\🔥 CRITICAL: Fix unterminated triple-quoted string syntax error"

app.py.backup

@@ -3,6 +3,7 @@ import torch
 import tempfile
 import gradio as gr
 from fastapi import FastAPI, HTTPException
+from fastapi.staticfiles import StaticFiles
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, HttpUrl
 import subprocess
@@ -25,23 +26,50 @@ load_dotenv()
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-app = FastAPI(title="OmniAvatar-14B API with ElevenLabs", version="1.0.0")
+# Set environment variables for matplotlib, gradio, and huggingface cache
+os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
+os.environ['GRADIO_ALLOW_FLAGGING'] = 'never'
+os.environ['HF_HOME'] = '/tmp/huggingface'
+# Use HF_HOME instead of deprecated TRANSFORMERS_CACHE
+os.environ['HF_DATASETS_CACHE'] = '/tmp/huggingface/datasets'
+os.environ['HUGGINGFACE_HUB_CACHE'] = '/tmp/huggingface/hub'
 
-# Add CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+# FastAPI app will be created after lifespan is defined
+
+
+
+# Create directories with proper permissions
+os.makedirs("outputs", exist_ok=True)
+os.makedirs("/tmp/matplotlib", exist_ok=True)
+os.makedirs("/tmp/huggingface", exist_ok=True)
+os.makedirs("/tmp/huggingface/transformers", exist_ok=True)
+os.makedirs("/tmp/huggingface/datasets", exist_ok=True)
+os.makedirs("/tmp/huggingface/hub", exist_ok=True)
+
+# Mount static files for serving generated videos  
+
+
+def get_video_url(output_path: str) -> str:
+    """Convert local file path to accessible URL"""
+    try:
+        from pathlib import Path
+        filename = Path(output_path).name
+        
+        # For HuggingFace Spaces, construct the URL
+        base_url = "https://bravedims-ai-avatar-chat.hf.space"
+        video_url = f"{base_url}/outputs/{filename}"
+        logger.info(f"Generated video URL: {video_url}")
+        return video_url
+    except Exception as e:
+        logger.error(f"Error creating video URL: {e}")
+        return output_path  # Fallback to original path
 
 # Pydantic models for request/response
 class GenerateRequest(BaseModel):
     prompt: str
     text_to_speech: Optional[str] = None  # Text to convert to speech
-    elevenlabs_audio_url: Optional[HttpUrl] = None  # Direct audio URL
-    voice_id: Optional[str] = "21m00Tcm4TlvDq8ikWAM"  # Default ElevenLabs voice
+    audio_url: Optional[HttpUrl] = None  # Direct audio URL
+    voice_id: Optional[str] = "21m00Tcm4TlvDq8ikWAM"  # Voice profile ID
     image_url: Optional[HttpUrl] = None
     guidance_scale: float = 5.0
     audio_scale: float = 3.0
@@ -54,88 +82,216 @@ class GenerateResponse(BaseModel):
     output_path: str
     processing_time: float
     audio_generated: bool = False
+    tts_method: Optional[str] = None
 
-class ElevenLabsClient:
-    def __init__(self, api_key: str = None):
-        self.api_key = api_key or os.getenv("ELEVENLABS_API_KEY", "sk_c7a0b115cd48fc026226158c5ac87755b063c802ad892de6")
-        self.base_url = "https://api.elevenlabs.io/v1"
+# Try to import TTS clients, but make them optional
+try:
+    from advanced_tts_client import AdvancedTTSClient
+    ADVANCED_TTS_AVAILABLE = True
+    logger.info("SUCCESS: Advanced TTS client available")
+except ImportError as e:
+    ADVANCED_TTS_AVAILABLE = False
+    logger.warning(f"WARNING: Advanced TTS client not available: {e}")
+
+# Always import the robust fallback
+try:
+    from robust_tts_client import RobustTTSClient
+    ROBUST_TTS_AVAILABLE = True
+    logger.info("SUCCESS: Robust TTS client available")
+except ImportError as e:
+    ROBUST_TTS_AVAILABLE = False
+    logger.error(f"ERROR: Robust TTS client not available: {e}")
+
+class TTSManager:
+    """Manages multiple TTS clients with fallback chain"""
+    
+    def __init__(self):
+        # Initialize TTS clients based on availability
+        self.advanced_tts = None
+        self.robust_tts = None
+        self.clients_loaded = False
         
-    async def text_to_speech(self, text: str, voice_id: str = "21m00Tcm4TlvDq8ikWAM") -> str:
-        """Convert text to speech using ElevenLabs and return temporary file path"""
-        url = f"{self.base_url}/text-to-speech/{voice_id}"
+        if ADVANCED_TTS_AVAILABLE:
+            try:
+                self.advanced_tts = AdvancedTTSClient()
+                logger.info("SUCCESS: Advanced TTS client initialized")
+            except Exception as e:
+                logger.warning(f"WARNING: Advanced TTS client initialization failed: {e}")
         
-        headers = {
-            "Accept": "audio/mpeg",
-            "Content-Type": "application/json",
-            "xi-api-key": self.api_key
-        }
+        if ROBUST_TTS_AVAILABLE:
+            try:
+                self.robust_tts = RobustTTSClient()
+                logger.info("SUCCESS: Robust TTS client initialized")
+            except Exception as e:
+                logger.error(f"ERROR: Robust TTS client initialization failed: {e}")
+        
+        if not self.advanced_tts and not self.robust_tts:
+            logger.error("ERROR: No TTS clients available!")
+        
+    async def load_models(self):
+        """Load TTS models"""
+        try:
+            logger.info("Loading TTS models...")
+            
+            # Try to load advanced TTS first
+            if self.advanced_tts:
+                try:
+                    logger.info("[PROCESS] Loading advanced TTS models (this may take a few minutes)...")
+                    success = await self.advanced_tts.load_models()
+                    if success:
+                        logger.info("SUCCESS: Advanced TTS models loaded successfully")
+                    else:
+                        logger.warning("WARNING: Advanced TTS models failed to load")
+                except Exception as e:
+                    logger.warning(f"WARNING: Advanced TTS loading error: {e}")
+            
+            # Always ensure robust TTS is available
+            if self.robust_tts:
+                try:
+                    await self.robust_tts.load_model()
+                    logger.info("SUCCESS: Robust TTS fallback ready")
+                except Exception as e:
+                    logger.error(f"ERROR: Robust TTS loading failed: {e}")
+            
+            self.clients_loaded = True
+            return True
+            
+        except Exception as e:
+            logger.error(f"ERROR: TTS manager initialization failed: {e}")
+            return False
+    
+    async def text_to_speech(self, text: str, voice_id: Optional[str] = None) -> tuple[str, str]:
+        """
+        Convert text to speech with fallback chain
+        Returns: (audio_file_path, method_used)
+        """
+        if not self.clients_loaded:
+            logger.info("TTS models not loaded, loading now...")
+            await self.load_models()
+        
+        logger.info(f"Generating speech: {text[:50]}...")
+        logger.info(f"Voice ID: {voice_id}")
+        
+        # Try Advanced TTS first (Facebook VITS / SpeechT5)
+        if self.advanced_tts:
+            try:
+                audio_path = await self.advanced_tts.text_to_speech(text, voice_id)
+                return audio_path, "Facebook VITS/SpeechT5"
+            except Exception as advanced_error:
+                logger.warning(f"Advanced TTS failed: {advanced_error}")
         
-        data = {
-            "text": text,
-            "model_id": "eleven_monolingual_v1",
-            "voice_settings": {
-                "stability": 0.5,
-                "similarity_boost": 0.5
-            }
+        # Fall back to robust TTS
+        if self.robust_tts:
+            try:
+                logger.info("Falling back to robust TTS...")
+                audio_path = await self.robust_tts.text_to_speech(text, voice_id)
+                return audio_path, "Robust TTS (Fallback)"
+            except Exception as robust_error:
+                logger.error(f"Robust TTS also failed: {robust_error}")
+        
+        # If we get here, all methods failed
+        logger.error("All TTS methods failed!")
+        raise HTTPException(
+            status_code=500, 
+            detail="All TTS methods failed. Please check system configuration."
+        )
+    
+    async def get_available_voices(self):
+        """Get available voice configurations"""
+        try:
+            if self.advanced_tts and hasattr(self.advanced_tts, 'get_available_voices'):
+                return await self.advanced_tts.get_available_voices()
+        except:
+            pass
+        
+        # Return default voices if advanced TTS not available
+        return {
+            "21m00Tcm4TlvDq8ikWAM": "Female (Neutral)",
+            "pNInz6obpgDQGcFmaJgB": "Male (Professional)", 
+            "EXAVITQu4vr4xnSDxMaL": "Female (Sweet)",
+            "ErXwobaYiN019PkySvjV": "Male (Professional)",
+            "TxGEqnHWrfGW9XjX": "Male (Deep)",
+            "yoZ06aMxZJJ28mfd3POQ": "Unisex (Friendly)",
+            "AZnzlk1XvdvUeBnXmlld": "Female (Strong)"
+        }
+    
+    def get_tts_info(self):
+        """Get TTS system information"""
+        info = {
+            "clients_loaded": self.clients_loaded,
+            "advanced_tts_available": self.advanced_tts is not None,
+            "robust_tts_available": self.robust_tts is not None,
+            "primary_method": "Robust TTS"
         }
         
         try:
-            async with aiohttp.ClientSession() as session:
-                async with session.post(url, headers=headers, json=data) as response:
-                    if response.status != 200:
-                        error_text = await response.text()
-                        raise HTTPException(
-                            status_code=400, 
-                            detail=f"ElevenLabs API error: {response.status} - {error_text}"
-                        )
-                    
-                    audio_content = await response.read()
-                    
-                    # Save to temporary file
-                    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3')
-                    temp_file.write(audio_content)
-                    temp_file.close()
-                    
-                    logger.info(f"Generated speech audio: {temp_file.name}")
-                    return temp_file.name
-                    
-        except aiohttp.ClientError as e:
-            logger.error(f"Network error calling ElevenLabs: {e}")
-            raise HTTPException(status_code=400, detail=f"Network error calling ElevenLabs: {e}")
+            if self.advanced_tts and hasattr(self.advanced_tts, 'get_model_info'):
+                advanced_info = self.advanced_tts.get_model_info()
+                info.update({
+                    "advanced_tts_loaded": advanced_info.get("models_loaded", False),
+                    "transformers_available": advanced_info.get("transformers_available", False),
+                    "primary_method": "Facebook VITS/SpeechT5" if advanced_info.get("models_loaded") else "Robust TTS",
+                    "device": advanced_info.get("device", "cpu"),
+                    "vits_available": advanced_info.get("vits_available", False),
+                    "speecht5_available": advanced_info.get("speecht5_available", False)
+                })
         except Exception as e:
-            logger.error(f"Error generating speech: {e}")
-            raise HTTPException(status_code=500, detail=f"Error generating speech: {e}")
+            logger.debug(f"Could not get advanced TTS info: {e}")
+        
+        return info
+
+# Import the VIDEO-FOCUSED engine
+try:
+    from omniavatar_video_engine import video_engine
+    VIDEO_ENGINE_AVAILABLE = True
+    logger.info("SUCCESS: OmniAvatar Video Engine available")
+except ImportError as e:
+    VIDEO_ENGINE_AVAILABLE = False
+    logger.error(f"ERROR: OmniAvatar Video Engine not available: {e}")
 
 class OmniAvatarAPI:
     def __init__(self):
         self.model_loaded = False
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.elevenlabs_client = ElevenLabsClient()
+        self.tts_manager = TTSManager()
         logger.info(f"Using device: {self.device}")
-        logger.info(f"ElevenLabs API Key configured: {'Yes' if self.elevenlabs_client.api_key else 'No'}")
+        logger.info("Initialized with robust TTS system")
         
     def load_model(self):
-        """Load the OmniAvatar model"""
+        """Load the OmniAvatar model - now more flexible"""
         try:
-            # Check if models are downloaded
+            # Check if models are downloaded (but don't require them)
             model_paths = [
                 "./pretrained_models/Wan2.1-T2V-14B",
                 "./pretrained_models/OmniAvatar-14B", 
                 "./pretrained_models/wav2vec2-base-960h"
             ]
             
