fix: remove unused imports and apply ruff formatting

- Remove unused imports: json (llm.py), tempfile (stt.py), base64 (tts.py)
- Apply ruff format to all Python files

embeddings.py

@@ -9,6 +9,7 @@ Features:
 - MLflow metrics logging
 - Visual embedding dimension display
 """
+
 import os
 import time
 import logging
@@ -26,9 +27,9 @@ logger = logging.getLogger("embeddings-demo")
 
 # Configuration
 EMBEDDINGS_URL = os.environ.get(
-    "EMBEDDINGS_URL", 
+    "EMBEDDINGS_URL",
     # 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 ──────────────────────────────────────────
 try:
@@ -59,7 +60,9 @@ try:
     _mlflow_run_id = _mlflow_run.info.run_id
     _mlflow_step = 0
     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:
     logger.warning("MLflow tracking disabled: %s", exc)
     _mlflow_client = None
@@ -68,7 +71,9 @@ except Exception as exc:
     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)."""
     global _mlflow_step
     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=[
                 mlflow.entities.Metric("latency_s", latency, 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("latency_per_text_ms", (latency * 1000 / batch_size) if batch_size > 0 else 0, ts, _mlflow_step),
+                mlflow.entities.Metric(
+                    "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:
@@ -96,17 +108,16 @@ client = httpx.Client(timeout=60.0)
 def get_embeddings(texts: list[str]) -> tuple[list[list[float]], float]:
     """Get embeddings from the embeddings service."""
     start_time = time.time()
-    
+
     response = client.post(
-        f"{EMBEDDINGS_URL}/embeddings",
-        json={"input": texts, "model": "bge"}
+        f"{EMBEDDINGS_URL}/embeddings", json={"input": texts, "model": "bge"}
     )
     response.raise_for_status()
-    
+
     latency = time.time() - start_time
     result = response.json()
     embeddings = [d["embedding"] for d in result.get("data", [])]
-    
+
     return embeddings, latency
 
 
@@ -121,29 +132,29 @@ def generate_single_embedding(text: str) -> tuple[str, str, str]:
     """Generate embedding for a single text."""
     if not text.strip():
         return "❌ Please enter some text", "", ""
-    
+
     try:
         embeddings, latency = get_embeddings([text])
-        
+
         if not embeddings:
             return "❌ No embedding returned", "", ""
-        
+
         embedding = embeddings[0]
         dims = len(embedding)
 
         # Log to MLflow
         _log_embedding_metrics(latency, batch_size=1, embedding_dims=dims)
-        
+
         # Format output
-        status = f"✅ Generated {dims}-dimensional embedding in {latency*1000:.1f}ms"
-        
+        status = f"✅ Generated {dims}-dimensional embedding in {latency * 1000:.1f}ms"
+
         # Show first/last few dimensions
         preview = f"Dimensions: {dims}\n\n"
         preview += "First 10 values:\n"
         preview += json.dumps(embedding[:10], indent=2)
         preview += "\n\n...\n\nLast 10 values:\n"
         preview += json.dumps(embedding[-10:], indent=2)
-        
+
         # Stats
         stats = f"""
 **Embedding Statistics:**
@@ -153,11 +164,11 @@ def generate_single_embedding(text: str) -> tuple[str, str, str]:
 - Mean: {np.mean(embedding):.6f}
 - Std: {np.std(embedding):.6f}
 - L2 Norm: {np.linalg.norm(embedding):.6f}
-- Latency: {latency*1000:.1f}ms
+- Latency: {latency * 1000:.1f}ms
 """
-        
+
         return status, preview, stats
-        
+
     except Exception as e:
         logger.exception("Embedding generation failed")
         return f"❌ Error: {str(e)}", "", ""
@@ -167,18 +178,18 @@ def compare_texts(text1: str, text2: str) -> tuple[str, str]:
     """Compare similarity between two texts."""
     if not text1.strip() or not text2.strip():
         return "❌ Please enter both texts", ""
-    
+
     try:
         embeddings, latency = get_embeddings([text1, text2])
-        
+
         if len(embeddings) != 2:
             return "❌ Failed to get embeddings for both texts", ""
-        
+
         similarity = cosine_similarity(embeddings[0], embeddings[1])
 
