llm streaming outputs, bumped up images.

kustomization.yaml

@@ -8,3 +8,8 @@ resources:
   - llm.yaml
   - tts.yaml
   - stt.yaml
+
+images:
+  - name: gitea-http.gitea.svc.cluster.local:3000/daviestechlabs/gradio-ui
+    newName: registry.lab.daviestechlabs.io/daviestechlabs/gradio-ui
+    newTag: "0.0.7"

llm.py

@@ -3,13 +3,14 @@
 LLM Chat Demo - Gradio UI for testing vLLM inference service.
 
 Features:
-- Multi-turn chat with streaming responses
+- Multi-turn chat with true SSE streaming responses
 - Configurable temperature, max tokens, top-p
 - System prompt customisation
 - Token usage and latency metrics
 - Chat history management
 """
 
+import json
 import os
 import time
 import logging
@@ -127,6 +128,27 @@ async_client = httpx.AsyncClient(timeout=httpx.Timeout(300.0, connect=30.0))
 sync_client = httpx.Client(timeout=httpx.Timeout(60.0, connect=10.0))
 
 
+def _extract_content(content) -> str:
+    """Extract plain text from message content.
+
+    Handles both plain strings and Gradio 6.x content-parts format:
+        [{"type": "text", "text": "..."}]  or  [{"text": "..."}]
+    """
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        parts = []
+        for item in content:
+            if isinstance(item, dict):
+                parts.append(item.get("text", item.get("content", str(item))))
+            elif isinstance(item, str):
+                parts.append(item)
+            else:
+                parts.append(str(item))
+        return "".join(parts)
+    return str(content)
+
+
 async def chat_stream(
     message: str,
     history: list[dict[str, str]],
@@ -135,18 +157,21 @@ async def chat_stream(
     max_tokens: int,
     top_p: float,
 ):
-    """Stream chat responses from the vLLM endpoint."""
+    """Stream chat responses from the vLLM endpoint via SSE."""
     if not message.strip():
         yield ""
         return
 
-    # Build message list from history
+    # Build message list from history, normalising content-parts
     messages = []
     if system_prompt.strip():
         messages.append({"role": "system", "content": system_prompt})
 
     for entry in history:
-        messages.append({"role": entry["role"], "content": entry["content"]})
+        messages.append({
+            "role": entry["role"],
+            "content": _extract_content(entry["content"]),
+        })
 
     messages.append({"role": "user", "content": message})
 
@@ -155,16 +180,58 @@ async def chat_stream(
         "temperature": temperature,
         "max_tokens": max_tokens,
         "top_p": top_p,
+        "stream": True,
     }
 
     start_time = time.time()
 
     try:
-        response = await async_client.post(LLM_URL, json=payload)
+        # Try true SSE streaming first
+        async with async_client.stream("POST", LLM_URL, json=payload) as response:
             response.raise_for_status()
+            content_type = response.headers.get("content-type", "")
 
-        result = response.json()
-        text = result["choices"][0]["message"]["content"]
+            if "text/event-stream" in content_type:
+                # SSE streaming — accumulate deltas
+                full_text = ""
+                async for line in response.aiter_lines():
+                    if not line.startswith("data: "):
+                        continue
+                    data = line[6:]
+                    if data.strip() == "[DONE]":
+                        break
+                    try:
+                        chunk = json.loads(data)
+                        delta = (
+                            chunk.get("choices", [{}])[0]
+                            .get("delta", {})
+                            .get("content", "")
+                        )
+                        if delta:
+                            full_text += delta
+                            yield full_text
+                    except json.JSONDecodeError:
+                        continue
+
+                latency = time.time() - start_time
+                logger.info("LLM streamed response: %d chars in %.1fs", len(full_text), latency)
+
+                # Best-effort metrics from the final SSE payload
+                _log_llm_metrics(
+                    latency=latency,
+                    prompt_tokens=0,
+                    completion_tokens=len(full_text.split()),
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                    top_p=top_p,
+                )
+            else:
+                # Non-streaming fallback (endpoint doesn't support stream)
+                body = await response.aread()
+                result = json.loads(body)
+                text = _extract_content(
+                    result["choices"][0]["message"]["content"]
+                )
                 latency = time.time() - start_time
                 usage = result.get("usage", {})
 
@@ -176,7 +243,6 @@ async def chat_stream(
                     usage.get("completion_tokens", 0),
                 )
 
-        # Log to MLflow
                 _log_llm_metrics(
                     latency=latency,
                     prompt_tokens=usage.get("prompt_tokens", 0),
@@ -186,7 +252,7 @@ async def chat_stream(
                     top_p=top_p,
                 )
 
-        # Yield text progressively for a nicer streaming feel
+                # Yield text progressively for a nicer feel
                 chunk_size = 4
                 words = text.split(" ")
                 partial = ""
@@ -266,7 +332,7 @@ def single_prompt(
         result = response.json()
         latency = time.time() - start_time
 
-        text = result["choices"][0]["message"]["content"]
+        text = _extract_content(result["choices"][0]["message"]["content"])
         usage = result.get("usage", {})
 
         # Log to MLflow
@@ -325,7 +391,7 @@ Chat with **Llama 3.1 70B** (AWQ INT4) served via vLLM on AMD Strix Halo (ROCm).
         )
         with gr.Row():
             temperature = gr.Slider(0.0, 2.0, value=0.7, step=0.05, label="Temperature")
-            max_tokens = gr.Slider(16, 4096, value=512, step=16, label="Max Tokens")
+            max_tokens = gr.Slider(16, 8192, value=2048, step=16, label="Max Tokens")
             top_p = gr.Slider(0.0, 1.0, value=0.95, step=0.01, label="Top-p")
 
     with gr.Tabs():