+            missing_models = []
             for path in model_paths:
                 if not os.path.exists(path):
-                    logger.error(f"Model path not found: {path}")
-                    return False
-                    
-            self.model_loaded = True
-            logger.info("Models loaded successfully")
-            return True
+                    missing_models.append(path)
             
+            if missing_models:
+                logger.warning("WARNING: Some OmniAvatar models not found:")
+                for model in missing_models:
+                    logger.warning(f"   - {model}")
+                logger.info("TIP: App will run in TTS-only mode (no video generation)")
+                logger.info("TIP: To enable full avatar generation, download the required models")
+                
+                # Set as loaded but in limited mode
+                self.model_loaded = False  # Video generation disabled
+                return True  # But app can still run
+            else:
+                self.model_loaded = True
+                logger.info("SUCCESS: All OmniAvatar models found - full functionality enabled")
+                return True
+                
         except Exception as e:
-            logger.error(f"Error loading model: {str(e)}")
-            return False
+            logger.error(f"Error checking models: {str(e)}")
+            logger.info("TIP: Continuing in TTS-only mode")
+            self.model_loaded = False
+            return True  # Continue running
     
     async def download_file(self, url: str, suffix: str = "") -> str:
         """Download file from URL and save to temporary location"""
@@ -165,12 +321,11 @@ class OmniAvatarAPI:
         """Validate if URL is likely an audio file"""
         try:
             parsed = urlparse(url)
-            # Check for common audio file extensions or ElevenLabs patterns
-            audio_extensions = ['.mp3', '.wav', '.m4a', '.ogg', '.aac']
+            # Check for common audio file extensions
+            audio_extensions = ['.mp3', '.wav', '.m4a', '.ogg', '.aac', '.flac']
             is_audio_ext = any(parsed.path.lower().endswith(ext) for ext in audio_extensions)
-            is_elevenlabs = 'elevenlabs' in parsed.netloc.lower()
             
-            return is_audio_ext or is_elevenlabs or 'audio' in url.lower()
+            return is_audio_ext or 'audio' in url.lower()
         except:
             return False
     
@@ -183,37 +338,142 @@ class OmniAvatarAPI:
         except:
             return False
     
-    async def generate_avatar(self, request: GenerateRequest) -> tuple[str, float, bool]:
-        """Generate avatar video from prompt and audio/text"""
+    async def generate_avatar(self, request: GenerateRequest) -> tuple[str, float, bool, str]:
+        """Generate avatar VIDEO - PRIMARY FUNCTIONALITY"""
+        import time
+        start_time = time.time()
+        audio_generated = False
+        method_used = "Unknown"
+        
+        logger.info("[VIDEO] STARTING AVATAR VIDEO GENERATION")
+        logger.info(f"[INFO] Prompt: {request.prompt}")
+        
+        if VIDEO_ENGINE_AVAILABLE:
+            try:
+                # PRIORITIZE VIDEO GENERATION
+                logger.info("[TARGET] Using OmniAvatar Video Engine for FULL video generation")
+                
+                # Handle audio source
+                audio_path = None
+                if request.text_to_speech:
+                    logger.info("[MIC] Generating audio from text...")
+                    audio_path, method_used = await self.tts_manager.text_to_speech(
+                        request.text_to_speech, 
+                        request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                    )
+                    audio_generated = True
+                elif request.audio_url:
+                    logger.info("📥 Downloading audio from URL...")
+                    audio_path = await self.download_file(str(request.audio_url), ".mp3")
+                    method_used = "External Audio"
+                else:
+                    raise HTTPException(status_code=400, detail="Either text_to_speech or audio_url required for video generation")
+                
+                # Handle image if provided
+                image_path = None
+                if request.image_url:
+                    logger.info("[IMAGE] Downloading reference image...")
+                    parsed = urlparse(str(request.image_url))
+                    ext = os.path.splitext(parsed.path)[1] or ".jpg"
+                    image_path = await self.download_file(str(request.image_url), ext)
+                
+                # GENERATE VIDEO using OmniAvatar engine
+                logger.info("[VIDEO] Generating avatar video with adaptive body animation...")
+                video_path, generation_time = video_engine.generate_avatar_video(
+                    prompt=request.prompt,
+                    audio_path=audio_path,
+                    image_path=image_path,
+                    guidance_scale=request.guidance_scale,
+                    audio_scale=request.audio_scale,
+                    num_steps=request.num_steps
+                )
+                
+                processing_time = time.time() - start_time
+                logger.info(f"SUCCESS: VIDEO GENERATED successfully in {processing_time:.1f}s")
+                
+                # Cleanup temporary files
+                if audio_path and os.path.exists(audio_path):
+                    os.unlink(audio_path)
+                if image_path and os.path.exists(image_path):
+                    os.unlink(image_path)
+                
+                return video_path, processing_time, audio_generated, f"OmniAvatar Video Generation ({method_used})"
+                
+            except Exception as e:
+                logger.error(f"ERROR: Video generation failed: {e}")
+                # For a VIDEO generation app, we should NOT fall back to audio-only
+                # Instead, provide clear guidance
+                if "models" in str(e).lower():
+                    raise HTTPException(
+                        status_code=503,
+                        detail=f"Video generation requires OmniAvatar models (~30GB). Please run model download script. Error: {str(e)}"
+                    )
+                else:
+                    raise HTTPException(status_code=500, detail=f"Video generation failed: {str(e)}")
+        
+        # If video engine not available, this is a critical error for a VIDEO app
+        raise HTTPException(
+            status_code=503, 
+            detail="Video generation engine not available. This application requires OmniAvatar models for video generation."
+        )
+
+    async def generate_avatar_BACKUP(self, request: GenerateRequest) -> tuple[str, float, bool, str]:
+        """OLD TTS-ONLY METHOD - kept as backup reference
+        """Generate avatar video from prompt and audio/text - now handles missing models"""
         import time
         start_time = time.time()
         audio_generated = False
+        tts_method = None
         
         try:
+            # Check if video generation is available
+            if not self.model_loaded:
+                logger.info("🎙️ Running in TTS-only mode (OmniAvatar models not available)")
+                
+                # Only generate audio, no video
+                if request.text_to_speech:
+                    logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
+                    audio_path, tts_method = await self.tts_manager.text_to_speech(
+                        request.text_to_speech, 
+                        request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                    )
+                    
+                    # Return the audio file as the "output"
+                    processing_time = time.time() - start_time
+                    logger.info(f"SUCCESS: TTS completed in {processing_time:.1f}s using {tts_method}")
+                    return audio_path, processing_time, True, f"{tts_method} (TTS-only mode)"
+                else:
+                    raise HTTPException(
+                        status_code=503,
+                        detail="Video generation unavailable. OmniAvatar models not found. Only TTS from text is supported."
+                    )
+            
+            # Original video generation logic (when models are available)
             # Determine audio source
             audio_path = None
             
             if request.text_to_speech:
-                # Generate speech from text using ElevenLabs
+                # Generate speech from text using TTS manager
                 logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
-                audio_path = await self.elevenlabs_client.text_to_speech(
+                audio_path, tts_method = await self.tts_manager.text_to_speech(
                     request.text_to_speech, 
                     request.voice_id or "21m00Tcm4TlvDq8ikWAM"
                 )
                 audio_generated = True
                 
-            elif request.elevenlabs_audio_url:
+            elif request.audio_url:
                 # Download audio from provided URL
-                logger.info(f"Downloading audio from URL: {request.elevenlabs_audio_url}")
-                if not self.validate_audio_url(str(request.elevenlabs_audio_url)):
-                    logger.warning(f"Audio URL may not be valid: {request.elevenlabs_audio_url}")
+                logger.info(f"Downloading audio from URL: {request.audio_url}")
+                if not self.validate_audio_url(str(request.audio_url)):
+                    logger.warning(f"Audio URL may not be valid: {request.audio_url}")
                 
-                audio_path = await self.download_file(str(request.elevenlabs_audio_url), ".mp3")
+                audio_path = await self.download_file(str(request.audio_url), ".mp3")
+                tts_method = "External Audio URL"
             
             else:
                 raise HTTPException(
                     status_code=400, 
-                    detail="Either text_to_speech or elevenlabs_audio_url must be provided"
+                    detail="Either text_to_speech or audio_url must be provided"
                 )
             
             # Download image if provided
@@ -276,7 +536,7 @@ class OmniAvatarAPI:
                     video_files.sort(key=lambda x: os.path.getmtime(os.path.join(output_dir, x)), reverse=True)
                     output_path = os.path.join(output_dir, video_files[0])
                     processing_time = time.time() - start_time
-                    return output_path, processing_time, audio_generated
+                    return output_path, processing_time, audio_generated, tts_method
             
             raise Exception("No output video generated")
             
@@ -298,50 +558,99 @@ class OmniAvatarAPI:
 # Initialize API
 omni_api = OmniAvatarAPI()
 
-@app.on_event("startup")
-async def startup_event():
-    """Load model on startup"""
+# Use FastAPI lifespan instead of deprecated on_event
+from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Startup
     success = omni_api.load_model()
     if not success:
-        logger.warning("Model loading failed on startup")
+        logger.warning("WARNING: OmniAvatar model loading failed - running in limited mode")
+    
+    # Load TTS models
+    try:
+        await omni_api.tts_manager.load_models()
+        logger.info("SUCCESS: TTS models initialization completed")
+    except Exception as e:
+        logger.error(f"ERROR: TTS initialization failed: {e}")
+    
+    yield
+    
+    # Shutdown (if needed)
+    logger.info("Application shutting down...")
+
+# Create FastAPI app WITH lifespan parameter
+app = FastAPI(
+    title="OmniAvatar-14B API with Advanced TTS", 
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static files for serving generated videos
+app.mount("/outputs", StaticFiles(directory="outputs"), name="outputs")
 
 @app.get("/health")
 async def health_check():
     """Health check endpoint"""
+    tts_info = omni_api.tts_manager.get_tts_info()
+    
     return {
         "status": "healthy",
         "model_loaded": omni_api.model_loaded,
+        "video_generation_available": omni_api.model_loaded,
+        "tts_only_mode": not omni_api.model_loaded,
         "device": omni_api.device,
-        "supports_elevenlabs": True,
-        "supports_image_urls": True,
         "supports_text_to_speech": True,
-        "elevenlabs_api_configured": bool(omni_api.elevenlabs_client.api_key)
+        "supports_image_urls": omni_api.model_loaded,
+        "supports_audio_urls": omni_api.model_loaded,
+        "tts_system": "Advanced TTS with Robust Fallback",
+        "advanced_tts_available": ADVANCED_TTS_AVAILABLE,
+        "robust_tts_available": ROBUST_TTS_AVAILABLE,
+        **tts_info
     }
 
+@app.get("/voices")
+async def get_voices():
+    """Get available voice configurations"""
+    try:
+        voices = await omni_api.tts_manager.get_available_voices()
+        return {"voices": voices}
+    except Exception as e:
+        logger.error(f"Error getting voices: {e}")
+        return {"error": str(e)}
+
 @app.post("/generate", response_model=GenerateResponse)
 async def generate_avatar(request: GenerateRequest):
     """Generate avatar video from prompt, text/audio, and optional image URL"""
     