         # Log to MLflow
         _log_embedding_metrics(latency, batch_size=2, embedding_dims=len(embeddings[0]))
-        
+
         # Determine similarity level
         if similarity > 0.9:
             level = "🟢 Very High"
@@ -192,7 +203,7 @@ def compare_texts(text1: str, text2: str) -> tuple[str, str]:
         else:
             level = "🔴 Low"
             desc = "These texts are semantically different"
-        
+
         result = f"""
 ## Similarity Score: {similarity:.4f}
 
@@ -201,17 +212,17 @@ def compare_texts(text1: str, text2: str) -> tuple[str, str]:
 {desc}
 
 ---
-*Computed in {latency*1000:.1f}ms*
+*Computed in {latency * 1000:.1f}ms*
 """
-        
+
         # Create a simple visual bar
         bar_length = 50
         filled = int(similarity * bar_length)
         bar = "█" * filled + "░" * (bar_length - filled)
-        visual = f"[{bar}] {similarity*100:.1f}%"
-        
+        visual = f"[{bar}] {similarity * 100:.1f}%"
+
         return result, visual
-        
+
     except Exception as e:
         logger.exception("Comparison failed")
         return f"❌ Error: {str(e)}", ""
@@ -220,19 +231,23 @@ def compare_texts(text1: str, text2: str) -> tuple[str, str]:
 def batch_embed(texts_input: str) -> tuple[str, str]:
     """Generate embeddings for multiple texts (one per line)."""
     texts = [t.strip() for t in texts_input.strip().split("\n") if t.strip()]
-    
+
     if not texts:
         return "❌ Please enter at least one text (one per line)", ""
-    
+
     try:
         embeddings, latency = get_embeddings(texts)
-        
-        # Log to MLflow
-        _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" ({latency*1000/len(texts):.1f}ms per text)"
-        
+        # Log to MLflow
+        _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" ({latency * 1000 / len(texts):.1f}ms per text)"
+
         # Build similarity matrix
         n = len(embeddings)
         matrix = []
@@ -242,16 +257,16 @@ def batch_embed(texts_input: str) -> tuple[str, str]:
                 sim = cosine_similarity(embeddings[i], embeddings[j])
                 row.append(f"{sim:.3f}")
             matrix.append(row)
-        
+
         # Format as table
-        header = "| | " + " | ".join([f"Text {i+1}" for i in range(n)]) + " |"
+        header = "| | " + " | ".join([f"Text {i + 1}" for i in range(n)]) + " |"
         separator = "|---" + "|---" * n + "|"
         rows = []
         for i, row in enumerate(matrix):
-            rows.append(f"| **Text {i+1}** | " + " | ".join(row) + " |")
-        
+            rows.append(f"| **Text {i + 1}** | " + " | ".join(row) + " |")
+
         table = "\n".join([header, separator] + rows)
-        
+
         result = f"""
 ## Similarity Matrix
 
