlflow_run_id = _mlflow_run.info.run_id
_mlflow_step = 0 _mlflow_step = 0
MLFLOW_ENABLED = True MLFLOW_ENABLED = True
logger.info("MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id) logger.info(
"MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id
)
except Exception as exc: except Exception as exc:
logger.warning("MLflow tracking disabled: %s", exc) logger.warning("MLflow tracking disabled: %s", exc)
_mlflow_client = None _mlflow_client = None
@@ -68,7 +71,9 @@ except Exception as exc:
MLFLOW_ENABLED = False MLFLOW_ENABLED = False
def _log_embedding_metrics(latency: float, batch_size: int, embedding_dims: int = 0) -> None: def _log_embedding_metrics(
latency: float, batch_size: int, embedding_dims: int = 0
) -> None:
"""Log embedding inference metrics to MLflow (non-blocking best-effort).""" """Log embedding inference metrics to MLflow (non-blocking best-effort)."""
global _mlflow_step global _mlflow_step
if not MLFLOW_ENABLED or _mlflow_client is None: if not MLFLOW_ENABLED or _mlflow_client is None:
@@ -81,8 +86,15 @@ def _log_embedding_metrics(latency: float, batch_size: int, embedding_dims: int
metrics=[ metrics=[
mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step), mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step),
mlflow.entities.Metric("batch_size", batch_size, ts, _mlflow_step), mlflow.entities.Metric("batch_size", batch_size, ts, _mlflow_step),
mlflow.entities.Metric("embedding_dims", embedding_dims, ts, _mlflow_step), mlflow.entities.Metric(
mlflow.entities.Metric("latency_per_text_ms", (latency * 1000 / batch_size) if batch_size > 0 else 0, ts, _mlflow_step), "embedding_dims", embedding_dims, ts, _mlflow_step
),
mlflow.entities.Metric(
"latency_per_text_ms",
(latency * 1000 / batch_size) if batch_size > 0 else 0,
ts,
_mlflow_step,
),
], ],
) )
except Exception: except Exception:
@@ -98,8 +110,7 @@ def get_embeddings(texts: list[str]) -> tuple[list[list[float]], float]:
start_time = time.time() start_time = time.time()
response = client.post( response = client.post(
f"{EMBEDDINGS_URL}/embeddings", f"{EMBEDDINGS_URL}/embeddings", json={"input": texts, "model": "bge"}
json={"input": texts, "model": "bge"}
) )
response.raise_for_status() response.raise_for_status()
@@ -228,7 +239,11 @@ def batch_embed(texts_input: str) -> tuple[str, str]:
embeddings, latency = get_embeddings(texts) embeddings, latency = get_embeddings(texts)
# Log to MLflow # Log to MLflow
_log_embedding_metrics(latency, batch_size=len(embeddings), embedding_dims=len(embeddings[0]) if embeddings else 0) _log_embedding_metrics(
latency,
batch_size=len(embeddings),
embedding_dims=len(embeddings[0]) if embeddings else 0,
)
status = f"✅ Generated {len(embeddings)} embeddings in {latency * 1000:.1f}ms" status = f"✅ Generated {len(embeddings)} embeddings in {latency * 1000:.1f}ms"
status += f" ({latency * 1000 / len(texts):.1f}ms per text)" status += f" ({latency * 1000 / len(texts):.1f}ms per text)"
@@ -306,7 +321,7 @@ Generate embeddings, compare text similarity, and explore vector representations
single_input = gr.Textbox( single_input = gr.Textbox(
label="Input Text", label="Input Text",
placeholder="Enter text to generate embeddings...", placeholder="Enter text to generate embeddings...",
lines=3 lines=3,
) )