-    if not omni_api.model_loaded:
-        raise HTTPException(status_code=503, detail="Model not loaded")
-    
     logger.info(f"Generating avatar with prompt: {request.prompt}")
     if request.text_to_speech:
         logger.info(f"Text to speech: {request.text_to_speech[:100]}...")
         logger.info(f"Voice ID: {request.voice_id}")
-    if request.elevenlabs_audio_url:
-        logger.info(f"Audio URL: {request.elevenlabs_audio_url}")
+    if request.audio_url:
+        logger.info(f"Audio URL: {request.audio_url}")
     if request.image_url:
         logger.info(f"Image URL: {request.image_url}")
     
     try:
-        output_path, processing_time, audio_generated = await omni_api.generate_avatar(request)
+        output_path, processing_time, audio_generated, tts_method = await omni_api.generate_avatar(request)
         
         return GenerateResponse(
-            message="Avatar generation completed successfully",
-            output_path=output_path,
+            message="Generation completed successfully" + (" (TTS-only mode)" if not omni_api.model_loaded else ""),
+            output_path=get_video_url(output_path) if omni_api.model_loaded else output_path,
             processing_time=processing_time,
-            audio_generated=audio_generated
+            audio_generated=audio_generated,
+            tts_method=tts_method
         )
         
     except HTTPException:
@@ -350,12 +659,9 @@ async def generate_avatar(request: GenerateRequest):
         logger.error(f"Unexpected error: {e}")
         raise HTTPException(status_code=500, detail=f"Unexpected error: {e}")
 
-# Enhanced Gradio interface with text-to-speech option
+# Enhanced Gradio interface
 def gradio_generate(prompt, text_to_speech, audio_url, image_url, voice_id, guidance_scale, audio_scale, num_steps):
-    """Gradio interface wrapper with text-to-speech support"""
-    if not omni_api.model_loaded:
-        return "Error: Model not loaded"
-    
+    """Gradio interface wrapper with robust TTS support"""
     try:
         # Create request object
         request_data = {
@@ -370,28 +676,46 @@ def gradio_generate(prompt, text_to_speech, audio_url, image_url, voice_id, guid
             request_data["text_to_speech"] = text_to_speech
             request_data["voice_id"] = voice_id or "21m00Tcm4TlvDq8ikWAM"
         elif audio_url and audio_url.strip():
-            request_data["elevenlabs_audio_url"] = audio_url
+            if omni_api.model_loaded:
+                request_data["audio_url"] = audio_url
+            else:
+                return "Error: Audio URL input requires full OmniAvatar models. Please use text-to-speech instead."
         else:
             return "Error: Please provide either text to speech or audio URL"
         
         if image_url and image_url.strip():
-            request_data["image_url"] = image_url
+            if omni_api.model_loaded:
+                request_data["image_url"] = image_url
+            else:
+                return "Error: Image URL input requires full OmniAvatar models for video generation."
         
         request = GenerateRequest(**request_data)
         
         # Run async function in sync context
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
-        output_path, processing_time, audio_generated = loop.run_until_complete(omni_api.generate_avatar(request))
+        output_path, processing_time, audio_generated, tts_method = loop.run_until_complete(omni_api.generate_avatar(request))
         loop.close()
         
-        return output_path
+        success_message = f"SUCCESS: Generation completed in {processing_time:.1f}s using {tts_method}"
+        print(success_message)
+        
+        if omni_api.model_loaded:
+            return output_path
+        else:
+            return f"🎙️ TTS Audio generated successfully using {tts_method}\nFile: {output_path}\n\nWARNING: Video generation unavailable (OmniAvatar models not found)"
         
     except Exception as e:
         logger.error(f"Gradio generation error: {e}")
         return f"Error: {str(e)}"
 
-# Updated Gradio interface with text-to-speech support
+# Create Gradio interface
+mode_info = " (TTS-Only Mode)" if not omni_api.model_loaded else ""
+description_extra = """
+WARNING: Running in TTS-Only Mode - OmniAvatar models not found. Only text-to-speech generation is available.
+To enable full video generation, the required model files need to be downloaded.
+""" if not omni_api.model_loaded else ""
+
 iface = gr.Interface(
     fn=gradio_generate,
     inputs=[
@@ -402,60 +726,71 @@ iface = gr.Interface(
         ),
         gr.Textbox(
             label="Text to Speech", 
-            placeholder="Enter text to convert to speech using ElevenLabs",
+            placeholder="Enter text to convert to speech",
             lines=3,
-            info="This will be converted to speech automatically"
+            info="Will use best available TTS system (Advanced or Fallback)"
         ),
         gr.Textbox(
             label="OR Audio URL", 
-            placeholder="https://api.elevenlabs.io/v1/text-to-speech/...",
-            info="Direct URL to audio file (alternative to text-to-speech)"
+            placeholder="https://example.com/audio.mp3",
+            info="Direct URL to audio file (requires full models)" if not omni_api.model_loaded else "Direct URL to audio file"
         ),
         gr.Textbox(
             label="Image URL (Optional)", 
             placeholder="https://example.com/image.jpg",
-            info="Direct URL to reference image (JPG, PNG, etc.)"
+            info="Direct URL to reference image (requires full models)" if not omni_api.model_loaded else "Direct URL to reference image"
         ),
         gr.Dropdown(
-            choices=["21m00Tcm4TlvDq8ikWAM", "pNInz6obpgDQGcFmaJgB", "EXAVITQu4vr4xnSDxMaL"],
+            choices=[
+                "21m00Tcm4TlvDq8ikWAM", 
+                "pNInz6obpgDQGcFmaJgB", 
+                "EXAVITQu4vr4xnSDxMaL",
+                "ErXwobaYiN019PkySvjV",
+                "TxGEqnHWrfGW9XjX",
+                "yoZ06aMxZJJ28mfd3POQ",
+                "AZnzlk1XvdvUeBnXmlld"
+            ],
             value="21m00Tcm4TlvDq8ikWAM",
-            label="ElevenLabs Voice ID",
-            info="Choose voice for text-to-speech"
+            label="Voice Profile",
+            info="Choose voice characteristics for TTS generation"
         ),
         gr.Slider(minimum=1, maximum=10, value=5.0, label="Guidance Scale", info="4-6 recommended"),
         gr.Slider(minimum=1, maximum=10, value=3.0, label="Audio Scale", info="Higher values = better lip-sync"),
         gr.Slider(minimum=10, maximum=100, value=30, step=1, label="Number of Steps", info="20-50 recommended")
     ],
-    outputs=gr.Video(label="Generated Avatar Video"),
-    title="🎭 OmniAvatar-14B with ElevenLabs TTS",
-    description="""
-    Generate avatar videos with lip-sync from text prompts and speech.
+    outputs=gr.Video(label="Generated Avatar Video") if omni_api.model_loaded else gr.Textbox(label="TTS Output"),
+    title="[VIDEO] OmniAvatar-14B - Avatar Video Generation with Adaptive Body Animation",
+    description=f"""
+    Generate avatar videos with lip-sync from text prompts and speech using robust TTS system.
+    
+    {description_extra}
+    
+    **Robust TTS Architecture**
+    - **Primary**: Advanced TTS (Facebook VITS & SpeechT5) if available
+    - **Fallback**: Robust tone generation for 100% reliability
+    - **Automatic**: Seamless switching between methods
     
     **Features:**
-    - ✅ **Text-to-Speech**: Enter text to generate speech automatically
-    - ✅ **ElevenLabs Integration**: High-quality voice synthesis  
-    - ✅ **Audio URL Support**: Use pre-generated audio files
-    - ✅ **Image URL Support**: Reference images for character appearance
-    - ✅ **Customizable Parameters**: Fine-tune generation quality
+    - **Guaranteed Generation**: Always produces audio output
+    - **No Dependencies**: Works even without advanced models
+    - **High Availability**: Multiple fallback layers
+    - **Voice Profiles**: Multiple voice characteristics
+    - **Audio URL Support**: Use external audio files {"(full models required)" if not omni_api.model_loaded else ""}
+    - **Image URL Support**: Reference images for characters {"(full models required)" if not omni_api.model_loaded else ""}
     
     **Usage:**
     1. Enter a character description in the prompt
-    2. **Either** enter text for speech generation **OR** provide an audio URL
-    3. Optionally add a reference image URL
-    4. Choose voice and adjust parameters
-    5. Generate your avatar video!
-    
-    **Tips:**
-    - Use guidance scale 4-6 for best prompt following
-    - Increase audio scale for better lip-sync
-    - Clear, descriptive prompts work best
+    2. **Enter text for speech generation** (recommended in current mode)
+    3. {"Optionally add reference image/audio URLs (requires full models)" if not omni_api.model_loaded else "Optionally add reference image URL and choose audio source"}
+    4. Choose voice profile and adjust parameters
+    5. Generate your {"audio" if not omni_api.model_loaded else "avatar video"}!
     """,
     examples=[
         [
             "A professional teacher explaining a mathematical concept with clear gestures",
-            "Hello students! Today we're going to learn about calculus and how derivatives work in real life.",
+            "Hello students! Today we're going to learn about calculus and derivatives.",
+            "",
             "",
-            "https://example.com/teacher.jpg",
             "21m00Tcm4TlvDq8ikWAM",
             5.0,
             3.5,
@@ -463,7 +798,7 @@ iface = gr.Interface(
         ],
         [
             "A friendly presenter speaking confidently to an audience",
-            "Welcome everyone to our presentation on artificial intelligence and its applications!",
+            "Welcome everyone to our presentation on artificial intelligence!",
             "",
             "",
             "pNInz6obpgDQGcFmaJgB", 
@@ -471,7 +806,9 @@ iface = gr.Interface(
             4.0,
             35
         ]
-    ]
+    ],
+    allow_flagging="never",
+    flagging_dir="/tmp/gradio_flagged"
 )
 
 # Mount Gradio app
@@ -480,3 +817,11 @@ app = gr.mount_gradio_app(app, iface, path="/gradio")
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)
+
+
+
+
+
+
+
+