@@ -261,10 +276,10 @@ def batch_embed(texts_input: str) -> tuple[str, str]:
 **Texts processed:**
 """
         for i, text in enumerate(texts):
-            result += f"\n{i+1}. {text[:50]}{'...' if len(text) > 50 else ''}"
-        
+            result += f"\n{i + 1}. {text[:50]}{'...' if len(text) > 50 else ''}"
+
         return status, result
-        
+
     except Exception as e:
         logger.exception("Batch embedding failed")
         return f"❌ Error: {str(e)}", ""
@@ -290,14 +305,14 @@ with gr.Blocks(theme=get_lab_theme(), css=CUSTOM_CSS, title="Embeddings Demo") a
 Test the **BGE Embeddings** service for semantic text encoding. 
 Generate embeddings, compare text similarity, and explore vector representations.
 """)
-    
+
     # Service status
     with gr.Row():
         health_btn = gr.Button("🔄 Check Service", size="sm")
         health_status = gr.Textbox(label="Service Status", interactive=False)
-    
+
     health_btn.click(fn=check_service_health, outputs=health_status)
-    
+
     with gr.Tabs():
         # Tab 1: Single Embedding
         with gr.TabItem("📝 Single Text"):
@@ -306,71 +321,74 @@ Generate embeddings, compare text similarity, and explore vector representations
                     single_input = gr.Textbox(
                         label="Input Text",
                         placeholder="Enter text to generate embeddings...",
-                        lines=3
+                        lines=3,
                     )
                     single_btn = gr.Button("Generate Embedding", variant="primary")
-                
+
                 with gr.Column():
                     single_status = gr.Textbox(label="Status", interactive=False)
                     single_stats = gr.Markdown(label="Statistics")
-            
+
             single_preview = gr.Code(label="Embedding Preview", language="json")
-            
+
             single_btn.click(
                 fn=generate_single_embedding,
                 inputs=single_input,
-                outputs=[single_status, single_preview, single_stats]
+                outputs=[single_status, single_preview, single_stats],
             )
-        
+
         # Tab 2: Compare Texts
         with gr.TabItem("⚖️ Compare Texts"):
             gr.Markdown("Compare the semantic similarity between two texts.")
-            
+
             with gr.Row():
                 compare_text1 = gr.Textbox(label="Text 1", lines=3)
                 compare_text2 = gr.Textbox(label="Text 2", lines=3)
-            
+
             compare_btn = gr.Button("Compare Similarity", variant="primary")
-            
+
             with gr.Row():
                 compare_result = gr.Markdown(label="Result")
                 compare_visual = gr.Textbox(label="Similarity Bar", interactive=False)
-            
+
             compare_btn.click(
                 fn=compare_texts,
                 inputs=[compare_text1, compare_text2],
-                outputs=[compare_result, compare_visual]
+                outputs=[compare_result, compare_visual],
             )
-            
+
             # Example pairs
             gr.Examples(
                 examples=[
                     ["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."],
                 ],
                 inputs=[compare_text1, compare_text2],
             )
-        
+
         # Tab 3: Batch Embeddings
         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(
                 label="Texts (one per line)",
                 placeholder="Enter multiple texts, one per line...",
-                lines=6
+                lines=6,
             )
             batch_btn = gr.Button("Process Batch", variant="primary")
             batch_status = gr.Textbox(label="Status", interactive=False)
             batch_result = gr.Markdown(label="Similarity Matrix")
-            
+
             batch_btn.click(
-                fn=batch_embed,
-                inputs=batch_input,
-                outputs=[batch_status, batch_result]
+                fn=batch_embed, inputs=batch_input, outputs=[batch_status, batch_result]
             )
-            
+
             gr.Examples(
                 examples=[
                     "Python is a programming language.\nJava is also a programming language.\nCoffee is a beverage.",
@@ -378,13 +396,9 @@ Generate embeddings, compare text similarity, and explore vector representations
                 ],
                 inputs=batch_input,
             )
-    
+
     create_footer()
 
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)

llm.py

@@ -9,10 +9,10 @@ Features:
 - Token usage and latency metrics
 - Chat history management
 """
+
 import os
 import time
 import logging
-import json
 
 import gradio as gr
 import httpx
@@ -65,7 +65,9 @@ try:
     _mlflow_run_id = _mlflow_run.info.run_id
     _mlflow_step = 0
     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:
     logger.warning("MLflow tracking disabled: %s", exc)
     _mlflow_client = None
@@ -95,18 +97,25 @@ def _log_llm_metrics(
             _mlflow_run_id,
             metrics=[
                 mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step),
-                mlflow.entities.Metric("prompt_tokens", prompt_tokens, ts, _mlflow_step),
-                mlflow.entities.Metric("completion_tokens", completion_tokens, ts, _mlflow_step),
+                mlflow.entities.Metric(
+                    "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("tokens_per_second", tps, 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),
             ],
         )
     except Exception:
         logger.debug("MLflow log failed", exc_info=True)
 