single_btn = gr.Button("Generate Embedding", variant="primary") single_btn = gr.Button("Generate Embedding", variant="primary")
@@ -319,7 +334,7 @@ Generate embeddings, compare text similarity, and explore vector representations
single_btn.click( single_btn.click(
fn=generate_single_embedding, fn=generate_single_embedding,
inputs=single_input, inputs=single_input,
outputs=[single_status, single_preview, single_stats] outputs=[single_status, single_preview, single_stats],
) )
# Tab 2: Compare Texts # Tab 2: Compare Texts
@@ -339,14 +354,17 @@ Generate embeddings, compare text similarity, and explore vector representations
compare_btn.click( compare_btn.click(
fn=compare_texts, fn=compare_texts,
inputs=[compare_text1, compare_text2], inputs=[compare_text1, compare_text2],
outputs=[compare_result, compare_visual] outputs=[compare_result, compare_visual],
) )
# Example pairs # Example pairs
gr.Examples( gr.Examples(
examples=[ examples=[
["The cat sat on the mat.", "A feline was resting on the rug."], ["The cat sat on the mat.", "A feline was resting on the rug."],
["Machine learning is a subset of AI.", "Deep learning uses neural networks."], [
"Machine learning is a subset of AI.",
"Deep learning uses neural networks.",
],
["I love pizza.", "The stock market crashed today."], ["I love pizza.", "The stock market crashed today."],
], ],
inputs=[compare_text1, compare_text2], inputs=[compare_text1, compare_text2],
@@ -354,21 +372,21 @@ Generate embeddings, compare text similarity, and explore vector representations
# Tab 3: Batch Embeddings # Tab 3: Batch Embeddings
with gr.TabItem("📚 Batch Processing"): with gr.TabItem("📚 Batch Processing"):
gr.Markdown("Generate embeddings for multiple texts and see their similarity matrix.") gr.Markdown(
"Generate embeddings for multiple texts and see their similarity matrix."
)
batch_input = gr.Textbox( batch_input = gr.Textbox(
label="Texts (one per line)", label="Texts (one per line)",
placeholder="Enter multiple texts, one per line...", placeholder="Enter multiple texts, one per line...",
lines=6 lines=6,
) )
batch_btn = gr.Button("Process Batch", variant="primary") batch_btn = gr.Button("Process Batch", variant="primary")
batch_status = gr.Textbox(label="Status", interactive=False) batch_status = gr.Textbox(label="Status", interactive=False)
batch_result = gr.Markdown(label="Similarity Matrix") batch_result = gr.Markdown(label="Similarity Matrix")
batch_btn.click( batch_btn.click(
fn=batch_embed, fn=batch_embed, inputs=batch_input, outputs=[batch_status, batch_result]
inputs=batch_input,
outputs=[batch_status, batch_result]
) )
gr.Examples( gr.Examples(
@@ -383,8 +401,4 @@ Generate embeddings, compare text similarity, and explore vector representations
if __name__ == "__main__": if __name__ == "__main__":
demo.launch( demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)
server_name="0.0.0.0",
server_port=7860,
show_error=True
)

35
llm.py
View File

@@ -9,10 +9,10 @@ Features:
- Token usage and latency metrics - Token usage and latency metrics
- Chat history management - Chat history management
""" """
import os import os
import time import time
import logging import logging
import json
import gradio as gr import gradio as gr
import httpx import httpx
@@ -65,7 +65,9 @@ try:
_mlflow_run_id = _mlflow_run.info.run_id _mlflow_run_id = _mlflow_run.info.run_id