app_fixed.py

@@ -0,0 +1,828 @@
+import os
+import torch
+import tempfile
+import gradio as gr
+from fastapi import FastAPI, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, HttpUrl
+import subprocess
+import json
+from pathlib import Path
+import logging
+import requests
+from urllib.parse import urlparse
+from PIL import Image
+import io
+from typing import Optional
+import aiohttp
+import asyncio
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Set environment variables for matplotlib, gradio, and huggingface cache
+os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
+os.environ['GRADIO_ALLOW_FLAGGING'] = 'never'
+os.environ['HF_HOME'] = '/tmp/huggingface'
+# Use HF_HOME instead of deprecated TRANSFORMERS_CACHE
+os.environ['HF_DATASETS_CACHE'] = '/tmp/huggingface/datasets'
+os.environ['HUGGINGFACE_HUB_CACHE'] = '/tmp/huggingface/hub'
+
+# FastAPI app will be created after lifespan is defined
+
+
+
+# Create directories with proper permissions
+os.makedirs("outputs", exist_ok=True)
+os.makedirs("/tmp/matplotlib", exist_ok=True)
+os.makedirs("/tmp/huggingface", exist_ok=True)
+os.makedirs("/tmp/huggingface/transformers", exist_ok=True)
+os.makedirs("/tmp/huggingface/datasets", exist_ok=True)
+os.makedirs("/tmp/huggingface/hub", exist_ok=True)
+
+# Mount static files for serving generated videos  
+
+
+def get_video_url(output_path: str) -> str:
+    """Convert local file path to accessible URL"""
+    try:
+        from pathlib import Path
+        filename = Path(output_path).name
+        
+        # For HuggingFace Spaces, construct the URL
+        base_url = "https://bravedims-ai-avatar-chat.hf.space"
+        video_url = f"{base_url}/outputs/{filename}"
+        logger.info(f"Generated video URL: {video_url}")
+        return video_url
+    except Exception as e:
+        logger.error(f"Error creating video URL: {e}")
+        return output_path  # Fallback to original path
+
+# Pydantic models for request/response
+class GenerateRequest(BaseModel):
+    prompt: str
+    text_to_speech: Optional[str] = None  # Text to convert to speech
+    audio_url: Optional[HttpUrl] = None  # Direct audio URL
+    voice_id: Optional[str] = "21m00Tcm4TlvDq8ikWAM"  # Voice profile ID
+    image_url: Optional[HttpUrl] = None
+    guidance_scale: float = 5.0
+    audio_scale: float = 3.0
+    num_steps: int = 30
+    sp_size: int = 1
+    tea_cache_l1_thresh: Optional[float] = None
+
+class GenerateResponse(BaseModel):
+    message: str
+    output_path: str
+    processing_time: float
+    audio_generated: bool = False
+    tts_method: Optional[str] = None
+
+# Try to import TTS clients, but make them optional
+try:
+    from advanced_tts_client import AdvancedTTSClient
+    ADVANCED_TTS_AVAILABLE = True
+    logger.info("SUCCESS: Advanced TTS client available")
+except ImportError as e:
+    ADVANCED_TTS_AVAILABLE = False
+    logger.warning(f"WARNING: Advanced TTS client not available: {e}")
+
+# Always import the robust fallback
+try:
+    from robust_tts_client import RobustTTSClient
+    ROBUST_TTS_AVAILABLE = True
+    logger.info("SUCCESS: Robust TTS client available")
+except ImportError as e:
+    ROBUST_TTS_AVAILABLE = False
+    logger.error(f"ERROR: Robust TTS client not available: {e}")
+
+class TTSManager:
+    """Manages multiple TTS clients with fallback chain"""
+    
+    def __init__(self):
+        # Initialize TTS clients based on availability
+        self.advanced_tts = None
+        self.robust_tts = None
+        self.clients_loaded = False
+        
+        if ADVANCED_TTS_AVAILABLE:
+            try:
+                self.advanced_tts = AdvancedTTSClient()
+                logger.info("SUCCESS: Advanced TTS client initialized")
+            except Exception as e:
+                logger.warning(f"WARNING: Advanced TTS client initialization failed: {e}")
+        
+        if ROBUST_TTS_AVAILABLE:
+            try:
+                self.robust_tts = RobustTTSClient()
+                logger.info("SUCCESS: Robust TTS client initialized")
+            except Exception as e:
+                logger.error(f"ERROR: Robust TTS client initialization failed: {e}")
+        
+        if not self.advanced_tts and not self.robust_tts:
+            logger.error("ERROR: No TTS clients available!")
+        
+    async def load_models(self):
+        """Load TTS models"""
+        try:
+            logger.info("Loading TTS models...")
+            
+            # Try to load advanced TTS first
+            if self.advanced_tts:
+                try:
+                    logger.info("[PROCESS] Loading advanced TTS models (this may take a few minutes)...")
+                    success = await self.advanced_tts.load_models()
+                    if success:
+                        logger.info("SUCCESS: Advanced TTS models loaded successfully")
+                    else:
+                        logger.warning("WARNING: Advanced TTS models failed to load")
+                except Exception as e:
+                    logger.warning(f"WARNING: Advanced TTS loading error: {e}")
+            
+            # Always ensure robust TTS is available
+            if self.robust_tts:
+                try:
+                    await self.robust_tts.load_model()
+                    logger.info("SUCCESS: Robust TTS fallback ready")
+                except Exception as e:
+                    logger.error(f"ERROR: Robust TTS loading failed: {e}")
+            
+            self.clients_loaded = True
+            return True
+            
+        except Exception as e:
+            logger.error(f"ERROR: TTS manager initialization failed: {e}")
+            return False
+    
+    async def text_to_speech(self, text: str, voice_id: Optional[str] = None) -> tuple[str, str]:
+        """
+        Convert text to speech with fallback chain
+        Returns: (audio_file_path, method_used)
+        """
+        if not self.clients_loaded:
+            logger.info("TTS models not loaded, loading now...")
+            await self.load_models()
+        
+        logger.info(f"Generating speech: {text[:50]}...")
+        logger.info(f"Voice ID: {voice_id}")
+        
+        # Try Advanced TTS first (Facebook VITS / SpeechT5)
+        if self.advanced_tts:
+            try:
+                audio_path = await self.advanced_tts.text_to_speech(text, voice_id)
+                return audio_path, "Facebook VITS/SpeechT5"
+            except Exception as advanced_error:
+                logger.warning(f"Advanced TTS failed: {advanced_error}")
+        
+        # Fall back to robust TTS
+        if self.robust_tts:
+            try:
+                logger.info("Falling back to robust TTS...")
+                audio_path = await self.robust_tts.text_to_speech(text, voice_id)
+                return audio_path, "Robust TTS (Fallback)"
+            except Exception as robust_error:
+                logger.error(f"Robust TTS also failed: {robust_error}")
+        
+        # If we get here, all methods failed
+        logger.error("All TTS methods failed!")
+        raise HTTPException(
+            status_code=500, 
+            detail="All TTS methods failed. Please check system configuration."
+        )
+    
+    async def get_available_voices(self):
+        """Get available voice configurations"""
+        try:
+            if self.advanced_tts and hasattr(self.advanced_tts, 'get_available_voices'):
+                return await self.advanced_tts.get_available_voices()
+        except:
+            pass
+        
+        # Return default voices if advanced TTS not available
+        return {
+            "21m00Tcm4TlvDq8ikWAM": "Female (Neutral)",
+            "pNInz6obpgDQGcFmaJgB": "Male (Professional)", 
+            "EXAVITQu4vr4xnSDxMaL": "Female (Sweet)",
+            "ErXwobaYiN019PkySvjV": "Male (Professional)",
+            "TxGEqnHWrfGW9XjX": "Male (Deep)",
+            "yoZ06aMxZJJ28mfd3POQ": "Unisex (Friendly)",
+            "AZnzlk1XvdvUeBnXmlld": "Female (Strong)"
+        }
+    
+    def get_tts_info(self):
+        """Get TTS system information"""
+        info = {
+            "clients_loaded": self.clients_loaded,
+            "advanced_tts_available": self.advanced_tts is not None,
+            "robust_tts_available": self.robust_tts is not None,
+            "primary_method": "Robust TTS"
+        }
+        
+        try:
+            if self.advanced_tts and hasattr(self.advanced_tts, 'get_model_info'):
+                advanced_info = self.advanced_tts.get_model_info()
+                info.update({
+                    "advanced_tts_loaded": advanced_info.get("models_loaded", False),
+                    "transformers_available": advanced_info.get("transformers_available", False),
+                    "primary_method": "Facebook VITS/SpeechT5" if advanced_info.get("models_loaded") else "Robust TTS",
+                    "device": advanced_info.get("device", "cpu"),
+                    "vits_available": advanced_info.get("vits_available", False),
+                    "speecht5_available": advanced_info.get("speecht5_available", False)
+                })
+        except Exception as e:
+            logger.debug(f"Could not get advanced TTS info: {e}")
+        
+        return info
+
+# Import the VIDEO-FOCUSED engine
+try:
+    from omniavatar_video_engine import video_engine
+    VIDEO_ENGINE_AVAILABLE = True
+    logger.info("SUCCESS: OmniAvatar Video Engine available")
+except ImportError as e:
+    VIDEO_ENGINE_AVAILABLE = False
+    logger.error(f"ERROR: OmniAvatar Video Engine not available: {e}")
+
+class OmniAvatarAPI:
+    def __init__(self):
+        self.model_loaded = False
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.tts_manager = TTSManager()
+        logger.info(f"Using device: {self.device}")
+        logger.info("Initialized with robust TTS system")
+        
+    def load_model(self):
+        """Load the OmniAvatar model - now more flexible"""
+        try:
+            # Check if models are downloaded (but don't require them)
+            model_paths = [
+                "./pretrained_models/Wan2.1-T2V-14B",
+                "./pretrained_models/OmniAvatar-14B", 
+                "./pretrained_models/wav2vec2-base-960h"
+            ]
+            
+            missing_models = []
+            for path in model_paths:
+                if not os.path.exists(path):
+                    missing_models.append(path)
+            
+            if missing_models:
+                logger.warning("WARNING: Some OmniAvatar models not found:")
+                for model in missing_models:
+                    logger.warning(f"   - {model}")
+                logger.info("TIP: App will run in TTS-only mode (no video generation)")
+                logger.info("TIP: To enable full avatar generation, download the required models")
+                
+                # Set as loaded but in limited mode
+                self.model_loaded = False  # Video generation disabled
+                return True  # But app can still run
+            else:
+                self.model_loaded = True
+                logger.info("SUCCESS: All OmniAvatar models found - full functionality enabled")
+                return True
+                
+        except Exception as e:
+            logger.error(f"Error checking models: {str(e)}")
+            logger.info("TIP: Continuing in TTS-only mode")
+            self.model_loaded = False
+            return True  # Continue running
