fix: remove unused imports and apply ruff formatting

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

stt.py

@@ -9,11 +9,11 @@ Features:
 - Translation mode
 - MLflow metrics logging
 """
+
 import os
 import time
 import logging
 import io
-import tempfile
 
 import gradio as gr
 import httpx
@@ -30,11 +30,10 @@ logger = logging.getLogger("stt-demo")
 STT_URL = os.environ.get(
     "STT_URL",
     # Default: Ray Serve whisper endpoint
-    "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/whisper"
+    "http://ai-inference-serve-svc.ai-ml.svc.cluster.local:8000/whisper",
 )
 MLFLOW_TRACKING_URI = os.environ.get(
-    "MLFLOW_TRACKING_URI",
-    "http://mlflow.mlflow.svc.cluster.local:80"
+    "MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80"
 )
 
 # ─── MLflow experiment tracking ──────────────────────────────────────────
@@ -62,7 +61,9 @@ try:
     _mlflow_run_id = _mlflow_run.info.run_id
     _mlflow_step = 0
     MLFLOW_ENABLED = True
-    logger.info("MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id)
+    logger.info(
+        "MLflow tracking enabled: experiment=%s run=%s", _experiment_id, _mlflow_run_id
+    )
 except Exception as exc:
     logger.warning("MLflow tracking disabled: %s", exc)
     _mlflow_client = None
@@ -72,7 +73,10 @@ except Exception as exc:
 
 
 def _log_stt_metrics(
-    latency: float, audio_duration: float, word_count: int, task: str,
+    latency: float,
+    audio_duration: float,
+    word_count: int,
+    task: str,
 ) -> None:
     """Log STT inference metrics to MLflow (non-blocking best-effort)."""
     global _mlflow_step
@@ -86,11 +90,15 @@ def _log_stt_metrics(
             _mlflow_run_id,
             metrics=[
                 mlflow.entities.Metric("latency_s", latency, ts, _mlflow_step),
-                mlflow.entities.Metric("audio_duration_s", audio_duration, ts, _mlflow_step),
+                mlflow.entities.Metric(
+                    "audio_duration_s", audio_duration, ts, _mlflow_step
+                ),
                 mlflow.entities.Metric("realtime_factor", rtf, ts, _mlflow_step),
                 mlflow.entities.Metric("word_count", word_count, ts, _mlflow_step),
             ],
-            params=[] if _mlflow_step > 1 else [
+            params=[]
+            if _mlflow_step > 1
+            else [
                 mlflow.entities.Param("task", task),
             ],
         )
@@ -124,57 +132,55 @@ LANGUAGES = {
 
 
 def transcribe_audio(
-    audio_input: tuple[int, np.ndarray] | str | None,
-    language: str,
-    task: str
+    audio_input: tuple[int, np.ndarray] | str | None, language: str, task: str
 ) -> tuple[str, str, str]:
     """Transcribe audio using the Whisper STT service."""
     if audio_input is None:
         return "❌ Please provide audio input", "", ""
-    
+
     try:
         start_time = time.time()
-        
+
         # Handle different input types
         if isinstance(audio_input, tuple):
             # Microphone input: (sample_rate, audio_data)
             sample_rate, audio_data = audio_input
-            
+
             # Convert to WAV bytes
             audio_buffer = io.BytesIO()
-            sf.write(audio_buffer, audio_data, sample_rate, format='WAV')
+            sf.write(audio_buffer, audio_data, sample_rate, format="WAV")
             audio_bytes = audio_buffer.getvalue()
             audio_duration = len(audio_data) / sample_rate
         else:
             # File path
-            with open(audio_input, 'rb') as f:
+            with open(audio_input, "rb") as f:
                 audio_bytes = f.read()
             # Get duration
             audio_data, sample_rate = sf.read(audio_input)
             audio_duration = len(audio_data) / sample_rate
-        
+
         # Prepare request
         lang_code = LANGUAGES.get(language)
-        
+
         files = {"file": ("audio.wav", audio_bytes, "audio/wav")}
         data = {"response_format": "json"}
-        
+
         if lang_code:
             data["language"] = lang_code
-        
+
         # Choose endpoint based on task
         if task == "Translate to English":
             endpoint = f"{STT_URL}/v1/audio/translations"
         else:
             endpoint = f"{STT_URL}/v1/audio/transcriptions"
-        
+
         # Send request
         response = client.post(endpoint, files=files, data=data)
         response.raise_for_status()
-        
+
         latency = time.time() - start_time
         result = response.json()
-        
+
         text = result.get("text", "")
         detected_language = result.get("language", "unknown")
 
