feat: add MLflow experiment tracking to all 4 Gradio UIs

Each UI now logs per-request metrics to MLflow:
- llm.py: latency, tokens/sec, prompt/completion tokens (gradio-llm-tuning)
- embeddings.py: latency, text length, batch size (gradio-embeddings-tuning)
- stt.py: latency, audio duration, real-time factor (gradio-stt-tuning)
- tts.py: latency, text length, audio duration (gradio-tts-tuning)

Uses try/except guarded imports so UIs still work if MLflow is
unreachable. Persistent run per Gradio instance, batched metric logging
via MlflowClient.log_batch().

embeddings.py

@@ -30,10 +30,64 @@ EMBEDDINGS_URL = os.environ.get(
     # Default: Ray Serve Embeddings endpoint
     "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/embeddings"
 )
-MLFLOW_TRACKING_URI = os.environ.get(
+# ─── MLflow experiment tracking ──────────────────────────────────────────
+try:
+    import mlflow
+    from mlflow.tracking import MlflowClient
+
+    MLFLOW_TRACKING_URI = os.environ.get(
         "MLFLOW_TRACKING_URI",
-    "http://mlflow.mlflow.svc.cluster.local:80"
+        "http://mlflow.mlflow.svc.cluster.local:80",
-)
+    )
+    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
+    _mlflow_client = MlflowClient()
+
+    _experiment = _mlflow_client.get_experiment_by_name("gradio-embeddings-tuning")
+    if _experiment is None:
+        _experiment_id = _mlflow_client.create_experiment(
+            "gradio-embeddings-tuning",
+            artifact_location="/mlflow/artifacts/gradio-embeddings-tuning",
+        )
+    else:
+        _experiment_id = _experiment.experiment_id
+
+    _mlflow_run = mlflow.start_run(
+        experiment_id=_experiment_id,
+        run_name=f"gradio-embeddings-{os.environ.get('HOSTNAME', 'local')}",
+        tags={"service": "gradio-embeddings", "endpoint": EMBEDDINGS_URL},
+    )
+    _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)
+except Exception as exc:
+    logger.warning("MLflow tracking disabled: %s", exc)
+    _mlflow_client = None
+    _mlflow_run_id = None
+    _mlflow_step = 0
+    MLFLOW_ENABLED = False
+
+
+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:
+        return
+    try:
+        _mlflow_step += 1
+        ts = int(time.time() * 1000)
+        _mlflow_client.log_batch(
+            _mlflow_run_id,
+            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),
+            ],
+        )
+    except Exception:
+        logger.debug("MLflow log failed", exc_info=True)
+
 
 # HTTP client
 client = httpx.Client(timeout=60.0)
@@ -77,6 +131,9 @@ def generate_single_embedding(text: str) -> tuple[str, str, str]:
         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"
         
@@ -119,6 +176,9 @@ def compare_texts(text1: str, text2: str) -> tuple[str, str]:
         
         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"
@@ -167,6 +227,9 @@ def batch_embed(texts_input: str) -> tuple[str, str]:
     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)"
         

llm.py

@@ -30,6 +30,83 @@ LLM_URL = os.environ.get(
     "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/llm",
 )
 