+    
+    async def download_file(self, url: str, suffix: str = "") -> str:
+        """Download file from URL and save to temporary location"""
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(str(url)) as response:
+                    if response.status != 200:
+                        raise HTTPException(status_code=400, detail=f"Failed to download file from URL: {url}")
+                    
+                    content = await response.read()
+                    
+                    # Create temporary file
+                    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+                    temp_file.write(content)
+                    temp_file.close()
+                    
+                    return temp_file.name
+                    
+        except aiohttp.ClientError as e:
+            logger.error(f"Network error downloading {url}: {e}")
+            raise HTTPException(status_code=400, detail=f"Network error downloading file: {e}")
+        except Exception as e:
+            logger.error(f"Error downloading file from {url}: {e}")
+            raise HTTPException(status_code=500, detail=f"Error downloading file: {e}")
+    
+    def validate_audio_url(self, url: str) -> bool:
+        """Validate if URL is likely an audio file"""
+        try:
+            parsed = urlparse(url)
+            # Check for common audio file extensions
+            audio_extensions = ['.mp3', '.wav', '.m4a', '.ogg', '.aac', '.flac']
+            is_audio_ext = any(parsed.path.lower().endswith(ext) for ext in audio_extensions)
+            
+            return is_audio_ext or 'audio' in url.lower()
+        except:
+            return False
+    
+    def validate_image_url(self, url: str) -> bool:
+        """Validate if URL is likely an image file"""
+        try:
+            parsed = urlparse(url)
+            image_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.bmp', '.gif']
+            return any(parsed.path.lower().endswith(ext) for ext in image_extensions)
+        except:
+            return False
+    
+    async def generate_avatar(self, request: GenerateRequest) -> tuple[str, float, bool, str]:
+        """Generate avatar VIDEO - PRIMARY FUNCTIONALITY"""
+        import time
+        start_time = time.time()
+        audio_generated = False
+        method_used = "Unknown"
+        
+        logger.info("[VIDEO] STARTING AVATAR VIDEO GENERATION")
+        logger.info(f"[INFO] Prompt: {request.prompt}")
+        
+        if VIDEO_ENGINE_AVAILABLE:
+            try:
+                # PRIORITIZE VIDEO GENERATION
+                logger.info("[TARGET] Using OmniAvatar Video Engine for FULL video generation")
+                
+                # Handle audio source
+                audio_path = None
+                if request.text_to_speech:
+                    logger.info("[MIC] Generating audio from text...")
+                    audio_path, method_used = await self.tts_manager.text_to_speech(
+                        request.text_to_speech, 
+                        request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                    )
+                    audio_generated = True
+                elif request.audio_url:
+                    logger.info("?? Downloading audio from URL...")
+                    audio_path = await self.download_file(str(request.audio_url), ".mp3")
+                    method_used = "External Audio"
+                else:
+                    raise HTTPException(status_code=400, detail="Either text_to_speech or audio_url required for video generation")
+                
+                # Handle image if provided
+                image_path = None
+                if request.image_url:
+                    logger.info("[IMAGE] Downloading reference image...")
+                    parsed = urlparse(str(request.image_url))
+                    ext = os.path.splitext(parsed.path)[1] or ".jpg"
+                    image_path = await self.download_file(str(request.image_url), ext)
+                
+                # GENERATE VIDEO using OmniAvatar engine
+                logger.info("[VIDEO] Generating avatar video with adaptive body animation...")
+                video_path, generation_time = video_engine.generate_avatar_video(
+                    prompt=request.prompt,
+                    audio_path=audio_path,
+                    image_path=image_path,
+                    guidance_scale=request.guidance_scale,
+                    audio_scale=request.audio_scale,
+                    num_steps=request.num_steps
+                )
+                
+                processing_time = time.time() - start_time
+                logger.info(f"SUCCESS: VIDEO GENERATED successfully in {processing_time:.1f}s")
+                
+                # Cleanup temporary files
+                if audio_path and os.path.exists(audio_path):
+                    os.unlink(audio_path)
+                if image_path and os.path.exists(image_path):
+                    os.unlink(image_path)
+                
+                return video_path, processing_time, audio_generated, f"OmniAvatar Video Generation ({method_used})"
+                
+            except Exception as e:
+                logger.error(f"ERROR: Video generation failed: {e}")
+                # For a VIDEO generation app, we should NOT fall back to audio-only
+                # Instead, provide clear guidance
+                if "models" in str(e).lower():
+                    raise HTTPException(
+                        status_code=503,
+                        detail=f"Video generation requires OmniAvatar models (~30GB). Please run model download script. Error: {str(e)}"
+                    )
+                else:
+                    raise HTTPException(status_code=500, detail=f"Video generation failed: {str(e)}")
+        
+        # If video engine not available, this is a critical error for a VIDEO app
+        raise HTTPException(
+            status_code=503, 
+            detail="Video generation engine not available. This application requires OmniAvatar models for video generation."
+        )
+
+    async def generate_avatar_BACKUP(self, request: GenerateRequest) -> tuple[str, float, bool, str]:
+        """OLD TTS-ONLY METHOD - kept as backup reference
+        """Generate avatar video from prompt and audio/text - now handles missing models"""
+        import time
+        start_time = time.time()
+        audio_generated = False
+        tts_method = None
+        
+        try:
+            # Check if video generation is available
+            if not self.model_loaded:
+                logger.info("??? Running in TTS-only mode (OmniAvatar models not available)")
+                
+                # Only generate audio, no video
+                if request.text_to_speech:
+                    logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
+                    audio_path, tts_method = await self.tts_manager.text_to_speech(
+                        request.text_to_speech, 
+                        request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                    )
+                    
+                    # Return the audio file as the "output"
+                    processing_time = time.time() - start_time
+                    logger.info(f"SUCCESS: TTS completed in {processing_time:.1f}s using {tts_method}")
+                    return audio_path, processing_time, True, f"{tts_method} (TTS-only mode)"
+                else:
+                    raise HTTPException(
+                        status_code=503,
+                        detail="Video generation unavailable. OmniAvatar models not found. Only TTS from text is supported."
+                    )
+            
+            # Original video generation logic (when models are available)
+            # Determine audio source
+            audio_path = None
+            
+            if request.text_to_speech:
+                # Generate speech from text using TTS manager
+                logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
+                audio_path, tts_method = await self.tts_manager.text_to_speech(
+                    request.text_to_speech, 
+                    request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                )
+                audio_generated = True
+                
+            elif request.audio_url:
+                # Download audio from provided URL
+                logger.info(f"Downloading audio from URL: {request.audio_url}")
+                if not self.validate_audio_url(str(request.audio_url)):
+                    logger.warning(f"Audio URL may not be valid: {request.audio_url}")
+                
+                audio_path = await self.download_file(str(request.audio_url), ".mp3")
+                tts_method = "External Audio URL"
+            
+            else:
+                raise HTTPException(
+                    status_code=400, 
+                    detail="Either text_to_speech or audio_url must be provided"
+                )
+            
+            # Download image if provided
+            image_path = None
+            if request.image_url:
+                logger.info(f"Downloading image from URL: {request.image_url}")
+                if not self.validate_image_url(str(request.image_url)):
+                    logger.warning(f"Image URL may not be valid: {request.image_url}")
+                
+                # Determine image extension from URL or default to .jpg
+                parsed = urlparse(str(request.image_url))
+                ext = os.path.splitext(parsed.path)[1] or ".jpg"
+                image_path = await self.download_file(str(request.image_url), ext)
+            
+            # Create temporary input file for inference
+            with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
+                if image_path:
+                    input_line = f"{request.prompt}@@{image_path}@@{audio_path}"
+                else:
+                    input_line = f"{request.prompt}@@@@{audio_path}"
+                f.write(input_line)
+                temp_input_file = f.name
+            
+            # Prepare inference command
+            cmd = [
+                "python", "-m", "torch.distributed.run",
+                "--standalone", f"--nproc_per_node={request.sp_size}",
+                "scripts/inference.py",
+                "--config", "configs/inference.yaml",
+                "--input_file", temp_input_file,
+                "--guidance_scale", str(request.guidance_scale),
+                "--audio_scale", str(request.audio_scale),
+                "--num_steps", str(request.num_steps)
+            ]
+            
+            if request.tea_cache_l1_thresh:
+                cmd.extend(["--tea_cache_l1_thresh", str(request.tea_cache_l1_thresh)])
+            
+            logger.info(f"Running inference with command: {' '.join(cmd)}")
+            
+            # Run inference
+            result = subprocess.run(cmd, capture_output=True, text=True)
+            
+            # Clean up temporary files
+            os.unlink(temp_input_file)
+            os.unlink(audio_path)
+            if image_path:
+                os.unlink(image_path)
+            
+            if result.returncode != 0:
+                logger.error(f"Inference failed: {result.stderr}")
+                raise Exception(f"Inference failed: {result.stderr}")