_mlflow_step = 0 _mlflow_step = 0
MLFLOW_ENABLED = True MLFLOW_ENABLED = True
logger.info("MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id) logger.info(
"MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id
)
except Exception as exc: except Exception as exc:
logger.warning("MLflow tracking disabled: %s", exc) logger.warning("MLflow tracking disabled: %s", exc)
_mlflow_client = None _mlflow_client = None
@@ -95,18 +97,25 @@ def _log_llm_metrics(
_mlflow_run_id, _mlflow_run_id,
metrics=[ metrics=[
mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step), mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step),
mlflow.entities.Metric("prompt_tokens", prompt_tokens, ts, _mlflow_step), mlflow.entities.Metric(
mlflow.entities.Metric("completion_tokens", completion_tokens, ts, _mlflow_step), "prompt_tokens", prompt_tokens, ts, _mlflow_step
),
mlflow.entities.Metric(
"completion_tokens", completion_tokens, ts, _mlflow_step
),
mlflow.entities.Metric("total_tokens", total_tokens, ts, _mlflow_step), mlflow.entities.Metric("total_tokens", total_tokens, ts, _mlflow_step),
mlflow.entities.Metric("tokens_per_second", tps, ts, _mlflow_step), mlflow.entities.Metric("tokens_per_second", tps, ts, _mlflow_step),
mlflow.entities.Metric("temperature", temperature, ts, _mlflow_step), mlflow.entities.Metric("temperature", temperature, ts, _mlflow_step),
mlflow.entities.Metric("max_tokens_requested", max_tokens, ts, _mlflow_step), mlflow.entities.Metric(
"max_tokens_requested", max_tokens, ts, _mlflow_step
),
mlflow.entities.Metric("top_p", top_p, ts, _mlflow_step), mlflow.entities.Metric("top_p", top_p, ts, _mlflow_step),
], ],
) )
except Exception: except Exception:
logger.debug("MLflow log failed", exc_info=True) logger.debug("MLflow log failed", exc_info=True)
DEFAULT_SYSTEM_PROMPT = ( DEFAULT_SYSTEM_PROMPT = (
"You are a helpful AI assistant running on Davies Tech Labs homelab infrastructure. " "You are a helpful AI assistant running on Davies Tech Labs homelab infrastructure. "
"You are powered by Llama 3.1 70B served via vLLM on AMD Strix Halo (ROCm). " "You are powered by Llama 3.1 70B served via vLLM on AMD Strix Halo (ROCm). "
@@ -273,10 +282,10 @@ def single_prompt(
metrics = f""" metrics = f"""
**Generation Metrics:** **Generation Metrics:**
- Latency: {latency:.1f}s - Latency: {latency:.1f}s
- Prompt tokens: {usage.get('prompt_tokens', 'N/A')} - Prompt tokens: {usage.get("prompt_tokens", "N/A")}
- Completion tokens: {usage.get('completion_tokens', 'N/A')} - Completion tokens: {usage.get("completion_tokens", "N/A")}
- Total tokens: {usage.get('total_tokens', 'N/A')} - Total tokens: {usage.get("total_tokens", "N/A")}
- Model: {result.get('model', 'N/A')} - Model: {result.get("model", "N/A")}
""" """
return text, metrics return text, metrics
@@ -360,9 +369,13 @@ Chat with **Llama 3.1 70B** (AWQ INT4) served via vLLM on AMD Strix Halo (ROCm).
gr.Examples( gr.Examples(
examples=[ examples=[
["Summarise the key differences between CUDA and ROCm for ML workloads."], [
"Summarise the key differences between CUDA and ROCm for ML workloads."
],
["Write a haiku about Kubernetes."], ["Write a haiku about Kubernetes."],
["Explain Ray Serve in one paragraph for someone new to ML serving."], [
"Explain Ray Serve in one paragraph for someone new to ML serving."