+# ─── MLflow experiment tracking ──────────────────────────────────────────
+try:
+    import mlflow
+    from mlflow.tracking import MlflowClient
+
+    MLFLOW_TRACKING_URI = os.environ.get(
+        "MLFLOW_TRACKING_URI",
+        "http://mlflow.mlflow.svc.cluster.local:80",
+    )
+    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
+    _mlflow_client = MlflowClient()
+
+    # Ensure experiment exists
+    _experiment = _mlflow_client.get_experiment_by_name("gradio-llm-tuning")
+    if _experiment is None:
+        _experiment_id = _mlflow_client.create_experiment(
+            "gradio-llm-tuning",
+            artifact_location="/mlflow/artifacts/gradio-llm-tuning",
+        )
+    else:
+        _experiment_id = _experiment.experiment_id
+
+    # One persistent run per Gradio instance
+    _mlflow_run = mlflow.start_run(
+        experiment_id=_experiment_id,
+        run_name=f"gradio-llm-{os.environ.get('HOSTNAME', 'local')}",
+        tags={
+            "service": "gradio-llm",
+            "endpoint": LLM_URL,
+            "mlflow.runName": f"gradio-llm-{os.environ.get('HOSTNAME', 'local')}",
+        },
+    )
+    _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)
+except Exception as exc:
+    logger.warning("MLflow tracking disabled: %s", exc)
+    _mlflow_client = None
+    _mlflow_run_id = None
+    _mlflow_step = 0
+    MLFLOW_ENABLED = False
+
+
+def _log_llm_metrics(
+    latency: float,
+    prompt_tokens: int,
+    completion_tokens: int,
+    temperature: float,
+    max_tokens: int,
+    top_p: float,
+) -> None:
+    """Log inference metrics to MLflow (non-blocking best-effort)."""
+    global _mlflow_step
+    if not MLFLOW_ENABLED or _mlflow_client is None:
+        return
+    try:
+        _mlflow_step += 1
+        ts = int(time.time() * 1000)
+        total_tokens = prompt_tokens + completion_tokens
+        tps = completion_tokens / latency if latency > 0 else 0
+        _mlflow_client.log_batch(
+            _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("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("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). "
@@ -90,6 +167,16 @@ async def chat_stream(
             usage.get("completion_tokens", 0),
         )
 
+        # Log to MLflow
+        _log_llm_metrics(
+            latency=latency,
+            prompt_tokens=usage.get("prompt_tokens", 0),
+            completion_tokens=usage.get("completion_tokens", 0),
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+        )
+
         # Yield text progressively for a nicer streaming feel
         chunk_size = 4
         words = text.split(" ")
@@ -164,6 +251,16 @@ def single_prompt(
         text = result["choices"][0]["message"]["content"]
         usage = result.get("usage", {})
 
+        # Log to MLflow
+        _log_llm_metrics(
+            latency=latency,
+            prompt_tokens=usage.get("prompt_tokens", 0),
+            completion_tokens=usage.get("completion_tokens", 0),
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+        )
+
         metrics = f"""
 **Generation Metrics:**
 - Latency: {latency:.1f}s

stt.py

@@ -37,6 +37,67 @@ MLFLOW_TRACKING_URI = os.environ.get(
     "http://mlflow.mlflow.svc.cluster.local:80"
 )
 
+# ─── MLflow experiment tracking ──────────────────────────────────────────
+try:
+    import mlflow
+    from mlflow.tracking import MlflowClient
+
+    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
+    _mlflow_client = MlflowClient()
+
+    _experiment = _mlflow_client.get_experiment_by_name("gradio-stt-tuning")
+    if _experiment is None:
+        _experiment_id = _mlflow_client.create_experiment(
+            "gradio-stt-tuning",
+            artifact_location="/mlflow/artifacts/gradio-stt-tuning",
+        )
+    else:
+        _experiment_id = _experiment.experiment_id
+
+    _mlflow_run = mlflow.start_run(
+        experiment_id=_experiment_id,
+        run_name=f"gradio-stt-{os.environ.get('HOSTNAME', 'local')}",
+        tags={"service": "gradio-stt", "endpoint": STT_URL},
+    )
+    _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)
+except Exception as exc:
+    logger.warning("MLflow tracking disabled: %s", exc)
+    _mlflow_client = None
+    _mlflow_run_id = None
+    _mlflow_step = 0
+    MLFLOW_ENABLED = False
+
+
+def _log_stt_metrics(
+    latency: float, audio_duration: float, word_count: int, task: str,
+) -> None:
+    """Log STT inference metrics to MLflow (non-blocking best-effort)."""
+    global _mlflow_step
+    if not MLFLOW_ENABLED or _mlflow_client is None:
+        return
+    try:
+        _mlflow_step += 1
+        ts = int(time.time() * 1000)
+        rtf = latency / audio_duration if audio_duration > 0 else 0
+        _mlflow_client.log_batch(
+            _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("realtime_factor", rtf, ts, _mlflow_step),
+                mlflow.entities.Metric("word_count", word_count, ts, _mlflow_step),
+            ],
+            params=[] if _mlflow_step > 1 else [
+                mlflow.entities.Param("task", task),
+            ],
+        )
+    except Exception:
+        logger.debug("MLflow log failed", exc_info=True)
+
+
 # HTTP client with longer timeout for transcription
 client = httpx.Client(timeout=180.0)
 