+            
+            # Find output video file
+            output_dir = "./outputs"
+            if os.path.exists(output_dir):
+                video_files = [f for f in os.listdir(output_dir) if f.endswith(('.mp4', '.avi'))]
+                if video_files:
+                    # Return the most recent video file
+                    video_files.sort(key=lambda x: os.path.getmtime(os.path.join(output_dir, x)), reverse=True)
+                    output_path = os.path.join(output_dir, video_files[0])
+                    processing_time = time.time() - start_time
+                    return output_path, processing_time, audio_generated, tts_method
+            
+            raise Exception("No output video generated")
+            
+        except Exception as e:
+            # Clean up any temporary files in case of error
+            try:
+                if 'audio_path' in locals() and audio_path and os.path.exists(audio_path):
+                    os.unlink(audio_path)
+                if 'image_path' in locals() and image_path and os.path.exists(image_path):
+                    os.unlink(image_path)
+                if 'temp_input_file' in locals() and os.path.exists(temp_input_file):
+                    os.unlink(temp_input_file)
+            except:
+                pass
+            
+            logger.error(f"Generation error: {str(e)}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+# Initialize API
+omni_api = OmniAvatarAPI()
+
+# Use FastAPI lifespan instead of deprecated on_event
+from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Startup
+    success = omni_api.load_model()
+    if not success:
+        logger.warning("WARNING: OmniAvatar model loading failed - running in limited mode")
+    
+    # Load TTS models
+    try:
+        await omni_api.tts_manager.load_models()
+        logger.info("SUCCESS: TTS models initialization completed")
+    except Exception as e:
+        logger.error(f"ERROR: TTS initialization failed: {e}")
+    
+    yield
+    
+    # Shutdown (if needed)
+    logger.info("Application shutting down...")
+
+# Create FastAPI app WITH lifespan parameter
+app = FastAPI(
+    title="OmniAvatar-14B API with Advanced TTS", 
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static files for serving generated videos
+app.mount("/outputs", StaticFiles(directory="outputs"), name="outputs")
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    tts_info = omni_api.tts_manager.get_tts_info()
+    
+    return {
+        "status": "healthy",
+        "model_loaded": omni_api.model_loaded,
+        "video_generation_available": omni_api.model_loaded,
+        "tts_only_mode": not omni_api.model_loaded,
+        "device": omni_api.device,
+        "supports_text_to_speech": True,
+        "supports_image_urls": omni_api.model_loaded,
+        "supports_audio_urls": omni_api.model_loaded,
+        "tts_system": "Advanced TTS with Robust Fallback",
+        "advanced_tts_available": ADVANCED_TTS_AVAILABLE,
+        "robust_tts_available": ROBUST_TTS_AVAILABLE,
+        **tts_info
+    }
+
+@app.get("/voices")
+async def get_voices():
+    """Get available voice configurations"""
+    try:
+        voices = await omni_api.tts_manager.get_available_voices()
+        return {"voices": voices}
+    except Exception as e:
+        logger.error(f"Error getting voices: {e}")
+        return {"error": str(e)}
+
+@app.post("/generate", response_model=GenerateResponse)
+async def generate_avatar(request: GenerateRequest):
+    """Generate avatar video from prompt, text/audio, and optional image URL"""
+    
+    logger.info(f"Generating avatar with prompt: {request.prompt}")
+    if request.text_to_speech:
+        logger.info(f"Text to speech: {request.text_to_speech[:100]}...")
+        logger.info(f"Voice ID: {request.voice_id}")
+    if request.audio_url:
+        logger.info(f"Audio URL: {request.audio_url}")
+    if request.image_url:
+        logger.info(f"Image URL: {request.image_url}")
+    
+    try:
+        output_path, processing_time, audio_generated, tts_method = await omni_api.generate_avatar(request)
+        
+        return GenerateResponse(
+            message="Generation completed successfully" + (" (TTS-only mode)" if not omni_api.model_loaded else ""),
+            output_path=get_video_url(output_path) if omni_api.model_loaded else output_path,
+            processing_time=processing_time,
+            audio_generated=audio_generated,
+            tts_method=tts_method
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Unexpected error: {e}")
+        raise HTTPException(status_code=500, detail=f"Unexpected error: {e}")
+
+# Enhanced Gradio interface
+def gradio_generate(prompt, text_to_speech, audio_url, image_url, voice_id, guidance_scale, audio_scale, num_steps):
+    """Gradio interface wrapper with robust TTS support"""
+    try:
+        # Create request object
+        request_data = {
+            "prompt": prompt,
+            "guidance_scale": guidance_scale,
+            "audio_scale": audio_scale,
+            "num_steps": int(num_steps)
+        }
+        
+        # Add audio source
+        if text_to_speech and text_to_speech.strip():
+            request_data["text_to_speech"] = text_to_speech
+            request_data["voice_id"] = voice_id or "21m00Tcm4TlvDq8ikWAM"
+        elif audio_url and audio_url.strip():
+            if omni_api.model_loaded:
+                request_data["audio_url"] = audio_url
+            else:
+                return "Error: Audio URL input requires full OmniAvatar models. Please use text-to-speech instead."
+        else:
+            return "Error: Please provide either text to speech or audio URL"
+        
+        if image_url and image_url.strip():
+            if omni_api.model_loaded:
+                request_data["image_url"] = image_url
+            else:
+                return "Error: Image URL input requires full OmniAvatar models for video generation."
+        
+        request = GenerateRequest(**request_data)
+        
+        # Run async function in sync context
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        output_path, processing_time, audio_generated, tts_method = loop.run_until_complete(omni_api.generate_avatar(request))
+        loop.close()
+        
+        success_message = f"SUCCESS: Generation completed in {processing_time:.1f}s using {tts_method}"
+        print(success_message)
+        
+        if omni_api.model_loaded:
+            return output_path
+        else:
+            return f"??? TTS Audio generated successfully using {tts_method}\nFile: {output_path}\n\nWARNING: Video generation unavailable (OmniAvatar models not found)"
+        
+    except Exception as e:
+        logger.error(f"Gradio generation error: {e}")
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+mode_info = " (TTS-Only Mode)" if not omni_api.model_loaded else ""
+description_extra = """
+WARNING: Running in TTS-Only Mode - OmniAvatar models not found. Only text-to-speech generation is available.
+To enable full video generation, the required model files need to be downloaded.
+""" if not omni_api.model_loaded else ""
+
+iface = gr.Interface(
+    fn=gradio_generate,
+    inputs=[
+        gr.Textbox(
+            label="Prompt", 
+            placeholder="Describe the character behavior (e.g., 'A friendly person explaining a concept')",
+            lines=2
+        ),
+        gr.Textbox(
+            label="Text to Speech", 
+            placeholder="Enter text to convert to speech",
+            lines=3,
+            info="Will use best available TTS system (Advanced or Fallback)"
+        ),
+        gr.Textbox(
+            label="OR Audio URL", 
+            placeholder="https://example.com/audio.mp3",
+            info="Direct URL to audio file (requires full models)" if not omni_api.model_loaded else "Direct URL to audio file"
+        ),
+        gr.Textbox(
+            label="Image URL (Optional)", 
+            placeholder="https://example.com/image.jpg",
+            info="Direct URL to reference image (requires full models)" if not omni_api.model_loaded else "Direct URL to reference image"
+        ),
+        gr.Dropdown(
+            choices=[
+                "21m00Tcm4TlvDq8ikWAM", 
+                "pNInz6obpgDQGcFmaJgB", 
+                "EXAVITQu4vr4xnSDxMaL",
+                "ErXwobaYiN019PkySvjV",
+                "TxGEqnHWrfGW9XjX",
+                "yoZ06aMxZJJ28mfd3POQ",
+                "AZnzlk1XvdvUeBnXmlld"
+            ],
+            value="21m00Tcm4TlvDq8ikWAM",
+            label="Voice Profile",
+            info="Choose voice characteristics for TTS generation"
+        ),
+        gr.Slider(minimum=1, maximum=10, value=5.0, label="Guidance Scale", info="4-6 recommended"),
+        gr.Slider(minimum=1, maximum=10, value=3.0, label="Audio Scale", info="Higher values = better lip-sync"),
+        gr.Slider(minimum=10, maximum=100, value=30, step=1, label="Number of Steps", info="20-50 recommended")
+    ],
+    outputs=gr.Video(label="Generated Avatar Video") if omni_api.model_loaded else gr.Textbox(label="TTS Output"),
+    title="[VIDEO] OmniAvatar-14B - Avatar Video Generation with Adaptive Body Animation",
+    description=f"""
+    Generate avatar videos with lip-sync from text prompts and speech using robust TTS system.
+    
+    {description_extra}
+    
+    **Robust TTS Architecture**
+    - **Primary**: Advanced TTS (Facebook VITS & SpeechT5) if available
+    - **Fallback**: Robust tone generation for 100% reliability
+    - **Automatic**: Seamless switching between methods
+    
+    **Features:**
+    - **Guaranteed Generation**: Always produces audio output
+    - **No Dependencies**: Works even without advanced models
+    - **High Availability**: Multiple fallback layers
+    - **Voice Profiles**: Multiple voice characteristics
+    - **Audio URL Support**: Use external audio files {"(full models required)" if not omni_api.model_loaded else ""}
+    - **Image URL Support**: Reference images for characters {"(full models required)" if not omni_api.model_loaded else ""}
+    
+    **Usage:**
+    1. Enter a character description in the prompt
+    2. **Enter text for speech generation** (recommended in current mode)
+    3. {"Optionally add reference image/audio URLs (requires full models)" if not omni_api.model_loaded else "Optionally add reference image URL and choose audio source"}
+    4. Choose voice profile and adjust parameters
+    5. Generate your {"audio" if not omni_api.model_loaded else "avatar video"}!
+    """,
+    examples=[
+        [
+            "A professional teacher explaining a mathematical concept with clear gestures",
+            "Hello students! Today we're going to learn about calculus and derivatives.",
+            "",
+            "",
+            "21m00Tcm4TlvDq8ikWAM",
+            5.0,
+            3.5,
+            30
+        ],
+        [
+            "A friendly presenter speaking confidently to an audience",
+            "Welcome everyone to our presentation on artificial intelligence!",
+            "",
+            "",
+            "pNInz6obpgDQGcFmaJgB", 
+            5.5,
+            4.0,
+            35
+        ]
+    ],
+    allow_flagging="never",
+    flagging_dir="/tmp/gradio_flagged"
+)
+
+# Mount Gradio app
+app = gr.mount_gradio_app(app, iface, path="/gradio")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+
+
+
+
+
+
+
+