@@ -185,24 +191,26 @@ def transcribe_audio(
             word_count=len(text.split()),
             task=task,
         )
-        
+
         # Status message
-        status = f"✅ Transcribed {audio_duration:.1f}s of audio in {latency*1000:.0f}ms"
-        
+        status = (
+            f"✅ Transcribed {audio_duration:.1f}s of audio in {latency * 1000:.0f}ms"
+        )
+
         # Metrics
         metrics = f"""
 **Transcription Statistics:**
 - Audio Duration: {audio_duration:.2f} seconds
-- Processing Time: {latency*1000:.0f}ms
-- Real-time Factor: {latency/audio_duration:.2f}x
+- Processing Time: {latency * 1000:.0f}ms
+- Real-time Factor: {latency / audio_duration:.2f}x
 - Detected Language: {detected_language}
 - Task: {task}
 - Word Count: {len(text.split())}
 - Character Count: {len(text)}
 """
-        
+
         return status, text, metrics
-        
+
     except httpx.HTTPStatusError as e:
         logger.exception("STT request failed")
         return f"❌ STT service error: {e.response.status_code}", "", ""
@@ -217,12 +225,12 @@ def check_service_health() -> str:
         response = client.get(f"{STT_URL}/health", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is healthy"
-        
+
         # Try v1/models endpoint (OpenAI-compatible)
         response = client.get(f"{STT_URL}/v1/models", timeout=5.0)
         if response.status_code == 200:
             return "🟢 Service is healthy"
-        
+
         return f"🟡 Service returned status {response.status_code}"
     except Exception as e:
         return f"🔴 Service unavailable: {str(e)}"
@@ -236,99 +244,89 @@ with gr.Blocks(theme=get_lab_theme(), css=CUSTOM_CSS, title="STT Demo") as demo:
 Test the **Whisper** speech-to-text service. Transcribe audio from microphone
 or file upload with support for 100+ languages.
 """)
-    
+
     # Service status
     with gr.Row():
         health_btn = gr.Button("🔄 Check Service", size="sm")
         health_status = gr.Textbox(label="Service Status", interactive=False)
-    
+
     health_btn.click(fn=check_service_health, outputs=health_status)
-    
+
     with gr.Tabs():
         # Tab 1: Microphone Input
         with gr.TabItem("🎤 Microphone"):
             with gr.Row():
                 with gr.Column():
                     mic_input = gr.Audio(
-                        label="Record Audio",
-                        sources=["microphone"],
-                        type="numpy"
+                        label="Record Audio", sources=["microphone"], type="numpy"
                     )
-                    
+
                     with gr.Row():
                         mic_language = gr.Dropdown(
                             choices=list(LANGUAGES.keys()),
                             value="Auto-detect",
-                            label="Language"
+                            label="Language",
                         )
                         mic_task = gr.Radio(
                             choices=["Transcribe", "Translate to English"],
                             value="Transcribe",
-                            label="Task"
+                            label="Task",
                         )
-                    
+
                     mic_btn = gr.Button("🎯 Transcribe", variant="primary")
-                
+
                 with gr.Column():
                     mic_status = gr.Textbox(label="Status", interactive=False)
                     mic_metrics = gr.Markdown(label="Metrics")
-            
-            mic_output = gr.Textbox(
-                label="Transcription",
-                lines=5
-            )
-            
+
+            mic_output = gr.Textbox(label="Transcription", lines=5)
+
             mic_btn.click(
                 fn=transcribe_audio,
                 inputs=[mic_input, mic_language, mic_task],
-                outputs=[mic_status, mic_output, mic_metrics]
+                outputs=[mic_status, mic_output, mic_metrics],
             )
-        
+
         # Tab 2: File Upload
         with gr.TabItem("📁 File Upload"):
             with gr.Row():
                 with gr.Column():
                     file_input = gr.Audio(
-                        label="Upload Audio File",
-                        sources=["upload"],
-                        type="filepath"
+                        label="Upload Audio File", sources=["upload"], type="filepath"
                     )
-                    
+
                     with gr.Row():
                         file_language = gr.Dropdown(
                             choices=list(LANGUAGES.keys()),
                             value="Auto-detect",
-                            label="Language"
+                            label="Language",
                         )
                         file_task = gr.Radio(
                             choices=["Transcribe", "Translate to English"],
                             value="Transcribe",
-                            label="Task"
+                            label="Task",
                         )
-                    
+
                     file_btn = gr.Button("🎯 Transcribe", variant="primary")
-                
+
                 with gr.Column():
                     file_status = gr.Textbox(label="Status", interactive=False)
                     file_metrics = gr.Markdown(label="Metrics")
-            
-            file_output = gr.Textbox(
-                label="Transcription",
-                lines=5
-            )
-            
+
+            file_output = gr.Textbox(label="Transcription", lines=5)
+
             file_btn.click(
                 fn=transcribe_audio,
                 inputs=[file_input, file_language, file_task],
-                outputs=[file_status, file_output, file_metrics]
+                outputs=[file_status, file_output, file_metrics],
             )
-            
+
             gr.Markdown("""
 **Supported formats:** WAV, MP3, FLAC, OGG, M4A, WEBM
 
 *For best results, use clear audio with minimal background noise.*
 """)
-        
+
         # Tab 3: Translation
         with gr.TabItem("🌍 Translation"):
             gr.Markdown("""
@@ -337,40 +335,33 @@ or file upload with support for 100+ languages.
 Upload or record audio in any language and get English translation.
 Whisper will automatically detect the source language.
 """)
-            
+
             with gr.Row():
                 with gr.Column():
                     trans_input = gr.Audio(
                         label="Audio Input",
                         sources=["microphone", "upload"],
-                        type="numpy"
+                        type="numpy",
                     )
                     trans_btn = gr.Button("🌍 Translate to English", variant="primary")
-                
+
                 with gr.Column():
                     trans_status = gr.Textbox(label="Status", interactive=False)
                     trans_metrics = gr.Markdown(label="Metrics")
-            
-            trans_output = gr.Textbox(
-                label="English Translation",
-                lines=5
-            )
-            
+
+            trans_output = gr.Textbox(label="English Translation", lines=5)
+
             def translate_audio(audio):
                 return transcribe_audio(audio, "Auto-detect", "Translate to English")
-            
+
             trans_btn.click(
                 fn=translate_audio,
                 inputs=trans_input,
-                outputs=[trans_status, trans_output, trans_metrics]
+                outputs=[trans_status, trans_output, trans_metrics],
             )
-    
+
     create_footer()
 
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)