style: fix ruff lint and formatting issues

- tts.py: rename ambiguous variable 'l' to 'line' (E741)
- tts.py, llm.py: apply ruff formatter

.gitea/workflows/ci.yml

@@ -0,0 +1,224 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+env:
+  NTFY_URL: http://ntfy.observability.svc.cluster.local:80
+  REGISTRY: gitea-http.gitea.svc.cluster.local:3000/daviestechlabs
+  REGISTRY_HOST: gitea-http.gitea.svc.cluster.local:3000
+  IMAGE_NAME: gradio-ui
+  KUSTOMIZE_NAMESPACE: ai-ml
+
+jobs:
+  lint:
+    name: Lint
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up uv
+        run: curl -LsSf https://astral.sh/uv/install.sh | sh && echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+      - name: Run ruff check
+        run: uvx ruff check .
+
+      - name: Run ruff format check
+        run: uvx ruff format --check .
+
+  release:
+    name: Release
+    runs-on: ubuntu-latest
+    needs: [lint]
+    if: gitea.ref == 'refs/heads/main' && gitea.event_name == 'push'
+    outputs:
+      version: ${{ steps.version.outputs.version }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Determine version bump
+        id: version
+        run: |
+          LATEST=$(git describe --tags --abbrev=0 2>/dev/null || echo "v0.0.0")
+          VERSION=${LATEST#v}
+          IFS='.' read -r MAJOR MINOR PATCH <<< "$VERSION"
+
+          MSG="${{ gitea.event.head_commit.message }}"
+          if echo "$MSG" | grep -qiE "^major:|BREAKING CHANGE"; then
+            MAJOR=$((MAJOR + 1)); MINOR=0; PATCH=0
+            BUMP="major"
+          elif echo "$MSG" | grep -qiE "^(minor:|feat:)"; then
+            MINOR=$((MINOR + 1)); PATCH=0
+            BUMP="minor"
+          else
+            PATCH=$((PATCH + 1))
+            BUMP="patch"
+          fi
+
+          NEW_VERSION="v${MAJOR}.${MINOR}.${PATCH}"
+          echo "version=$NEW_VERSION" >> $GITHUB_OUTPUT
+          echo "bump=$BUMP" >> $GITHUB_OUTPUT
+          echo "Bumping $LATEST → $NEW_VERSION ($BUMP)"
+
+      - name: Create and push tag
+        run: |
+          git config user.name "gitea-actions[bot]"
+          git config user.email "actions@git.daviestechlabs.io"
+          git tag -a ${{ steps.version.outputs.version }} -m "Release ${{ steps.version.outputs.version }}"
+          git push origin ${{ steps.version.outputs.version }}
+
+  docker:
+    name: Docker Build & Push
+    runs-on: ubuntu-latest
+    needs: [lint, release]
+    if: gitea.ref == 'refs/heads/main' && gitea.event_name == 'push'
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Login to Gitea Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY_HOST }}
+          username: ${{ secrets.REGISTRY_USER }}
+          password: ${{ secrets.REGISTRY_TOKEN }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          buildkitd-config-inline: |
+            [registry."gitea-http.gitea.svc.cluster.local:3000"]
+              http = true
+              insecure = true
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=semver,pattern={{version}},value=${{ needs.release.outputs.version }}
+            type=semver,pattern={{major}}.{{minor}},value=${{ needs.release.outputs.version }}
+            type=raw,value=latest,enable={{is_default_branch}}
+
+      - name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+
+  deploy:
+    name: Deploy to Kubernetes
+    runs-on: ubuntu-latest
+    needs: [docker, release]
+    if: gitea.ref == 'refs/heads/main' && gitea.event_name == 'push'
+    container:
+      image: catthehacker/ubuntu:act-latest
+      volumes:
+        - /secrets/kubeconfig:/secrets/kubeconfig
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Install kubectl
+        run: |