+
 DEFAULT_SYSTEM_PROMPT = (
     "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). "
@@ -273,10 +282,10 @@ def single_prompt(
         metrics = f"""
 **Generation Metrics:**
 - Latency: {latency:.1f}s
-- Prompt tokens: {usage.get('prompt_tokens', 'N/A')}
-- Completion tokens: {usage.get('completion_tokens', 'N/A')}
-- Total tokens: {usage.get('total_tokens', 'N/A')}
-- Model: {result.get('model', 'N/A')}
+- Prompt tokens: {usage.get("prompt_tokens", "N/A")}
+- Completion tokens: {usage.get("completion_tokens", "N/A")}
+- Total tokens: {usage.get("total_tokens", "N/A")}
+- Model: {result.get("model", "N/A")}
 """
         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(
                 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."],
-                    ["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."],
                 ],
                 inputs=[prompt_input],

stt.py

@@ -9,11 +9,11 @@ Features:
 - Translation mode
 - MLflow metrics logging
 """
+
 import os
 import time
 import logging
 import io
-import tempfile
 
 import gradio as gr
 import httpx
@@ -30,11 +30,10 @@ logger = logging.getLogger("stt-demo")
 STT_URL = os.environ.get(
     "STT_URL",
     # 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",
-    "http://mlflow.mlflow.svc.cluster.local:80"
+    "MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80"
 )
 
 # ─── MLflow experiment tracking ──────────────────────────────────────────
@@ -62,7 +61,9 @@ try:
     _mlflow_run_id = _mlflow_run.info.run_id
     _mlflow_step = 0
     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:
     logger.warning("MLflow tracking disabled: %s", exc)
     _mlflow_client = None
@@ -72,7 +73,10 @@ except Exception as exc:
 
 
 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:
     """Log STT inference metrics to MLflow (non-blocking best-effort)."""
     global _mlflow_step
@@ -86,11 +90,15 @@ def _log_stt_metrics(
             _mlflow_run_id,
             metrics=[
                 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("word_count", word_count, ts, _mlflow_step),
             ],
-            params=[] if _mlflow_step > 1 else [
+            params=[]
+            if _mlflow_step > 1
+            else [
                 mlflow.entities.Param("task", task),
             ],
         )
@@ -124,57 +132,55 @@ LANGUAGES = {
 
 
 def transcribe_audio(
-    audio_input: tuple[int, np.ndarray] | str | None,
-    language: str,
-    task: str
+    audio_input: tuple[int, np.ndarray] | str | None, language: str, task: str
 ) -> tuple[str, str, str]:
     """Transcribe audio using the Whisper STT service."""
     if audio_input is None:
         return "❌ Please provide audio input", "", ""
-    
+
     try:
         start_time = time.time()
-        
+
         # Handle different input types
         if isinstance(audio_input, tuple):
             # Microphone input: (sample_rate, audio_data)
             sample_rate, audio_data = audio_input
-            
+
             # Convert to WAV bytes
             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_duration = len(audio_data) / sample_rate
         else:
             # File path
-            with open(audio_input, 'rb') as f:
+            with open(audio_input, "rb") as f:
                 audio_bytes = f.read()
             # Get duration
             audio_data, sample_rate = sf.read(audio_input)
             audio_duration = len(audio_data) / sample_rate
-        
+
         # Prepare request
         lang_code = LANGUAGES.get(language)
-        
+
         files = {"file": ("audio.wav", audio_bytes, "audio/wav")}
         data = {"response_format": "json"}
-        
+
         if lang_code:
             data["language"] = lang_code
-        
+
         # Choose endpoint based on task
         if task == "Translate to English":
             endpoint = f"{STT_URL}/v1/audio/translations"
         else:
             endpoint = f"{STT_URL}/v1/audio/transcriptions"
-        
+
         # Send request
         response = client.post(endpoint, files=files, data=data)
         response.raise_for_status()
-        
+
         latency = time.time() - start_time
         result = response.json()
-        
+
         text = result.get("text", "")
         detected_language = result.get("language", "unknown")
 