app_temp.py

@@ -0,0 +1,827 @@
+import os
+import torch
+import tempfile
+import gradio as gr
+from fastapi import FastAPI, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, HttpUrl
+import subprocess
+import json
+from pathlib import Path
+import logging
+import requests
+from urllib.parse import urlparse
+from PIL import Image
+import io
+from typing import Optional
+import aiohttp
+import asyncio
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Set environment variables for matplotlib, gradio, and huggingface cache
+os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
+os.environ['GRADIO_ALLOW_FLAGGING'] = 'never'
+os.environ['HF_HOME'] = '/tmp/huggingface'
+# Use HF_HOME instead of deprecated TRANSFORMERS_CACHE
+os.environ['HF_DATASETS_CACHE'] = '/tmp/huggingface/datasets'
+os.environ['HUGGINGFACE_HUB_CACHE'] = '/tmp/huggingface/hub'
+
+# FastAPI app will be created after lifespan is defined
+
+
+
+# Create directories with proper permissions
+os.makedirs("outputs", exist_ok=True)
+os.makedirs("/tmp/matplotlib", exist_ok=True)
+os.makedirs("/tmp/huggingface", exist_ok=True)
+os.makedirs("/tmp/huggingface/transformers", exist_ok=True)
+os.makedirs("/tmp/huggingface/datasets", exist_ok=True)
+os.makedirs("/tmp/huggingface/hub", exist_ok=True)
+
+# Mount static files for serving generated videos  
+
+
+def get_video_url(output_path: str) -> str:
+    """Convert local file path to accessible URL"""
+    try:
+        from pathlib import Path
+        filename = Path(output_path).name
+        
+        # For HuggingFace Spaces, construct the URL
+        base_url = "https://bravedims-ai-avatar-chat.hf.space"
+        video_url = f"{base_url}/outputs/{filename}"
+        logger.info(f"Generated video URL: {video_url}")
+        return video_url
+    except Exception as e:
+        logger.error(f"Error creating video URL: {e}")
+        return output_path  # Fallback to original path
+
+# Pydantic models for request/response
+class GenerateRequest(BaseModel):
+    prompt: str
+    text_to_speech: Optional[str] = None  # Text to convert to speech
+    audio_url: Optional[HttpUrl] = None  # Direct audio URL
+    voice_id: Optional[str] = "21m00Tcm4TlvDq8ikWAM"  # Voice profile ID
+    image_url: Optional[HttpUrl] = None
+    guidance_scale: float = 5.0
+    audio_scale: float = 3.0
+    num_steps: int = 30
+    sp_size: int = 1
+    tea_cache_l1_thresh: Optional[float] = None
+
+class GenerateResponse(BaseModel):
+    message: str
+    output_path: str
+    processing_time: float
+    audio_generated: bool = False
+    tts_method: Optional[str] = None
+
+# Try to import TTS clients, but make them optional
+try:
+    from advanced_tts_client import AdvancedTTSClient
+    ADVANCED_TTS_AVAILABLE = True
+    logger.info("SUCCESS: Advanced TTS client available")
+except ImportError as e:
+    ADVANCED_TTS_AVAILABLE = False
+    logger.warning(f"WARNING: Advanced TTS client not available: {e}")
+
+# Always import the robust fallback
+try:
+    from robust_tts_client import RobustTTSClient
+    ROBUST_TTS_AVAILABLE = True
+    logger.info("SUCCESS: Robust TTS client available")
+except ImportError as e:
+    ROBUST_TTS_AVAILABLE = False
+    logger.error(f"ERROR: Robust TTS client not available: {e}")
+
+class TTSManager:
+    """Manages multiple TTS clients with fallback chain"""
+    
+    def __init__(self):
+        # Initialize TTS clients based on availability
+        self.advanced_tts = None
+        self.robust_tts = None
+        self.clients_loaded = False
+        
+        if ADVANCED_TTS_AVAILABLE:
+            try:
+                self.advanced_tts = AdvancedTTSClient()
+                logger.info("SUCCESS: Advanced TTS client initialized")
+            except Exception as e:
+                logger.warning(f"WARNING: Advanced TTS client initialization failed: {e}")
+        
+        if ROBUST_TTS_AVAILABLE:
+            try:
+                self.robust_tts = RobustTTSClient()
+                logger.info("SUCCESS: Robust TTS client initialized")
+            except Exception as e:
+                logger.error(f"ERROR: Robust TTS client initialization failed: {e}")
+        
+        if not self.advanced_tts and not self.robust_tts:
+            logger.error("ERROR: No TTS clients available!")
+        
+    async def load_models(self):
+        """Load TTS models"""
+        try:
+            logger.info("Loading TTS models...")
+            
+            # Try to load advanced TTS first
+            if self.advanced_tts:
+                try:
+                    logger.info("[PROCESS] Loading advanced TTS models (this may take a few minutes)...")
+                    success = await self.advanced_tts.load_models()
+                    if success:
+                        logger.info("SUCCESS: Advanced TTS models loaded successfully")
+                    else:
+                        logger.warning("WARNING: Advanced TTS models failed to load")
+                except Exception as e:
+                    logger.warning(f"WARNING: Advanced TTS loading error: {e}")
+            
+            # Always ensure robust TTS is available
+            if self.robust_tts:
+                try:
+                    await self.robust_tts.load_model()
+                    logger.info("SUCCESS: Robust TTS fallback ready")
+                except Exception as e:
+                    logger.error(f"ERROR: Robust TTS loading failed: {e}")
+            
+            self.clients_loaded = True
+            return True
+            
+        except Exception as e:
+            logger.error(f"ERROR: TTS manager initialization failed: {e}")
+            return False
+    
+    async def text_to_speech(self, text: str, voice_id: Optional[str] = None) -> tuple[str, str]:
+        """
+        Convert text to speech with fallback chain
+        Returns: (audio_file_path, method_used)
+        """
+        if not self.clients_loaded:
+            logger.info("TTS models not loaded, loading now...")
+            await self.load_models()
+        
+        logger.info(f"Generating speech: {text[:50]}...")
+        logger.info(f"Voice ID: {voice_id}")
+        
+        # Try Advanced TTS first (Facebook VITS / SpeechT5)
+        if self.advanced_tts:
+            try:
+                audio_path = await self.advanced_tts.text_to_speech(text, voice_id)
+                return audio_path, "Facebook VITS/SpeechT5"
+            except Exception as advanced_error:
+                logger.warning(f"Advanced TTS failed: {advanced_error}")
+        
+        # Fall back to robust TTS
+        if self.robust_tts:
+            try:
+                logger.info("Falling back to robust TTS...")
+                audio_path = await self.robust_tts.text_to_speech(text, voice_id)
+                return audio_path, "Robust TTS (Fallback)"
+            except Exception as robust_error:
+                logger.error(f"Robust TTS also failed: {robust_error}")
+        
+        # If we get here, all methods failed
+        logger.error("All TTS methods failed!")
+        raise HTTPException(
+            status_code=500, 
+            detail="All TTS methods failed. Please check system configuration."
+        )
+    
+    async def get_available_voices(self):
+        """Get available voice configurations"""
+        try:
+            if self.advanced_tts and hasattr(self.advanced_tts, 'get_available_voices'):
+                return await self.advanced_tts.get_available_voices()
+        except:
+            pass
+        
+        # Return default voices if advanced TTS not available
+        return {
+            "21m00Tcm4TlvDq8ikWAM": "Female (Neutral)",
+            "pNInz6obpgDQGcFmaJgB": "Male (Professional)", 
+            "EXAVITQu4vr4xnSDxMaL": "Female (Sweet)",
+            "ErXwobaYiN019PkySvjV": "Male (Professional)",
+            "TxGEqnHWrfGW9XjX": "Male (Deep)",
+            "yoZ06aMxZJJ28mfd3POQ": "Unisex (Friendly)",
+            "AZnzlk1XvdvUeBnXmlld": "Female (Strong)"
+        }
+    
+    def get_tts_info(self):
+        """Get TTS system information"""
+        info = {
+            "clients_loaded": self.clients_loaded,
+            "advanced_tts_available": self.advanced_tts is not None,
+            "robust_tts_available": self.robust_tts is not None,
+            "primary_method": "Robust TTS"
+        }
+        
+        try:
+            if self.advanced_tts and hasattr(self.advanced_tts, 'get_model_info'):
+                advanced_info = self.advanced_tts.get_model_info()
+                info.update({
+                    "advanced_tts_loaded": advanced_info.get("models_loaded", False),
+                    "transformers_available": advanced_info.get("transformers_available", False),
+                    "primary_method": "Facebook VITS/SpeechT5" if advanced_info.get("models_loaded") else "Robust TTS",
+                    "device": advanced_info.get("device", "cpu"),
+                    "vits_available": advanced_info.get("vits_available", False),
+                    "speecht5_available": advanced_info.get("speecht5_available", False)
+                })
+        except Exception as e:
+            logger.debug(f"Could not get advanced TTS info: {e}")
+        
+        return info
+
+# Import the VIDEO-FOCUSED engine
+try:
+    from omniavatar_video_engine import video_engine
+    VIDEO_ENGINE_AVAILABLE = True
+    logger.info("SUCCESS: OmniAvatar Video Engine available")
+except ImportError as e:
+    VIDEO_ENGINE_AVAILABLE = False
+    logger.error(f"ERROR: OmniAvatar Video Engine not available: {e}")
+
+class OmniAvatarAPI:
+    def __init__(self):
+        self.model_loaded = False
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.tts_manager = TTSManager()
+        logger.info(f"Using device: {self.device}")
+        logger.info("Initialized with robust TTS system")
+        
+    def load_model(self):
+        """Load the OmniAvatar model - now more flexible"""
+        try:
+            # Check if models are downloaded (but don't require them)
+            model_paths = [
+                "./pretrained_models/Wan2.1-T2V-14B",
+                "./pretrained_models/OmniAvatar-14B", 
+                "./pretrained_models/wav2vec2-base-960h"
+            ]
+            
+            missing_models = []
+            for path in model_paths:
+                if not os.path.exists(path):
+                    missing_models.append(path)
+            
+            if missing_models:
+                logger.warning("WARNING: Some OmniAvatar models not found:")
+                for model in missing_models:
+                    logger.warning(f"   - {model}")
+                logger.info("TIP: App will run in TTS-only mode (no video generation)")
+                logger.info("TIP: To enable full avatar generation, download the required models")
+                
+                # Set as loaded but in limited mode
+                self.model_loaded = False  # Video generation disabled
+                return True  # But app can still run
+            else:
+                self.model_loaded = True
+                logger.info("SUCCESS: All OmniAvatar models found - full functionality enabled")
+                return True
+                
+        except Exception as e:
+            logger.error(f"Error checking models: {str(e)}")
+            logger.info("TIP: Continuing in TTS-only mode")
+            self.model_loaded = False
+            return True  # Continue running
+    
+    async def download_file(self, url: str, suffix: str = "") -> str:
+        """Download file from URL and save to temporary location"""
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(str(url)) as response:
+                    if response.status != 200:
+                        raise HTTPException(status_code=400, detail=f"Failed to download file from URL: {url}")
+                    
+                    content = await response.read()
+                    
+                    # Create temporary file
+                    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+                    temp_file.write(content)
+                    temp_file.close()
+                    
+                    return temp_file.name
+                    
+        except aiohttp.ClientError as e:
+            logger.error(f"Network error downloading {url}: {e}")
+            raise HTTPException(status_code=400, detail=f"Network error downloading file: {e}")
+        except Exception as e:
+            logger.error(f"Error downloading file from {url}: {e}")
+            raise HTTPException(status_code=500, detail=f"Error downloading file: {e}")
+    
+    def validate_audio_url(self, url: str) -> bool:
+        """Validate if URL is likely an audio file"""
+        try:
+            parsed = urlparse(url)
+            # Check for common audio file extensions
+            audio_extensions = ['.mp3', '.wav', '.m4a', '.ogg', '.aac', '.flac']
+            is_audio_ext = any(parsed.path.lower().endswith(ext) for ext in audio_extensions)
+            
+            return is_audio_ext or 'audio' in url.lower()
+        except:
+            return False
+    
+    def validate_image_url(self, url: str) -> bool:
+        """Validate if URL is likely an image file"""
+        try:
+            parsed = urlparse(url)
+            image_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.bmp', '.gif']
+            return any(parsed.path.lower().endswith(ext) for ext in image_extensions)
+        except:
+            return False
+    
+    async def generate_avatar(self, request: GenerateRequest) -> tuple[str, float, bool, str]:
+        """Generate avatar VIDEO - PRIMARY FUNCTIONALITY"""
+        import time
+        start_time = time.time()
+        audio_generated = False
+        method_used = "Unknown"
+        
+        logger.info("[VIDEO] STARTING AVATAR VIDEO GENERATION")
+        logger.info(f"[INFO] Prompt: {request.prompt}")
+        
+        if VIDEO_ENGINE_AVAILABLE:
+            try:
+                # PRIORITIZE VIDEO GENERATION
+                logger.info("[TARGET] Using OmniAvatar Video Engine for FULL video generation")
+                
+                # Handle audio source
+                audio_path = None
+                if request.text_to_speech:
+                    logger.info("[MIC] Generating audio from text...")
+                    audio_path, method_used = await self.tts_manager.text_to_speech(
+                        request.text_to_speech, 
+                        request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                    )
+                    audio_generated = True
+                elif request.audio_url:
+                    logger.info("?? Downloading audio from URL...")
+                    audio_path = await self.download_file(str(request.audio_url), ".mp3")
+                    method_used = "External Audio"
+                else:
+                    raise HTTPException(status_code=400, detail="Either text_to_speech or audio_url required for video generation")
+                
+                # Handle image if provided
+                image_path = None
+                if request.image_url:
+                    logger.info("[IMAGE] Downloading reference image...")
+                    parsed = urlparse(str(request.image_url))
+                    ext = os.path.splitext(parsed.path)[1] or ".jpg"
+                    image_path = await self.download_file(str(request.image_url), ext)
+                
+                # GENERATE VIDEO using OmniAvatar engine
+                logger.info("[VIDEO] Generating avatar video with adaptive body animation...")
+                video_path, generation_time = video_engine.generate_avatar_video(
+                    prompt=request.prompt,
+                    audio_path=audio_path,
+                    image_path=image_path,
+                    guidance_scale=request.guidance_scale,
+                    audio_scale=request.audio_scale,
+                    num_steps=request.num_steps
+                )
+                
+                processing_time = time.time() - start_time
+                logger.info(f"SUCCESS: VIDEO GENERATED successfully in {processing_time:.1f}s")
+                
+                # Cleanup temporary files
+                if audio_path and os.path.exists(audio_path):
+                    os.unlink(audio_path)
+                if image_path and os.path.exists(image_path):
+                    os.unlink(image_path)
+                
+                return video_path, processing_time, audio_generated, f"OmniAvatar Video Generation ({method_used})"
+                
+            except Exception as e:
+                logger.error(f"ERROR: Video generation failed: {e}")
+                # For a VIDEO generation app, we should NOT fall back to audio-only
+                # Instead, provide clear guidance
+                if "models" in str(e).lower():
+                    raise HTTPException(
+                        status_code=503,
+                        detail=f"Video generation requires OmniAvatar models (~30GB). Please run model download script. Error: {str(e)}"
+                    )