+          curl -LO "https://dl.k8s.io/release/$(curl -Ls https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
+          chmod +x kubectl && sudo mv kubectl /usr/local/bin/
+
+      - name: Update image tag in manifests
+        env:
+          KUBECONFIG: /secrets/kubeconfig/config
+        run: |
+          VERSION="${{ needs.release.outputs.version }}"
+          VERSION="${VERSION#v}"
+          for DEPLOY in llm embeddings stt tts; do
+            sed -i "s|image: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:.*|image: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${VERSION}|" "${DEPLOY}.yaml"
+          done
+
+      - name: Apply kustomization
+        env:
+          KUBECONFIG: /secrets/kubeconfig/config
+        run: |
+          kubectl apply -k . --namespace ${{ env.KUSTOMIZE_NAMESPACE }}
+
+      - name: Rollout restart deployments
+        env:
+          KUBECONFIG: /secrets/kubeconfig/config
+        run: |
+          for DEPLOY in llm-ui embeddings-ui stt-ui tts-ui; do
+            kubectl rollout restart deployment/${DEPLOY} -n ${{ env.KUSTOMIZE_NAMESPACE }} 2>/dev/null || true
+          done
+
+      - name: Wait for rollout
+        env:
+          KUBECONFIG: /secrets/kubeconfig/config
+        run: |
+          for DEPLOY in llm-ui embeddings-ui stt-ui tts-ui; do
+            kubectl rollout status deployment/${DEPLOY} -n ${{ env.KUSTOMIZE_NAMESPACE }} --timeout=120s 2>/dev/null || true
+          done
+
+  notify:
+    name: Notify
+    runs-on: ubuntu-latest
+    needs: [lint, release, docker, deploy]
+    if: always()
+    steps:
+      - name: Notify on success
+        if: needs.lint.result == 'success' && needs.docker.result == 'success'
+        run: |
+          curl -s \
+            -H "Title: ✅ CI Passed: ${{ gitea.repository }}" \
+            -H "Priority: default" \
+            -H "Tags: white_check_mark,github" \
+            -H "Click: ${{ gitea.server_url }}/${{ gitea.repository }}/actions/runs/${{ gitea.run_id }}" \
+            -d "Branch: ${{ gitea.ref_name }}
+          Commit: ${{ gitea.event.head_commit.message || gitea.sha }}
+          Release: ${{ needs.release.result == 'success' && needs.release.outputs.version || 'skipped' }}
+          Docker: ${{ needs.docker.result }}
+          Deploy: ${{ needs.deploy.result }}" \
+            ${{ env.NTFY_URL }}/gitea-ci
+
+      - name: Notify on deploy success
+        if: needs.deploy.result == 'success'
+        run: |
+          curl -s \
+            -H "Title: 🚀 Deployed: ${{ gitea.repository }}" \
+            -H "Priority: default" \
+            -H "Tags: rocket,kubernetes" \
+            -H "Click: ${{ gitea.server_url }}/${{ gitea.repository }}/actions/runs/${{ gitea.run_id }}" \
+            -d "Version: ${{ needs.release.outputs.version }}
+          Namespace: ${{ env.KUSTOMIZE_NAMESPACE }}
+          Apps: llm-ui, embeddings-ui, stt-ui, tts-ui" \
+            ${{ env.NTFY_URL }}/gitea-ci
+
+      - name: Notify on failure
+        if: needs.lint.result == 'failure' || needs.docker.result == 'failure' || needs.deploy.result == 'failure'
+        run: |
+          curl -s \
+            -H "Title: ❌ CI Failed: ${{ gitea.repository }}" \
+            -H "Priority: high" \
+            -H "Tags: x,github" \
+            -H "Click: ${{ gitea.server_url }}/${{ gitea.repository }}/actions/runs/${{ gitea.run_id }}" \
+            -d "Branch: ${{ gitea.ref_name }}
+          Commit: ${{ gitea.event.head_commit.message || gitea.sha }}
+          Lint: ${{ needs.lint.result }}
+          Docker: ${{ needs.docker.result }}
+          Deploy: ${{ needs.deploy.result }}" \
+            ${{ env.NTFY_URL }}/gitea-ci