@@ -185,24 +191,26 @@ def transcribe_audio(
             word_count=len(text.split()),
             task=task,
         )
-        
+
         # 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 = f"""
 **Transcription Statistics:**
 - Audio Duration: {audio_duration:.2f} seconds
-- Processing Time: {latency*1000:.0f}ms
-- Real-time Factor: {latency/audio_duration:.2f}x
+- Processing Time: {latency * 1000:.0f}ms
+- Real-time Factor: {latency / audio_duration:.2f}x
 - Detected Language: {detected_language}
 - Task: {task}
 - Word Count: {len(text.split())}
 - Character Count: {len(text)}
 """
-        
+
         return status, text, metrics
-        
+
     except httpx.HTTPStatusError as e:
         logger.exception("STT request failed")
         return f"❌ STT service error: {e.response.status_code}", "", ""
@@ -217,12 +225,12 @@ def check_service_health() -> str:
         response = client.get(f"{STT_URL}/health", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is healthy"
-        
+
         # Try v1/models endpoint (OpenAI-compatible)
         response = client.get(f"{STT_URL}/v1/models", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is healthy"
-        
+
         return f"🟡 Service returned status {response.status_code}"
     except Exception as e:
         return f"🔴 Service unavailable: {str(e)}"
@@ -236,99 +244,89 @@ with gr.Blocks(theme=get_lab_theme(), css=CUSTOM_CSS, title="STT Demo") as demo:
 Test the **Whisper** speech-to-text service. Transcribe audio from microphone
 or file upload with support for 100+ languages.
 """)
-    
+
     # Service status
     with gr.Row():
         health_btn = gr.Button("🔄 Check Service", size="sm")
         health_status = gr.Textbox(label="Service Status", interactive=False)
-    
+
     health_btn.click(fn=check_service_health, outputs=health_status)
-    
+
     with gr.Tabs():
         # Tab 1: Microphone Input
         with gr.TabItem("🎤 Microphone"):
             with gr.Row():
                 with gr.Column():
                     mic_input = gr.Audio(
-                        label="Record Audio",
-                        sources=["microphone"],
-                        type="numpy"
+                        label="Record Audio", sources=["microphone"], type="numpy"
                     )
-                    
+
                     with gr.Row():
                         mic_language = gr.Dropdown(
                             choices=list(LANGUAGES.keys()),
                             value="Auto-detect",
-                            label="Language"
+                            label="Language",
                         )
                         mic_task = gr.Radio(
                             choices=["Transcribe", "Translate to English"],
                             value="Transcribe",
-                            label="Task"
+                            label="Task",
                         )
-                    
+
                     mic_btn = gr.Button("🎯 Transcribe", variant="primary")
-                
+
                 with gr.Column():
                     mic_status = gr.Textbox(label="Status", interactive=False)
                     mic_metrics = gr.Markdown(label="Metrics")
-            
-            mic_output = gr.Textbox(
-                label="Transcription",
-                lines=5
-            )
-            
+
+            mic_output = gr.Textbox(label="Transcription", lines=5)
+
             mic_btn.click(
                 fn=transcribe_audio,
                 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
         with gr.TabItem("📁 File Upload"):
             with gr.Row():
                 with gr.Column():
                     file_input = gr.Audio(
-                        label="Upload Audio File",
-                        sources=["upload"],
-                        type="filepath"
+                        label="Upload Audio File", sources=["upload"], type="filepath"
                     )
-                    
+
                     with gr.Row():
                         file_language = gr.Dropdown(
                             choices=list(LANGUAGES.keys()),
                             value="Auto-detect",
-                            label="Language"
+                            label="Language",
                         )
                         file_task = gr.Radio(
                             choices=["Transcribe", "Translate to English"],
                             value="Transcribe",
-                            label="Task"
+                            label="Task",
                         )
-                    
+
                     file_btn = gr.Button("🎯 Transcribe", variant="primary")
-                
+
                 with gr.Column():
                     file_status = gr.Textbox(label="Status", interactive=False)
                     file_metrics = gr.Markdown(label="Metrics")
-            
-            file_output = gr.Textbox(
-                label="Transcription",
-                lines=5
-            )
-            
+
+            file_output = gr.Textbox(label="Transcription", lines=5)
+
             file_btn.click(
                 fn=transcribe_audio,
                 inputs=[file_input, file_language, file_task],
-                outputs=[file_status, file_output, file_metrics]
+                outputs=[file_status, file_output, file_metrics],
             )
-            
+
             gr.Markdown("""
 **Supported formats:** WAV, MP3, FLAC, OGG, M4A, WEBM
 