],
["List 5 creative uses for a homelab GPU cluster."], ["List 5 creative uses for a homelab GPU cluster."],
], ],
inputs=[prompt_input], inputs=[prompt_input],

81
stt.py
View File

@@ -9,11 +9,11 @@ Features:
- Translation mode - Translation mode
- MLflow metrics logging - MLflow metrics logging
""" """
import os import os
import time import time
import logging import logging
import io import io
import tempfile
import gradio as gr import gradio as gr
import httpx import httpx
@@ -30,11 +30,10 @@ logger = logging.getLogger("stt-demo")
STT_URL = os.environ.get( STT_URL = os.environ.get(
"STT_URL", "STT_URL",
# Default: Ray Serve whisper endpoint # Default: Ray Serve whisper endpoint
"http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/whisper" "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/whisper",
) )
MLFLOW_TRACKING_URI = os.environ.get( MLFLOW_TRACKING_URI = os.environ.get(
"MLFLOW_TRACKING_URI", "MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80"
"http://mlflow.mlflow.svc.cluster.local:80"
) )
# ─── MLflow experiment tracking ────────────────────────────────────────── # ─── MLflow experiment tracking ──────────────────────────────────────────
@@ -62,7 +61,9 @@ try:
_mlflow_run_id = _mlflow_run.info.run_id _mlflow_run_id = _mlflow_run.info.run_id
_mlflow_step = 0 _mlflow_step = 0
MLFLOW_ENABLED = True MLFLOW_ENABLED = True
logger.info("MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id) logger.info(
"MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id
)
except Exception as exc: except Exception as exc:
logger.warning("MLflow tracking disabled: %s", exc) logger.warning("MLflow tracking disabled: %s", exc)
_mlflow_client = None _mlflow_client = None
@@ -72,7 +73,10 @@ except Exception as exc:
def _log_stt_metrics( def _log_stt_metrics(
latency: float, audio_duration: float, word_count: int, task: str, latency: float,
audio_duration: float,
word_count: int,
task: str,
) -> None: ) -> None:
"""Log STT inference metrics to MLflow (non-blocking best-effort).""" """Log STT inference metrics to MLflow (non-blocking best-effort)."""
global _mlflow_step global _mlflow_step
@@ -86,11 +90,15 @@ def _log_stt_metrics(
_mlflow_run_id, _mlflow_run_id,
metrics=[ metrics=[
mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step), mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step),
mlflow.entities.Metric("audio_duration_s", audio_duration, ts, _mlflow_step), mlflow.entities.Metric(
"audio_duration_s", audio_duration, ts, _mlflow_step
),
mlflow.entities.Metric("realtime_factor", rtf, ts, _mlflow_step), mlflow.entities.Metric("realtime_factor", rtf, ts, _mlflow_step),
mlflow.entities.Metric("word_count", word_count, ts, _mlflow_step), mlflow.entities.Metric("word_count", word_count, ts, _mlflow_step),
], ],
params=[] if _mlflow_step > 1 else [ params=[]
if _mlflow_step > 1
else [
mlflow.entities.Param("task", task), mlflow.entities.Param("task", task),
], ],
) )
@@ -124,9 +132,7 @@ LANGUAGES = {
def transcribe_audio( def transcribe_audio(
audio_input: tuple[int, np.ndarray] | str | None, audio_input: tuple[int, np.ndarray] | str | None, language: str, task: str
language: str,
task: str
) -> tuple[str, str, str]: ) -> tuple[str, str, str]:
"""Transcribe audio using the Whisper STT service.""" """Transcribe audio using the Whisper STT service."""
if audio_input is None: if audio_input is None:
@@ -142,12 +148,12 @@ def transcribe_audio(
# Convert to WAV bytes # Convert to WAV bytes
audio_buffer = io.BytesIO() audio_buffer = io.BytesIO()
sf.write(audio_buffer, audio_data, sample_rate, format='WAV') sf.write(audio_buffer, audio_data, sample_rate, format="WAV")
audio_bytes = audio_buffer.getvalue() audio_bytes = audio_buffer.getvalue()
audio_duration = len(audio_data) / sample_rate audio_duration = len(audio_data) / sample_rate
else: else:
# File path # File path
with open(audio_input, 'rb') as f: with open(audio_input, "rb") as f:
audio_bytes = f.read() audio_bytes = f.read()
# Get duration # Get duration
audio_data, sample_rate = sf.read(audio_input) audio_data, sample_rate = sf.read(audio_input)
@@ -187,7 +193,9 @@ def transcribe_audio(
) )
# Status message # Status message
status = f"✅ Transcribed {audio_duration:.1f}s of audio in {latency*1000:.0f}ms" status = (
f"✅ Transcribed {audio_duration:.1f}s of audio in {latency * 1000:.0f}ms"
)
# Metrics # Metrics
metrics = f""" metrics = f"""
@@ -250,21 +258,19 @@ or file upload with support for 100+ languages.