.gitignore

@@ -0,0 +1 @@
+__pycache__/

embeddings.py

@@ -9,6 +9,7 @@ Features:
 - MLflow metrics logging
 - Visual embedding dimension display
 """
+
 import os
 import time
 import logging
@@ -26,14 +27,79 @@ 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"
-)
-MLFLOW_TRACKING_URI = os.environ.get(
-    "MLFLOW_TRACKING_URI",
-    "http://mlflow.mlflow.svc.cluster.local:80"
+    "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/embeddings",
 )
+# ─── 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()
+
+    _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)
@@ -42,17 +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
 
 
@@ -67,26 +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:**
@@ -96,11 +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)}", "", ""
@@ -110,15 +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"
@@ -132,7 +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}
 
@@ -141,17 +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)}", ""
@@ -160,16 +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)
-        
-        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 = []
@@ -179,16 +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
 
@@ -198,10 +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)}", ""
@@ -227,14 +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"):
@@ -243,71 +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.",
@@ -315,13 +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)

embeddings.yaml

@@ -20,7 +20,7 @@ spec:
     spec:
       containers:
         - name: gradio
-          image: ghcr.io/billy-davies-2/llm-apps:v2-202602120526
+          image: gitea-http.gitea.svc.cluster.local:3000/daviestechlabs/gradio-ui:latest
           imagePullPolicy: Always
           command: ["python", "embeddings.py"]
           ports:

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

@@ -3,16 +3,17 @@
 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
-import json
 
 import gradio as gr
 import httpx
@@ -30,6 +31,92 @@ 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). "
@@ -38,7 +125,28 @@ DEFAULT_SYSTEM_PROMPT = (
 
 # Use async client for streaming
 async_client = httpx.AsyncClient(timeout=httpx.Timeout(300.0, connect=30.0))
-sync_client = httpx.Client(timeout=10.0)
+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(
@@ -49,18 +157,23 @@ 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})
 
@@ -69,35 +182,86 @@ 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)
-        response.raise_for_status()
+        # 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"]
-        latency = time.time() - start_time
-        usage = result.get("usage", {})
+            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
 
-        logger.info(
-            "LLM response: %d tokens in %.1fs (prompt=%d, completion=%d)",
-            usage.get("total_tokens", 0),
-            latency,
-            usage.get("prompt_tokens", 0),
-            usage.get("completion_tokens", 0),
-        )
+                latency = time.time() - start_time
+                logger.info(
+                    "LLM streamed response: %d chars in %.1fs", len(full_text), latency
+                )
 
-        # Yield text progressively for a nicer streaming feel
-        chunk_size = 4
-        words = text.split(" ")
-        partial = ""
-        for i, word in enumerate(words):
-            partial += ("" if i == 0 else " ") + word
-            if i % chunk_size == 0 or i == len(words) - 1:
-                yield partial
+                # 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", {})
+
+                logger.info(
+                    "LLM response: %d tokens in %.1fs (prompt=%d, completion=%d)",
+                    usage.get("total_tokens", 0),
+                    latency,
+                    usage.get("prompt_tokens", 0),
+                    usage.get("completion_tokens", 0),
+                )
+
+                _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 feel
+                chunk_size = 4
+                words = text.split(" ")
+                partial = ""
+                for i, word in enumerate(words):
+                    partial += ("" if i == 0 else " ") + word
+                    if i % chunk_size == 0 or i == len(words) - 1:
+                        yield partial
 
     except httpx.HTTPStatusError as e:
         logger.exception("LLM request failed")
@@ -112,6 +276,13 @@ async def chat_stream(
 def check_service_health() -> str:
     """Check if the LLM service is reachable."""
     try:
+        # Try a lightweight GET against the Ray Serve base first.
+        # This avoids burning GPU time on a full inference round-trip.
+        base_url = LLM_URL.rsplit("/", 1)[0]  # strip /llm path
+        response = sync_client.get(f"{base_url}/-/routes")
+        if response.status_code == 200:
+            return "🟢 LLM service is healthy"
+        # Fall back to a minimal inference probe
         response = sync_client.post(
             LLM_URL,
             json={
@@ -125,6 +296,8 @@ def check_service_health() -> str:
         return f"🟡 LLM responded with status {response.status_code}"
     except httpx.ConnectError:
         return "🔴 Cannot connect to LLM service"
+    except httpx.TimeoutException:
+        return "🟡 LLM service is reachable but slow to respond"
     except Exception as e:
         return f"🔴 Service unavailable: {e}"
 