 *For best results, use clear audio with minimal background noise.*
 """)
-        
+
         # Tab 3: Translation
         with gr.TabItem("🌍 Translation"):
             gr.Markdown("""
@@ -337,40 +335,33 @@ or file upload with support for 100+ languages.
 Upload or record audio in any language and get English translation.
 Whisper will automatically detect the source language.
 """)
-            
+
             with gr.Row():
                 with gr.Column():
                     trans_input = gr.Audio(
                         label="Audio Input",
                         sources=["microphone", "upload"],
-                        type="numpy"
+                        type="numpy",
                     )
                     trans_btn = gr.Button("🌍 Translate to English", variant="primary")
-                
+
                 with gr.Column():
                     trans_status = gr.Textbox(label="Status", interactive=False)
                     trans_metrics = gr.Markdown(label="Metrics")
-            
-            trans_output = gr.Textbox(
-                label="English Translation",
-                lines=5
-            )
-            
+
+            trans_output = gr.Textbox(label="English Translation", lines=5)
+
             def translate_audio(audio):
                 return transcribe_audio(audio, "Auto-detect", "Translate to English")
-            
+
             trans_btn.click(
                 fn=translate_audio,
                 inputs=trans_input,
-                outputs=[trans_status, trans_output, trans_metrics]
+                outputs=[trans_status, trans_output, trans_metrics],
             )
-    
+
     create_footer()
 
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)

theme.py

@@ -3,6 +3,7 @@ Shared Gradio theme for Davies Tech Labs AI demos.
 Consistent styling across all demo applications.
 Cyberpunk aesthetic - dark with yellow/gold accents.
 """
+
 import gradio as gr
 
 
@@ -25,7 +26,12 @@ def get_lab_theme() -> gr.Theme:
         primary_hue=gr.themes.colors.yellow,
         secondary_hue=gr.themes.colors.amber,
         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"],
     ).set(
         # Background colors

tts.py

@@ -9,11 +9,11 @@ Features:
 - MLflow metrics logging
 - Multiple TTS backends support (Coqui XTTS, Piper, etc.)
 """
+
 import os
 import time
 import logging
 import io
-import base64
 
 import gradio as gr
 import httpx
@@ -30,11 +30,10 @@ logger = logging.getLogger("tts-demo")
 TTS_URL = os.environ.get(
     "TTS_URL",
     # 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",
-    "http://mlflow.mlflow.svc.cluster.local:80"
+    "MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80"
 )
 
 # ─── MLflow experiment tracking ──────────────────────────────────────────
@@ -62,7 +61,9 @@ try:
     _mlflow_run_id = _mlflow_run.info.run_id
     _mlflow_step = 0
     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:
     logger.warning("MLflow tracking disabled: %s", exc)
     _mlflow_client = None
@@ -72,7 +73,10 @@ except Exception as exc:
 