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
mic_input = gr.Audio( mic_input = gr.Audio(
label="Record Audio", label="Record Audio", sources=["microphone"], type="numpy"
sources=["microphone"],
type="numpy"
) )
with gr.Row(): with gr.Row():
mic_language = gr.Dropdown( mic_language = gr.Dropdown(
choices=list(LANGUAGES.keys()), choices=list(LANGUAGES.keys()),
value="Auto-detect", value="Auto-detect",
label="Language" label="Language",
) )
mic_task = gr.Radio( mic_task = gr.Radio(
choices=["Transcribe", "Translate to English"], choices=["Transcribe", "Translate to English"],
value="Transcribe", value="Transcribe",
label="Task" label="Task",
) )
mic_btn = gr.Button("🎯 Transcribe", variant="primary") mic_btn = gr.Button("🎯 Transcribe", variant="primary")
@@ -273,15 +279,12 @@ or file upload with support for 100+ languages.
mic_status = gr.Textbox(label="Status", interactive=False) mic_status = gr.Textbox(label="Status", interactive=False)
mic_metrics = gr.Markdown(label="Metrics") mic_metrics = gr.Markdown(label="Metrics")
mic_output = gr.Textbox( mic_output = gr.Textbox(label="Transcription", lines=5)
label="Transcription",
lines=5
)
mic_btn.click( mic_btn.click(
fn=transcribe_audio, fn=transcribe_audio,
inputs=[mic_input, mic_language, mic_task], inputs=[mic_input, mic_language, mic_task],
outputs=[mic_status, mic_output, mic_metrics] outputs=[mic_status, mic_output, mic_metrics],
) )
# Tab 2: File Upload # Tab 2: File Upload
@@ -289,21 +292,19 @@ or file upload with support for 100+ languages.
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
file_input = gr.Audio( file_input = gr.Audio(
label="Upload Audio File", label="Upload Audio File", sources=["upload"], type="filepath"
sources=["upload"],
type="filepath"
) )
with gr.Row(): with gr.Row():
file_language = gr.Dropdown( file_language = gr.Dropdown(
choices=list(LANGUAGES.keys()), choices=list(LANGUAGES.keys()),
value="Auto-detect", value="Auto-detect",
label="Language" label="Language",
) )
file_task = gr.Radio( file_task = gr.Radio(
choices=["Transcribe", "Translate to English"], choices=["Transcribe", "Translate to English"],
value="Transcribe", value="Transcribe",
label="Task" label="Task",
) )
file_btn = gr.Button("🎯 Transcribe", variant="primary") file_btn = gr.Button("🎯 Transcribe", variant="primary")
@@ -312,15 +313,12 @@ or file upload with support for 100+ languages.
file_status = gr.Textbox(label="Status", interactive=False) file_status = gr.Textbox(label="Status", interactive=False)
file_metrics = gr.Markdown(label="Metrics") file_metrics = gr.Markdown(label="Metrics")
file_output = gr.Textbox( file_output = gr.Textbox(label="Transcription", lines=5)
label="Transcription",
lines=5
)
file_btn.click( file_btn.click(
fn=transcribe_audio, fn=transcribe_audio,
inputs=[file_input, file_language, file_task], inputs=[file_input, file_language, file_task],
outputs=[file_status, file_output, file_metrics] outputs=[file_status, file_output, file_metrics],
) )
gr.Markdown(""" gr.Markdown("""
@@ -343,7 +341,7 @@ Whisper will automatically detect the source language.