@@ -161,16 +334,26 @@ 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
+        _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
-- 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
 
@@ -210,7 +393,7 @@ 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():
@@ -218,18 +401,15 @@ Chat with **Llama 3.1 70B** (AWQ INT4) served via vLLM on AMD Strix Halo (ROCm).
         with gr.TabItem("💬 Chat"):
             chatbot = gr.ChatInterface(
                 fn=chat_stream,
-                type="messages",
                 additional_inputs=[system_prompt, temperature, max_tokens, top_p],
                 examples=[
-                    "Hello! What can you tell me about yourself?",
-                    "Explain how a GPU executes a matrix multiplication.",
-                    "Write a Python function to compute the Fibonacci sequence.",
-                    "What are the pros and cons of running LLMs on AMD GPUs?",
+                    ["Hello! What can you tell me about yourself?"],
+                    ["Explain how a GPU executes a matrix multiplication."],
+                    ["Write a Python function to compute the Fibonacci sequence."],
+                    ["What are the pros and cons of running LLMs on AMD GPUs?"],
                 ],
                 chatbot=gr.Chatbot(
                     height=520,
-                    type="messages",
-                    show_copy_button=True,
                     placeholder="Type a message to start chatting...",
                 ),
             )
@@ -257,9 +437,13 @@ 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],

llm.yaml

@@ -20,7 +20,7 @@ spec:
     spec:
       containers:
         - name: gradio
-          image: ghcr.io/billy-davies-2/llm-apps:v2-202602120526
+          image: gitea-http.gitea.svc.cluster.local:3000/daviestechlabs/gradio-ui:latest
           imagePullPolicy: Always
           command: ["python", "llm.py"]
           ports:
@@ -53,7 +53,7 @@ spec:
             initialDelaySeconds: 5
             periodSeconds: 10
       imagePullSecrets:
-        - name: ghcr-registry
+        - name: gitea-registry
 ---
 apiVersion: v1
 kind: Service

renovate.json

@@ -0,0 +1,7 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "extends": [
+    "local>daviestechlabs/renovate-config",
+    "local>daviestechlabs/renovate-config:python"
+  ]
+}

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
@@ -30,13 +30,82 @@ 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 ──────────────────────────────────────────
+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)
 
@@ -63,77 +132,85 @@ 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")
-        
+
+        # 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"
-        
+        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}", "", ""
@@ -148,12 +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)}"
@@ -167,99 +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("""
@@ -268,40 +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)

stt.yaml

@@ -20,7 +20,7 @@ spec:
     spec:
       containers:
         - name: gradio
-          image: ghcr.io/billy-davies-2/llm-apps:v2-202602120526
+          image: gitea-http.gitea.svc.cluster.local:3000/daviestechlabs/gradio-ui:latest
           imagePullPolicy: Always
           command: ["python", "stt.py"]
           ports:
@@ -28,7 +28,7 @@ spec:
               name: http
               protocol: TCP
           env:
-            - name: WHISPER_URL
+            - name: STT_URL
               # Ray Serve endpoint - routes to /whisper prefix
               value: "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/whisper"
             - name: MLFLOW_TRACKING_URI

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

@@ -5,19 +5,20 @@ TTS Demo - Gradio UI for testing Text-to-Speech service.
 Features:
 - Text input with language selection
 - Audio playback of synthesized speech
-- Voice/speaker selection (when available)
+- Sentence-level chunking for better quality
+- Speed control
 - MLflow metrics logging
-- Multiple TTS backends support (Coqui XTTS, Piper, etc.)
 """
+
 import os
+import re
 import time
 import logging
 import io
-import base64
+import wave
 
 import gradio as gr
 import httpx
-import soundfile as sf
 import numpy as np
 
 from theme import get_lab_theme, CUSTOM_CSS, create_footer
@@ -30,13 +31,79 @@ 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 ──────────────────────────────────────────
+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)
 
