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().

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