trans_input = gr.Audio( trans_input = gr.Audio(
label="Audio Input", label="Audio Input",
sources=["microphone", "upload"], sources=["microphone", "upload"],
type="numpy" type="numpy",
) )
trans_btn = gr.Button("🌍 Translate to English", variant="primary") trans_btn = gr.Button("🌍 Translate to English", variant="primary")
@@ -351,10 +349,7 @@ Whisper will automatically detect the source language.
trans_status = gr.Textbox(label="Status", interactive=False) trans_status = gr.Textbox(label="Status", interactive=False)
trans_metrics = gr.Markdown(label="Metrics") trans_metrics = gr.Markdown(label="Metrics")
trans_output = gr.Textbox( trans_output = gr.Textbox(label="English Translation", lines=5)
label="English Translation",
lines=5
)
def translate_audio(audio): def translate_audio(audio):
return transcribe_audio(audio, "Auto-detect", "Translate to English") return transcribe_audio(audio, "Auto-detect", "Translate to English")
@@ -362,15 +357,11 @@ Whisper will automatically detect the source language.
trans_btn.click( trans_btn.click(
fn=translate_audio, fn=translate_audio,
inputs=trans_input, inputs=trans_input,
outputs=[trans_status, trans_output, trans_metrics] outputs=[trans_status, trans_output, trans_metrics],
) )
create_footer() create_footer()
if __name__ == "__main__": if __name__ == "__main__":
demo.launch( demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)
server_name="0.0.0.0",
server_port=7860,
show_error=True
)

8
theme.py
View File

@@ -3,6 +3,7 @@ Shared Gradio theme for Davies Tech Labs AI demos.
Consistent styling across all demo applications. Consistent styling across all demo applications.
Cyberpunk aesthetic - dark with yellow/gold accents. Cyberpunk aesthetic - dark with yellow/gold accents.
""" """
import gradio as gr import gradio as gr
@@ -25,7 +26,12 @@ def get_lab_theme() -> gr.Theme:
primary_hue=gr.themes.colors.yellow, primary_hue=gr.themes.colors.yellow,
secondary_hue=gr.themes.colors.amber, secondary_hue=gr.themes.colors.amber,
neutral_hue=gr.themes.colors.zinc, neutral_hue=gr.themes.colors.zinc,
font=[gr.themes.GoogleFont("Space Grotesk"), "ui-sans-serif", "system-ui", "sans-serif"], font=[
gr.themes.GoogleFont("Space Grotesk"),
"ui-sans-serif",
"system-ui",
"sans-serif",
],
font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui-monospace", "monospace"], font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui-monospace", "monospace"],
).set( ).set(
# Background colors # Background colors

81
tts.py
View File

@@ -9,11 +9,11 @@ Features:
- MLflow metrics logging - MLflow metrics logging
- Multiple TTS backends support (Coqui XTTS, Piper, etc.) - Multiple TTS backends support (Coqui XTTS, Piper, etc.)
""" """
import os import os
import time import time
import logging import logging
import io import io
import base64
import gradio as gr import gradio as gr
import httpx import httpx
@@ -30,11 +30,10 @@ logger = logging.getLogger("tts-demo")
TTS_URL = os.environ.get( TTS_URL = os.environ.get(
"TTS_URL", "TTS_URL",
# Default: Ray Serve TTS endpoint # Default: Ray Serve TTS endpoint
"http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/tts" "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/tts",
) )
MLFLOW_TRACKING_URI = os.environ.get( MLFLOW_TRACKING_URI = os.environ.get(
"MLFLOW_TRACKING_URI", "MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80"
"http://mlflow.mlflow.svc.cluster.local:80"
) )
# ─── MLflow experiment tracking ────────────────────────────────────────── # ─── MLflow experiment tracking ──────────────────────────────────────────
@@ -62,7 +61,9 @@ try:
_mlflow_run_id = _mlflow_run.info.run_id _mlflow_run_id = _mlflow_run.info.run_id
_mlflow_step = 0 _mlflow_step = 0
MLFLOW_ENABLED = True MLFLOW_ENABLED = True
logger.info("MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id) logger.info(
"MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id
)
except Exception as exc: except Exception as exc:
logger.warning("MLflow tracking disabled: %s", exc) logger.warning("MLflow tracking disabled: %s", exc)
_mlflow_client = None _mlflow_client = None
@@ -72,7 +73,10 @@ except Exception as exc:
def _log_tts_metrics( def _log_tts_metrics(
latency: float, audio_duration: float, text_chars: int, language: str, latency: float,
audio_duration: float,
text_chars: int,
language: str,
) -> None: ) -> None:
"""Log TTS inference metrics to MLflow (non-blocking best-effort).""" """Log TTS inference metrics to MLflow (non-blocking best-effort)."""
global _mlflow_step global _mlflow_step
@@ -87,7 +91,9 @@ def _log_tts_metrics(
_mlflow_run_id, _mlflow_run_id,
metrics=[ metrics=[
mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step), mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step),
mlflow.entities.Metric("audio_duration_s", audio_duration, ts, _mlflow_step), mlflow.entities.Metric(
"audio_duration_s", audio_duration, ts, _mlflow_step
),
mlflow.entities.Metric("realtime_factor", rtf, ts, _mlflow_step), mlflow.entities.Metric("realtime_factor", rtf, ts, _mlflow_step),
mlflow.entities.Metric("chars_per_second", cps, ts, _mlflow_step), mlflow.entities.Metric("chars_per_second", cps, ts, _mlflow_step),
mlflow.entities.Metric("text_chars", text_chars, ts, _mlflow_step), mlflow.entities.Metric("text_chars", text_chars, ts, _mlflow_step),
@@ -121,7 +127,9 @@ LANGUAGES = {
} }
def synthesize_speech(text: str, language: str) -> tuple[str, tuple[int, np.ndarray] | None, str]: def synthesize_speech(
text: str, language: str
) -> tuple[str, tuple[int, np.ndarray] | None, str]:
"""Synthesize speech from text using the TTS service.""" """Synthesize speech from text using the TTS service."""
if not text.strip(): if not text.strip():
return "❌ Please enter some text", None, "" return "❌ Please enter some text", None, ""
@@ -133,8 +141,7 @@ def synthesize_speech(text: str, language: str) -> tuple[str, tuple[int, np.ndar
# Call TTS service (Coqui XTTS API format) # Call TTS service (Coqui XTTS API format)
response = client.get( response = client.get(
f"{TTS_URL}/api/tts", f"{TTS_URL}/api/tts", params={"text": text, "language_id": lang_code}
params={"text": text, "language_id": lang_code}
) )
response.raise_for_status() response.raise_for_status()
@@ -228,16 +235,18 @@ in multiple languages.
label="Text to Synthesize", label="Text to Synthesize",
placeholder="Enter text to convert to speech...", placeholder="Enter text to convert to speech...",
lines=5, lines=5,
max_lines=10 max_lines=10,
) )
with gr.Row(): with gr.Row():
language = gr.Dropdown( language = gr.Dropdown(
choices=list(LANGUAGES.keys()), choices=list(LANGUAGES.keys()),
value="English", value="English",
label="Language" label="Language",
)
synthesize_btn = gr.Button(
"🔊 Synthesize", variant="primary", scale=2
) )
synthesize_btn = gr.Button("🔊 Synthesize", variant="primary", scale=2)
with gr.Column(scale=1): with gr.Column(scale=1):
status_output = gr.Textbox(label="Status", interactive=False) status_output = gr.Textbox(label="Status", interactive=False)
@@ -248,15 +257,24 @@ in multiple languages.