@@ -60,99 +127,304 @@ LANGUAGES = {
     "Hungarian": "hu",
 }
 
+# ─── Text preprocessing ─────────────────────────────────────────────────
 
-def synthesize_speech(text: str, language: str) -> tuple[str, tuple[int, np.ndarray] | None, str]:
-    """Synthesize speech from text using the TTS service."""
+_SENTENCE_RE = re.compile(r"(?<=[.!?;])\s+|(?<=\n)\s*", re.MULTILINE)
+
+_DIGIT_WORDS = {
+    "0": "zero",
+    "1": "one",
+    "2": "two",
+    "3": "three",
+    "4": "four",
+    "5": "five",
+    "6": "six",
+    "7": "seven",
+    "8": "eight",
+    "9": "nine",
+}
+
+
+def _expand_numbers(text: str) -> str:
+    """Expand standalone single digits to words for clearer pronunciation."""
+    return re.sub(
+        r"\b(\d)\b",
+        lambda m: _DIGIT_WORDS.get(m.group(0), m.group(0)),
+        text,
+    )
+
+
+def _clean_text(text: str) -> str:
+    """Clean and normalise text for TTS input."""
+    text = re.sub(r"[ \t]+", " ", text)
+    text = "\n".join(line.strip() for line in text.splitlines())
+    # Strip markdown / code-fence characters
+    text = re.sub(r"[*#~`|<>{}[\]\\]", "", text)
+    # Expand common symbols
+    text = text.replace("&", " and ")
+    text = text.replace("@", " at ")
+    text = text.replace("%", " percent ")
+    text = text.replace("+", " plus ")
+    text = text.replace("=", " equals ")
+    text = _expand_numbers(text)
+    return text.strip()
+
+
+def _split_sentences(text: str) -> list[str]:
+    """Split text into sentences suitable for TTS.
+
+    Keeps sentences short for best quality while preserving natural phrasing.
+    Very long segments are further split on commas / semicolons.
+    """
+    text = _clean_text(text)
+    if not text:
+        return []
+
+    raw_parts = _SENTENCE_RE.split(text)
+    sentences: list[str] = []
+    for part in raw_parts:
+        part = part.strip()
+        if not part:
+            continue
+        if len(part) > 200:
+            for sp in re.split(r"(?<=[,;])\s+", part):
+                sp = sp.strip()
+                if sp:
+                    sentences.append(sp)
+        else:
+            sentences.append(part)
+    return sentences
+
+
+# ─── Audio helpers ───────────────────────────────────────────────────────
+
+
+def _read_wav_bytes(data: bytes) -> tuple[int, np.ndarray]:
+    """Read WAV audio from bytes, handling scipy wavfile and standard WAV.
+
+    Returns (sample_rate, float32_audio) with values in [-1, 1].
+    """
+    buf = io.BytesIO(data)
+
+    # Try stdlib wave module first — most robust for PCM WAV from scipy
+    try:
+        with wave.open(buf, "rb") as wf:
+            sr = wf.getframerate()
+            n_frames = wf.getnframes()
+            n_channels = wf.getnchannels()
+            sampwidth = wf.getsampwidth()
+            raw = wf.readframes(n_frames)
+
+        if sampwidth == 2:
+            audio = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+        elif sampwidth == 4:
+            audio = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
+        elif sampwidth == 1:
+            audio = (
+                np.frombuffer(raw, dtype=np.uint8).astype(np.float32) - 128.0
+            ) / 128.0
+        else:
+            raise ValueError(f"Unsupported sample width: {sampwidth}")
+
+        if n_channels > 1:
+            audio = audio.reshape(-1, n_channels).mean(axis=1)
+
+        return sr, audio
+    except Exception as exc:
+        logger.debug("wave module failed (%s), trying soundfile", exc)
+
+    # Fallback: soundfile (handles FLAC, OGG, etc.)
+    buf.seek(0)
+    try:
+        import soundfile as sf
+
+        audio, sr = sf.read(buf, dtype="float32")
+        if audio.ndim > 1:
+            audio = audio.mean(axis=1)
+        return sr, audio
+    except Exception as exc:
+        logger.debug("soundfile failed (%s), attempting raw PCM", exc)
+
+    # Last resort: raw 16-bit PCM at 22050 Hz
+    logger.warning(
+        "Could not parse WAV header (len=%d, first 4 bytes=%r); raw PCM decode",
+        len(data),
+        data[:4],
+    )
+    audio = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
+    return 22050, audio
+
+
+def _concat_audio(
+    chunks: list[tuple[int, np.ndarray]], pause_ms: int = 200
+) -> tuple[int, np.ndarray]:
+    """Concatenate (sample_rate, audio) chunks with silence gaps."""