@@ -117,6 +178,14 @@ def transcribe_audio(
         text = result.get("text", "")
         detected_language = result.get("language", "unknown")
 
+        # Log to MLflow
+        _log_stt_metrics(
+            latency=latency,
+            audio_duration=audio_duration,
+            word_count=len(text.split()),
+            task=task,
+        )
+        
         # Status message
         status = f"✅ Transcribed {audio_duration:.1f}s of audio in {latency*1000:.0f}ms"
         

tts.py

@@ -37,6 +37,66 @@ MLFLOW_TRACKING_URI = os.environ.get(
     "http://mlflow.mlflow.svc.cluster.local:80"
 )
 
+# ─── MLflow experiment tracking ──────────────────────────────────────────
+try:
+    import mlflow
+    from mlflow.tracking import MlflowClient
+
+    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
+    _mlflow_client = MlflowClient()
+
+    _experiment = _mlflow_client.get_experiment_by_name("gradio-tts-tuning")
+    if _experiment is None:
+        _experiment_id = _mlflow_client.create_experiment(
+            "gradio-tts-tuning",
+            artifact_location="/mlflow/artifacts/gradio-tts-tuning",
+        )
+    else:
+        _experiment_id = _experiment.experiment_id
+
+    _mlflow_run = mlflow.start_run(
+        experiment_id=_experiment_id,
+        run_name=f"gradio-tts-{os.environ.get('HOSTNAME', 'local')}",
+        tags={"service": "gradio-tts", "endpoint": TTS_URL},
+    )
+    _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)
+except Exception as exc:
+    logger.warning("MLflow tracking disabled: %s", exc)
+    _mlflow_client = None
+    _mlflow_run_id = None
+    _mlflow_step = 0
+    MLFLOW_ENABLED = False
+
+
+def _log_tts_metrics(
+    latency: float, audio_duration: float, text_chars: int, language: str,
+) -> None:
+    """Log TTS inference metrics to MLflow (non-blocking best-effort)."""
+    global _mlflow_step
+    if not MLFLOW_ENABLED or _mlflow_client is None:
+        return
+    try:
+        _mlflow_step += 1
+        ts = int(time.time() * 1000)
+        rtf = latency / audio_duration if audio_duration > 0 else 0
+        cps = text_chars / latency if latency > 0 else 0
+        _mlflow_client.log_batch(
+            _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("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),
+            ],
+        )
+    except Exception:
+        logger.debug("MLflow log failed", exc_info=True)
+
+
 # HTTP client with longer timeout for audio generation
 client = httpx.Client(timeout=120.0)
 
@@ -94,6 +154,14 @@ def synthesize_speech(text: str, language: str) -> tuple[str, tuple[int, np.ndar
         # Status message
         status = f"✅ Generated {duration:.2f}s of audio in {latency*1000:.0f}ms"
 
+        # Log to MLflow
+        _log_tts_metrics(
+            latency=latency,
+            audio_duration=duration,
+            text_chars=len(text),
+            language=lang_code,
+        )
+        
         # Metrics
         metrics = f"""
 **Audio Statistics:**