synthesize_btn.click( synthesize_btn.click(
fn=synthesize_speech, fn=synthesize_speech,
inputs=[text_input, language], inputs=[text_input, language],
outputs=[status_output, audio_output, metrics_output] outputs=[status_output, audio_output, metrics_output],
) )
# Example texts # Example texts
gr.Examples( gr.Examples(
examples=[ examples=[
["Hello! Welcome to Davies Tech Labs. This is a demonstration of our text-to-speech system.", "English"], [
["The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet.", "English"], "Hello! Welcome to Davies Tech Labs. This is a demonstration of our text-to-speech system.",
["Bonjour! Bienvenue au laboratoire technique de Davies.", "French"], "English",
],
[
"The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet.",
"English",
],
[
"Bonjour! Bienvenue au laboratoire technique de Davies.",
"French",
],
["Hola! Bienvenido al laboratorio de tecnología.", "Spanish"], ["Hola! Bienvenido al laboratorio de tecnología.", "Spanish"],
["Guten Tag! Willkommen im Techniklabor.", "German"], ["Guten Tag! Willkommen im Techniklabor.", "German"],
], ],
@@ -268,14 +286,16 @@ in multiple languages.
gr.Markdown("Compare the same text in different languages.") gr.Markdown("Compare the same text in different languages.")
compare_text = gr.Textbox( compare_text = gr.Textbox(
label="Text to Compare", label="Text to Compare", value="Hello, how are you today?", lines=2
value="Hello, how are you today?",
lines=2
) )
with gr.Row(): with gr.Row():
lang1 = gr.Dropdown(choices=list(LANGUAGES.keys()), value="English", label="Language 1") lang1 = gr.Dropdown(
lang2 = gr.Dropdown(choices=list(LANGUAGES.keys()), value="Spanish", label="Language 2") choices=list(LANGUAGES.keys()), value="English", label="Language 1"
)
lang2 = gr.Dropdown(
choices=list(LANGUAGES.keys()), value="Spanish", label="Language 2"
)
compare_btn = gr.Button("Compare Languages", variant="primary") compare_btn = gr.Button("Compare Languages", variant="primary")
@@ -298,7 +318,7 @@ in multiple languages.
compare_btn.click( compare_btn.click(
fn=compare_languages, fn=compare_languages,
inputs=[compare_text, lang1, lang2], inputs=[compare_text, lang1, lang2],
outputs=[status1, audio1, status2, audio2] outputs=[status1, audio1, status2, audio2],
) )
# Tab 3: Batch Processing # Tab 3: Batch Processing
@@ -308,19 +328,16 @@ in multiple languages.
batch_input = gr.Textbox( batch_input = gr.Textbox(
label="Texts (one per line)", label="Texts (one per line)",
placeholder="Enter multiple texts, one per line...", placeholder="Enter multiple texts, one per line...",
lines=6 lines=6,
) )
batch_lang = gr.Dropdown( batch_lang = gr.Dropdown(
choices=list(LANGUAGES.keys()), choices=list(LANGUAGES.keys()), value="English", label="Language"
value="English",
label="Language"
) )
batch_btn = gr.Button("Synthesize All", variant="primary") batch_btn = gr.Button("Synthesize All", variant="primary")
batch_status = gr.Textbox(label="Status", interactive=False) batch_status = gr.Textbox(label="Status", interactive=False)
batch_audios = gr.Dataset( batch_audios = gr.Dataset(
components=[gr.Audio(type="numpy")], components=[gr.Audio(type="numpy")], label="Generated Audio Files"
label="Generated Audio Files"
) )
# Note: Batch processing would need more complex handling # Note: Batch processing would need more complex handling
@@ -334,8 +351,4 @@ or the Kubeflow pipeline for better throughput.*
if __name__ == "__main__": if __name__ == "__main__":
demo.launch( demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)
server_name="0.0.0.0",
server_port=7860,
show_error=True
)