+    if not chunks:
+        return 22050, np.array([], dtype=np.float32)
+    if len(chunks) == 1:
+        return chunks[0]
+
+    sr = chunks[0][0]
+    silence = np.zeros(int(sr * pause_ms / 1000), dtype=np.float32)
+
+    parts: list[np.ndarray] = []
+    for sample_rate, audio in chunks:
+        if sample_rate != sr:
+            ratio = sr / sample_rate
+            indices = np.arange(0, len(audio), 1.0 / ratio).astype(int)
+            indices = indices[indices < len(audio)]
+            audio = audio[indices]
+        parts.append(audio)
+        parts.append(silence)
+
+    if parts:
+        parts.pop()  # remove trailing silence
+    return sr, np.concatenate(parts)
+
+
+# ─── TTS synthesis ───────────────────────────────────────────────────────
+
+
+def _synthesize_chunk(text: str, lang_code: str, speed: float = 1.0) -> bytes:
+    """Synthesize a single text chunk via the TTS backend.
+
+    Uses the JSON POST endpoint (no URL length limits, supports speed).
+    Falls back to the Coqui-compatible GET endpoint if POST fails.
+    """
+    import base64 as b64
+
+    # Try JSON POST first
+    try:
+        resp = client.post(
+            TTS_URL,
+            json={
+                "text": text,
+                "language": lang_code,
+                "speed": speed,
+                "return_base64": True,
+            },
+        )
+        resp.raise_for_status()
+        ct = resp.headers.get("content-type", "")
+        if "application/json" in ct:
+            body = resp.json()
+            if "error" in body:
+                raise RuntimeError(body["error"])
+            audio_b64 = body.get("audio", "")
+            if audio_b64:
+                return b64.b64decode(audio_b64)
+        # Non-JSON response — treat as raw audio bytes
+        return resp.content
+    except Exception:
+        logger.debug(
+            "POST endpoint failed, falling back to GET /api/tts", exc_info=True
+        )
+
+    # Fallback: Coqui-compatible GET (no speed control)
+    resp = client.get(
+        f"{TTS_URL}/api/tts",
+        params={"text": text, "language_id": lang_code},
+    )
+    resp.raise_for_status()
+    return resp.content
+
+
+def synthesize_speech(
+    text: str, language: str, speed: float
+) -> tuple[str, tuple[int, np.ndarray] | None, str]:
+    """Synthesize speech from text using the TTS service.
+
+    Long text is split into sentences and synthesized individually
+    for better quality, then concatenated with natural pauses.
+    """
     if not text.strip():
         return "❌ Please enter some text", None, ""
-    
+
     lang_code = LANGUAGES.get(language, "en")
-    
+    sentences = _split_sentences(text)
+    if not sentences:
+        return "❌ No speakable text found after cleaning", None, ""
+
     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}
-        )
-        response.raise_for_status()
-        
+        audio_chunks: list[tuple[int, np.ndarray]] = []
+
+        for sentence in sentences:
+            raw_audio = _synthesize_chunk(sentence, lang_code, speed)
+            sr, audio = _read_wav_bytes(raw_audio)
+            audio_chunks.append((sr, audio))
+
+        sample_rate, audio_data = _concat_audio(audio_chunks)
         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"
-        
-        # Metrics
+        duration = len(audio_data) / sample_rate if sample_rate > 0 else 0
+
+        n_chunks = len(sentences)
+        status = (
+            f"✅ Generated {duration:.2f}s of audio in {latency * 1000:.0f}ms"
+            f" ({n_chunks} sentence{'s' if n_chunks != 1 else ''})"
+        )
+
+        _log_tts_metrics(
+            latency=latency,
+            audio_duration=duration,
+            text_chars=len(text),
+            language=lang_code,
+        )
+
         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
+- Size: {len(audio_data) * 2 / 1024:.1f} KB
+- Generation Time: {latency * 1000:.0f}ms
+- Real-time Factor: {latency / duration:.2f}x
 - Language: {language} ({lang_code})
+- Speed: {speed:.1f}x
+- Sentences: {n_chunks}
 - 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, ""
     except Exception as e:
         logger.exception("TTS synthesis failed")
-        return f"❌ Error: {str(e)}", None, ""
+        return f"❌ Error: {e}", None, ""
 