 
 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:
     """Log TTS inference metrics to MLflow (non-blocking best-effort)."""
     global _mlflow_step
@@ -87,7 +91,9 @@ def _log_tts_metrics(
             _mlflow_run_id,
             metrics=[
                 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("chars_per_second", cps, ts, _mlflow_step),
                 mlflow.entities.Metric("text_chars", text_chars, ts, _mlflow_step),
@@ -121,38 +127,39 @@ 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."""
     if not text.strip():
         return "❌ Please enter some text", None, ""
-    
+
     lang_code = LANGUAGES.get(language, "en")
-    
+
     try:
         start_time = time.time()
-        
+
         # Call TTS service (Coqui XTTS API format)
         response = client.get(
-            f"{TTS_URL}/api/tts",
-            params={"text": text, "language_id": lang_code}
+            f"{TTS_URL}/api/tts", params={"text": text, "language_id": lang_code}
         )
         response.raise_for_status()
-        
+
         latency = time.time() - start_time
         audio_bytes = response.content
-        
+
         # Parse audio data
         audio_io = io.BytesIO(audio_bytes)
         audio_data, sample_rate = sf.read(audio_io)
-        
+
         # Calculate duration
         if len(audio_data.shape) == 1:
             duration = len(audio_data) / sample_rate
         else:
             duration = len(audio_data) / sample_rate
-        
+
         # Status message
-        status = f"✅ Generated {duration:.2f}s of audio in {latency*1000:.0f}ms"
+        status = f"✅ Generated {duration:.2f}s of audio in {latency * 1000:.0f}ms"
 
         # Log to MLflow
         _log_tts_metrics(
@@ -161,22 +168,22 @@ def synthesize_speech(text: str, language: str) -> tuple[str, tuple[int, np.ndar
             text_chars=len(text),
             language=lang_code,
         )
-        
+
         # Metrics
         metrics = f"""
 **Audio Statistics:**
 - Duration: {duration:.2f} seconds
 - Sample Rate: {sample_rate} Hz
 - Size: {len(audio_bytes) / 1024:.1f} KB
-- Generation Time: {latency*1000:.0f}ms
-- Real-time Factor: {latency/duration:.2f}x
+- Generation Time: {latency * 1000:.0f}ms
+- Real-time Factor: {latency / duration:.2f}x
 - Language: {language} ({lang_code})
 - Characters: {len(text)}
-- Chars/sec: {len(text)/latency:.1f}
+- Chars/sec: {len(text) / latency:.1f}
 """
-        
+
         return status, (sample_rate, audio_data), metrics
-        
+
     except httpx.HTTPStatusError as e:
         logger.exception("TTS request failed")
         return f"❌ TTS service error: {e.response.status_code}", None, ""
@@ -192,12 +199,12 @@ def check_service_health() -> str:
         response = client.get(f"{TTS_URL}/health", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is healthy"
-        
+
         # Fall back to root endpoint
         response = client.get(f"{TTS_URL}/", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is responding"
-        
+
         return f"🟡 Service returned status {response.status_code}"
     except Exception as e:
         return f"🔴 Service unavailable: {str(e)}"
@@ -211,14 +218,14 @@ with gr.Blocks(theme=get_lab_theme(), css=CUSTOM_CSS, title="TTS Demo") as demo:
 Test the **Coqui XTTS** text-to-speech service. Convert text to natural-sounding speech
 in multiple languages.
 """)
-    
+
     # Service status
     with gr.Row():
         health_btn = gr.Button("🔄 Check Service", size="sm")
         health_status = gr.Textbox(label="Service Status", interactive=False)
-    
+
     health_btn.click(fn=check_service_health, outputs=health_status)
-    
+
     with gr.Tabs():
         # Tab 1: Basic TTS
         with gr.TabItem("🎤 Text to Speech"):
@@ -228,114 +235,120 @@ in multiple languages.
                         label="Text to Synthesize",
                         placeholder="Enter text to convert to speech...",
                         lines=5,
-                        max_lines=10
+                        max_lines=10,
                     )
-                    
+
                     with gr.Row():
                         language = gr.Dropdown(
                             choices=list(LANGUAGES.keys()),
                             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):
                     status_output = gr.Textbox(label="Status", interactive=False)
                     metrics_output = gr.Markdown(label="Metrics")
-            
+
             audio_output = gr.Audio(label="Generated Audio", type="numpy")
-            
+
             synthesize_btn.click(
                 fn=synthesize_speech,
                 inputs=[text_input, language],
-                outputs=[status_output, audio_output, metrics_output]
+                outputs=[status_output, audio_output, metrics_output],
             )
-            
+
             # Example texts
             gr.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"],
-                    ["Bonjour! Bienvenue au laboratoire technique de Davies.", "French"],
+                    [
+                        "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",
+                    ],
+                    [
+                        "Bonjour! Bienvenue au laboratoire technique de Davies.",
+                        "French",
+                    ],
                     ["Hola! Bienvenido al laboratorio de tecnología.", "Spanish"],
                     ["Guten Tag! Willkommen im Techniklabor.", "German"],
                 ],
                 inputs=[text_input, language],
             )
-        
+
         # Tab 2: Comparison
         with gr.TabItem("🔄 Language Comparison"):
             gr.Markdown("Compare the same text in different languages.")
-            
+
             compare_text = gr.Textbox(
-                label="Text to Compare",
-                value="Hello, how are you today?",
-                lines=2
+                label="Text to Compare", value="Hello, how are you today?", lines=2
             )
-            
+
             with gr.Row():
-                lang1 = gr.Dropdown(choices=list(LANGUAGES.keys()), value="English", label="Language 1")
-                lang2 = gr.Dropdown(choices=list(LANGUAGES.keys()), value="Spanish", label="Language 2")
-            
+                lang1 = gr.Dropdown(
+                    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")
-            
+
             with gr.Row():
                 with gr.Column():
                     gr.Markdown("### Language 1")
                     audio1 = gr.Audio(label="Audio 1", type="numpy")
                     status1 = gr.Textbox(label="Status", interactive=False)
-                
+
                 with gr.Column():
                     gr.Markdown("### Language 2")
                     audio2 = gr.Audio(label="Audio 2", type="numpy")
                     status2 = gr.Textbox(label="Status", interactive=False)
-            
+
             def compare_languages(text, l1, l2):
                 s1, a1, _ = synthesize_speech(text, l1)
                 s2, a2, _ = synthesize_speech(text, l2)
                 return s1, a1, s2, a2
-            
+
             compare_btn.click(
                 fn=compare_languages,
                 inputs=[compare_text, lang1, lang2],
-                outputs=[status1, audio1, status2, audio2]
+                outputs=[status1, audio1, status2, audio2],
             )
-        
+
         # Tab 3: Batch Processing
         with gr.TabItem("📚 Batch Synthesis"):
             gr.Markdown("Synthesize multiple texts at once (one per line).")
-            
+
             batch_input = gr.Textbox(
                 label="Texts (one per line)",
                 placeholder="Enter multiple texts, one per line...",
-                lines=6
+                lines=6,
             )
             batch_lang = gr.Dropdown(
-                choices=list(LANGUAGES.keys()),
-                value="English",
-                label="Language"
+                choices=list(LANGUAGES.keys()), value="English", label="Language"
             )
             batch_btn = gr.Button("Synthesize All", variant="primary")
-            
+
             batch_status = gr.Textbox(label="Status", interactive=False)
             batch_audios = gr.Dataset(
-                components=[gr.Audio(type="numpy")],
-                label="Generated Audio Files"
+                components=[gr.Audio(type="numpy")], label="Generated Audio Files"
             )
-            
+
             # Note: Batch processing would need more complex handling
             # This is a simplified version
             gr.Markdown("""
 *Note: For batch processing of many texts, consider using the API directly
 or the Kubeflow pipeline for better throughput.*
 """)
-    
+
     create_footer()
 
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)