+                else:
+                    raise HTTPException(status_code=500, detail=f"Video generation failed: {str(e)}")
+        
+        # If video engine not available, this is a critical error for a VIDEO app
+        raise HTTPException(
+            status_code=503, 
+            detail="Video generation engine not available. This application requires OmniAvatar models for video generation."
+        )
+
+    async def generate_avatar_BACKUP(self, request: GenerateRequest) -> tuple[str, float, bool, str]:
+        """OLD TTS-ONLY METHOD - kept as backup reference
+        """Generate avatar video from prompt and audio/text - now handles missing models"""
+        import time
+        start_time = time.time()
+        audio_generated = False
+        tts_method = None
+        
+        try:
+            # Check if video generation is available
+            if not self.model_loaded:
+                logger.info("??? Running in TTS-only mode (OmniAvatar models not available)")
+                
+                # Only generate audio, no video
+                if request.text_to_speech:
+                    logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
+                    audio_path, tts_method = await self.tts_manager.text_to_speech(
+                        request.text_to_speech, 
+                        request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                    )
+                    
+                    # Return the audio file as the "output"
+                    processing_time = time.time() - start_time
+                    logger.info(f"SUCCESS: TTS completed in {processing_time:.1f}s using {tts_method}")
+                    return audio_path, processing_time, True, f"{tts_method} (TTS-only mode)"
+                else:
+                    raise HTTPException(
+                        status_code=503,
+                        detail="Video generation unavailable. OmniAvatar models not found. Only TTS from text is supported."
+                    )
+            
+            # Original video generation logic (when models are available)
+            # Determine audio source
+            audio_path = None
+            
+            if request.text_to_speech:
+                # Generate speech from text using TTS manager
+                logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
+                audio_path, tts_method = await self.tts_manager.text_to_speech(
+                    request.text_to_speech, 
+                    request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                )
+                audio_generated = True
+                
+            elif request.audio_url:
+                # Download audio from provided URL
+                logger.info(f"Downloading audio from URL: {request.audio_url}")
+                if not self.validate_audio_url(str(request.audio_url)):
+                    logger.warning(f"Audio URL may not be valid: {request.audio_url}")
+                
+                audio_path = await self.download_file(str(request.audio_url), ".mp3")
+                tts_method = "External Audio URL"
+            
+            else:
+                raise HTTPException(
+                    status_code=400, 
+                    detail="Either text_to_speech or audio_url must be provided"
+                )
+            
+            # Download image if provided
+            image_path = None
+            if request.image_url:
+                logger.info(f"Downloading image from URL: {request.image_url}")
+                if not self.validate_image_url(str(request.image_url)):
+                    logger.warning(f"Image URL may not be valid: {request.image_url}")
+                
+                # Determine image extension from URL or default to .jpg
+                parsed = urlparse(str(request.image_url))
+                ext = os.path.splitext(parsed.path)[1] or ".jpg"
+                image_path = await self.download_file(str(request.image_url), ext)
+            
+            # Create temporary input file for inference
+            with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
+                if image_path:
+                    input_line = f"{request.prompt}@@{image_path}@@{audio_path}"
+                else:
+                    input_line = f"{request.prompt}@@@@{audio_path}"
+                f.write(input_line)
+                temp_input_file = f.name
+            
+            # Prepare inference command
+            cmd = [
+                "python", "-m", "torch.distributed.run",
+                "--standalone", f"--nproc_per_node={request.sp_size}",
+                "scripts/inference.py",
+                "--config", "configs/inference.yaml",
+                "--input_file", temp_input_file,
+                "--guidance_scale", str(request.guidance_scale),
+                "--audio_scale", str(request.audio_scale),
+                "--num_steps", str(request.num_steps)
+            ]
+            
+            if request.tea_cache_l1_thresh:
+                cmd.extend(["--tea_cache_l1_thresh", str(request.tea_cache_l1_thresh)])
+            
+            logger.info(f"Running inference with command: {' '.join(cmd)}")
+            
+            # Run inference
+            result = subprocess.run(cmd, capture_output=True, text=True)
+            
+            # Clean up temporary files
+            os.unlink(temp_input_file)
+            os.unlink(audio_path)
+            if image_path:
+                os.unlink(image_path)
+            
+            if result.returncode != 0:
+                logger.error(f"Inference failed: {result.stderr}")
+                raise Exception(f"Inference failed: {result.stderr}")
+            
+            # Find output video file
+            output_dir = "./outputs"
+            if os.path.exists(output_dir):
+                video_files = [f for f in os.listdir(output_dir) if f.endswith(('.mp4', '.avi'))]
+                if video_files:
+                    # Return the most recent video file
+                    video_files.sort(key=lambda x: os.path.getmtime(os.path.join(output_dir, x)), reverse=True)
+                    output_path = os.path.join(output_dir, video_files[0])
+                    processing_time = time.time() - start_time
+                    return output_path, processing_time, audio_generated, tts_method
+            
+            raise Exception("No output video generated")
+            
+        except Exception as e:
+            # Clean up any temporary files in case of error
+            try:
+                if 'audio_path' in locals() and audio_path and os.path.exists(audio_path):
+                    os.unlink(audio_path)
+                if 'image_path' in locals() and image_path and os.path.exists(image_path):
+                    os.unlink(image_path)
+                if 'temp_input_file' in locals() and os.path.exists(temp_input_file):
+                    os.unlink(temp_input_file)
+            except:
+                pass
+            
+            logger.error(f"Generation error: {str(e)}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+# Initialize API
+omni_api = OmniAvatarAPI()
+
+# Use FastAPI lifespan instead of deprecated on_event
+from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Startup
+    success = omni_api.load_model()
+    if not success:
+        logger.warning("WARNING: OmniAvatar model loading failed - running in limited mode")
+    
+    # Load TTS models
+    try:
+        await omni_api.tts_manager.load_models()
+        logger.info("SUCCESS: TTS models initialization completed")
+    except Exception as e:
+        logger.error(f"ERROR: TTS initialization failed: {e}")
+    
+    yield
+    
+    # Shutdown (if needed)
+    logger.info("Application shutting down...")
+
+# Create FastAPI app WITH lifespan parameter
+app = FastAPI(
+    title="OmniAvatar-14B API with Advanced TTS", 
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static files for serving generated videos
+app.mount("/outputs", StaticFiles(directory="outputs"), name="outputs")
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    tts_info = omni_api.tts_manager.get_tts_info()
+    
+    return {
+        "status": "healthy",
+        "model_loaded": omni_api.model_loaded,
+        "video_generation_available": omni_api.model_loaded,
+        "tts_only_mode": not omni_api.model_loaded,
+        "device": omni_api.device,
+        "supports_text_to_speech": True,
+        "supports_image_urls": omni_api.model_loaded,
+        "supports_audio_urls": omni_api.model_loaded,
+        "tts_system": "Advanced TTS with Robust Fallback",
+        "advanced_tts_available": ADVANCED_TTS_AVAILABLE,
+        "robust_tts_available": ROBUST_TTS_AVAILABLE,
+        **tts_info
+    }
+
+@app.get("/voices")
+async def get_voices():
+    """Get available voice configurations"""
+    try:
+        voices = await omni_api.tts_manager.get_available_voices()
+        return {"voices": voices}
+    except Exception as e:
+        logger.error(f"Error getting voices: {e}")
+        return {"error": str(e)}
+
+@app.post("/generate", response_model=GenerateResponse)
+async def generate_avatar(request: GenerateRequest):
+    """Generate avatar video from prompt, text/audio, and optional image URL"""
+    
+    logger.info(f"Generating avatar with prompt: {request.prompt}")
+    if request.text_to_speech:
+        logger.info(f"Text to speech: {request.text_to_speech[:100]}...")
+        logger.info(f"Voice ID: {request.voice_id}")
+    if request.audio_url:
+        logger.info(f"Audio URL: {request.audio_url}")
+    if request.image_url:
+        logger.info(f"Image URL: {request.image_url}")
+    
+    try:
+        output_path, processing_time, audio_generated, tts_method = await omni_api.generate_avatar(request)
+        
+        return GenerateResponse(
+            message="Generation completed successfully" + (" (TTS-only mode)" if not omni_api.model_loaded else ""),
+            output_path=get_video_url(output_path) if omni_api.model_loaded else output_path,
+            processing_time=processing_time,
+            audio_generated=audio_generated,
+            tts_method=tts_method
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Unexpected error: {e}")
+        raise HTTPException(status_code=500, detail=f"Unexpected error: {e}")
+
+# Enhanced Gradio interface
+def gradio_generate(prompt, text_to_speech, audio_url, image_url, voice_id, guidance_scale, audio_scale, num_steps):
+    """Gradio interface wrapper with robust TTS support"""
+    try:
+        # Create request object
+        request_data = {
+            "prompt": prompt,
+            "guidance_scale": guidance_scale,
+            "audio_scale": audio_scale,
+            "num_steps": int(num_steps)
+        }
+        
+        # Add audio source
+        if text_to_speech and text_to_speech.strip():
+            request_data["text_to_speech"] = text_to_speech
+            request_data["voice_id"] = voice_id or "21m00Tcm4TlvDq8ikWAM"
+        elif audio_url and audio_url.strip():
+            if omni_api.model_loaded:
+                request_data["audio_url"] = audio_url
+            else:
+                return "Error: Audio URL input requires full OmniAvatar models. Please use text-to-speech instead."
+        else:
+            return "Error: Please provide either text to speech or audio URL"
+        
+        if image_url and image_url.strip():
+            if omni_api.model_loaded:
+                request_data["image_url"] = image_url
+            else:
+                return "Error: Image URL input requires full OmniAvatar models for video generation."
+        
+        request = GenerateRequest(**request_data)
+        
+        # Run async function in sync context
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        output_path, processing_time, audio_generated, tts_method = loop.run_until_complete(omni_api.generate_avatar(request))
+        loop.close()
+        
+        success_message = f"SUCCESS: Generation completed in {processing_time:.1f}s using {tts_method}"
+        print(success_message)
+        
+        if omni_api.model_loaded:
+            return output_path
+        else:
+            return f"??? TTS Audio generated successfully using {tts_method}\nFile: {output_path}\n\nWARNING: Video generation unavailable (OmniAvatar models not found)"
+        
+    except Exception as e:
+        logger.error(f"Gradio generation error: {e}")
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+mode_info = " (TTS-Only Mode)" if not omni_api.model_loaded else ""
+description_extra = """
+WARNING: Running in TTS-Only Mode - OmniAvatar models not found. Only text-to-speech generation is available.
+To enable full video generation, the required model files need to be downloaded.
+""" if not omni_api.model_loaded else ""
+
+iface = gr.Interface(
+    fn=gradio_generate,
+    inputs=[
+        gr.Textbox(
+            label="Prompt", 
+            placeholder="Describe the character behavior (e.g., 'A friendly person explaining a concept')",
+            lines=2
+        ),
+        gr.Textbox(
+            label="Text to Speech", 
+            placeholder="Enter text to convert to speech",
+            lines=3,
+            info="Will use best available TTS system (Advanced or Fallback)"
+        ),
+        gr.Textbox(
+            label="OR Audio URL", 
+            placeholder="https://example.com/audio.mp3",
+            info="Direct URL to audio file (requires full models)" if not omni_api.model_loaded else "Direct URL to audio file"
+        ),
+        gr.Textbox(
+            label="Image URL (Optional)", 
+            placeholder="https://example.com/image.jpg",
+            info="Direct URL to reference image (requires full models)" if not omni_api.model_loaded else "Direct URL to reference image"
+        ),
+        gr.Dropdown(
+            choices=[
+                "21m00Tcm4TlvDq8ikWAM", 
+                "pNInz6obpgDQGcFmaJgB", 
+                "EXAVITQu4vr4xnSDxMaL",
+                "ErXwobaYiN019PkySvjV",
+                "TxGEqnHWrfGW9XjX",
+                "yoZ06aMxZJJ28mfd3POQ",
+                "AZnzlk1XvdvUeBnXmlld"
+            ],
+            value="21m00Tcm4TlvDq8ikWAM",
+            label="Voice Profile",
+            info="Choose voice characteristics for TTS generation"
+        ),
+        gr.Slider(minimum=1, maximum=10, value=5.0, label="Guidance Scale", info="4-6 recommended"),
+        gr.Slider(minimum=1, maximum=10, value=3.0, label="Audio Scale", info="Higher values = better lip-sync"),
+        gr.Slider(minimum=10, maximum=100, value=30, step=1, label="Number of Steps", info="20-50 recommended")
+    ],
+    outputs=gr.Video(label="Generated Avatar Video") if omni_api.model_loaded else gr.Textbox(label="TTS Output"),
+    title="[VIDEO] OmniAvatar-14B - Avatar Video Generation with Adaptive Body Animation",
+    description=f"""
+    Generate avatar videos with lip-sync from text prompts and speech using robust TTS system.
+    
+    {description_extra}
+    
+    **Robust TTS Architecture**
+    - **Primary**: Advanced TTS (Facebook VITS & SpeechT5) if available
+    - **Fallback**: Robust tone generation for 100% reliability
+    - **Automatic**: Seamless switching between methods
+    
+    **Features:**
+    - **Guaranteed Generation**: Always produces audio output
+    - **No Dependencies**: Works even without advanced models
+    - **High Availability**: Multiple fallback layers
+    - **Voice Profiles**: Multiple voice characteristics
+    - **Audio URL Support**: Use external audio files {"(full models required)" if not omni_api.model_loaded else ""}
+    - **Image URL Support**: Reference images for characters {"(full models required)" if not omni_api.model_loaded else ""}
+    
+    **Usage:**
+    1. Enter a character description in the prompt
+    2. **Enter text for speech generation** (recommended in current mode)
+    3. {"Optionally add reference image/audio URLs (requires full models)" if not omni_api.model_loaded else "Optionally add reference image URL and choose audio source"}
+    4. Choose voice profile and adjust parameters
+    5. Generate your {"audio" if not omni_api.model_loaded else "avatar video"}!
+    """,
+    examples=[
+        [
+            "A professional teacher explaining a mathematical concept with clear gestures",
+            "Hello students! Today we're going to learn about calculus and derivatives.",
+            "",
+            "",
+            "21m00Tcm4TlvDq8ikWAM",
+            5.0,
+            3.5,
+            30
+        ],
+        [
+            "A friendly presenter speaking confidently to an audience",
+            "Welcome everyone to our presentation on artificial intelligence!",
+            "",
+            "",
+            "pNInz6obpgDQGcFmaJgB", 
+            5.5,
+            4.0,
+            35
+        ]
+    ],
+    allow_flagging="never",
+    flagging_dir="/tmp/gradio_flagged"
+)
+
+# Mount Gradio app
+app = gr.mount_gradio_app(app, iface, path="/gradio")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+
+
+
+
+
+
+