 
 def check_service_health() -> str:
     """Check if the TTS service is healthy."""
     try:
-        # Try the health endpoint first
         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)}"
+        return f"🔴 Service unavailable: {e}"
 
 
-# Build the Gradio app
+# ─── Gradio UI ───────────────────────────────────────────────────────────
+
 with gr.Blocks(theme=get_lab_theme(), css=CUSTOM_CSS, title="TTS Demo") as demo:
     gr.Markdown("""
 # 🔊 Text-to-Speech Demo
 
 Test the **Coqui XTTS** text-to-speech service. Convert text to natural-sounding speech
-in multiple languages.
+in multiple languages. Long text is automatically split into sentences for better quality.
 """)
-    
-    # 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"):
             with gr.Row():
                 with gr.Column(scale=2):
@@ -160,114 +432,140 @@ 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",
+                        )
+                        speed = gr.Slider(
+                            minimum=0.5,
+                            maximum=2.0,
+                            value=1.0,
+                            step=0.1,
+                            label="Speed",
+                        )
+                        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]
+                inputs=[text_input, language, speed],
+                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"],
-                    ["Hola! Bienvenido al laboratorio de tecnología.", "Spanish"],
-                    ["Guten Tag! Willkommen im Techniklabor.", "German"],
+                    [
+                        "Hello! Welcome to Davies Tech Labs. This is a demonstration of our text-to-speech system.",
+                        "English",
+                        1.0,
+                    ],
+                    [
+                        "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet.",
+                        "English",
+                        1.0,
+                    ],
+                    [
+                        "Bonjour! Bienvenue au laboratoire technique de Davies.",
+                        "French",
+                        1.0,
+                    ],
+                    ["Hola! Bienvenido al laboratorio de tecnología.", "Spanish", 1.0],
+                    ["Guten Tag! Willkommen im Techniklabor.", "German", 1.0],
                 ],
-                inputs=[text_input, language],
+                inputs=[text_input, language, speed],
             )
-        
-        # 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_speed = gr.Slider(
+                    minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed"
+                )
+
             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)
+
+            def compare_languages(text, l1, l2, spd):
+                s1, a1, _ = synthesize_speech(text, l1, spd)
+                s2, a2, _ = synthesize_speech(text, l2, spd)
                 return s1, a1, s2, a2
-            
+
             compare_btn.click(
                 fn=compare_languages,
-                inputs=[compare_text, lang1, lang2],
-                outputs=[status1, audio1, status2, audio2]
+                inputs=[compare_text, lang1, lang2, compare_speed],
+                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_speed = gr.Slider(
+                minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed"
             )
             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"
+            batch_audio = gr.Audio(label="Combined Audio", type="numpy")
+
+            def batch_synthesize(texts_raw: str, lang: str, spd: float):
+                lines = [
+                    line.strip()
+                    for line in texts_raw.strip().splitlines()
+                    if line.strip()
+                ]
+                if not lines:
+                    return "❌ Please enter at least one line of text", None
+                combined = "\n".join(lines)
+                status, audio, _ = synthesize_speech(combined, lang, spd)
+                return status, audio
+
+            batch_btn.click(
+                fn=batch_synthesize,
+                inputs=[batch_input, batch_lang, batch_speed],
+                outputs=[batch_status, batch_audio],
             )
-            
-            # 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)

tts.yaml

@@ -20,7 +20,7 @@ spec:
     spec:
       containers:
         - name: gradio
-          image: ghcr.io/billy-davies-2/llm-apps:v2-202602120526
+          image: gitea-http.gitea.svc.cluster.local:3000/daviestechlabs/gradio-ui:latest
           imagePullPolicy: Always
           command: ["python", "tts.py"]
           ports: