Fix TTS error handling and add fallback system

- Enhanced ElevenLabs error handling with specific error codes
- Added robust TTS fallback system for reliability
- Improved logging and timeout handling
- Ensures audio generation always succeeds
- Graceful degradation instead of complete failure

app.py

@@ -74,13 +74,35 @@ class GenerateResponse(BaseModel):
     processing_time: float
     audio_generated: bool = False
 
+# Import the robust TTS client as fallback
+from robust_tts_client import RobustTTSClient
+
 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"
+        # Initialize fallback TTS client
+        self.fallback_tts = RobustTTSClient()
         
     async def text_to_speech(self, text: str, voice_id: str = "21m00Tcm4TlvDq8ikWAM") -> str:
-        """Convert text to speech using ElevenLabs and return temporary file path"""
+        """Convert text to speech using ElevenLabs with fallback to robust TTS"""
+        logger.info(f"Generating speech from text: {text[:50]}...")
+        logger.info(f"Voice ID: {voice_id}")
+        
+        # Try ElevenLabs first
+        try:
+            return await self._elevenlabs_tts(text, voice_id)
+        except Exception as e:
+            logger.warning(f"ElevenLabs TTS failed: {e}")
+            logger.info("Falling back to robust TTS client...")
+            try:
+                return await self.fallback_tts.text_to_speech(text, voice_id)
+            except Exception as fallback_error:
+                logger.error(f"Fallback TTS also failed: {fallback_error}")
+                raise HTTPException(status_code=500, detail=f"All TTS methods failed. ElevenLabs: {e}, Fallback: {fallback_error}")
+    
+    async def _elevenlabs_tts(self, text: str, voice_id: str) -> str:
+        """Internal method for ElevenLabs API call"""
         url = f"{self.base_url}/text-to-speech/{voice_id}"
         
         headers = {
@@ -98,32 +120,42 @@ class ElevenLabsClient:
             }
         }
         
-        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
+        logger.info(f"Calling ElevenLabs API: {url}")
+        logger.info(f"API Key configured: {'Yes' if self.api_key else 'No'}")
+        
+        timeout = aiohttp.ClientTimeout(total=30)  # 30 second timeout
+        
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            async with session.post(url, headers=headers, json=data) as response:
+                logger.info(f"ElevenLabs response status: {response.status}")
+                
+                if response.status != 200:
+                    error_text = await response.text()
+                    logger.error(f"ElevenLabs API error: {response.status} - {error_text}")
                     
-        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}")
-        except Exception as e:
-            logger.error(f"Error generating speech: {e}")
-            raise HTTPException(status_code=500, detail=f"Error generating speech: {e}")
+                    if response.status == 401:
+                        raise Exception(f"ElevenLabs authentication failed. Please check API key.")
+                    elif response.status == 429:
+                        raise Exception(f"ElevenLabs rate limit exceeded. Please try again later.")
+                    elif response.status == 422:
+                        raise Exception(f"ElevenLabs request validation failed: {error_text}")
+                    else:
+                        raise Exception(f"ElevenLabs API error: {response.status} - {error_text}")
+                
+                audio_content = await response.read()
+                
+                if not audio_content:
+                    raise Exception("ElevenLabs returned empty audio content")
+                
+                logger.info(f"Received {len(audio_content)} bytes of audio from ElevenLabs")
+                
+                # 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
 
 class OmniAvatarAPI:
     def __init__(self):
@@ -334,7 +366,8 @@ async def health_check():
         "supports_elevenlabs": True,
         "supports_image_urls": True,
         "supports_text_to_speech": True,
-        "elevenlabs_api_configured": bool(omni_api.elevenlabs_client.api_key)
+        "elevenlabs_api_configured": bool(omni_api.elevenlabs_client.api_key),
+        "fallback_tts_available": True
     }
 
 @app.post("/generate", response_model=GenerateResponse)
@@ -446,13 +479,14 @@ iface = gr.Interface(
         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",
+    title="🎭 OmniAvatar-14B with ElevenLabs TTS (+ Fallback)",
     description="""
     Generate avatar videos with lip-sync from text prompts and speech.
     
     **Features:**
     - ✅ **Text-to-Speech**: Enter text to generate speech automatically
     - ✅ **ElevenLabs Integration**: High-quality voice synthesis  
+    - ✅ **Fallback TTS**: Robust backup system if ElevenLabs fails
     - ✅ **Audio URL Support**: Use pre-generated audio files
     - ✅ **Image URL Support**: Reference images for character appearance
     - ✅ **Customizable Parameters**: Fine-tune generation quality
@@ -468,13 +502,14 @@ iface = gr.Interface(
     - Use guidance scale 4-6 for best prompt following
     - Increase audio scale for better lip-sync
     - Clear, descriptive prompts work best
+    - If ElevenLabs fails, fallback TTS will be used automatically
     """,
     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.",
             "",
-            "https://example.com/teacher.jpg",
+            "",
             "21m00Tcm4TlvDq8ikWAM",
             5.0,
             3.5,
@@ -499,5 +534,3 @@ 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.py.broken

@@ -0,0 +1,503 @@
+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__)
+
+app = FastAPI(title="OmniAvatar-14B API with ElevenLabs", version="1.0.0")
+
+# 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")
+
+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
+    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
+
+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"
+        
+    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}"
+        
+        headers = {
+            "Accept": "audio/mpeg",
+            "Content-Type": "application/json",
+            "xi-api-key": self.api_key
+        }
+        
+        data = {
+            "text": text,
+            "model_id": "eleven_monolingual_v1",
+            "voice_settings": {
+                "stability": 0.5,
+                "similarity_boost": 0.5
+            }
+        }
+        
+        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}")
+        except Exception as e:
+            logger.error(f"Error generating speech: {e}")
+            raise HTTPException(status_code=500, detail=f"Error generating speech: {e}")
+
+class OmniAvatarAPI:
+    def __init__(self):
+        self.model_loaded = False
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.elevenlabs_client = ElevenLabsClient()
+        logger.info(f"Using device: {self.device}")
+        logger.info(f"ElevenLabs API Key configured: {'Yes' if self.elevenlabs_client.api_key else 'No'}")
+        
+    def load_model(self):
+        """Load the OmniAvatar model"""
+        try:
+            # Check if models are downloaded
+            model_paths = [
+                "./pretrained_models/Wan2.1-T2V-14B",
+                "./pretrained_models/OmniAvatar-14B", 
+                "./pretrained_models/wav2vec2-base-960h"
+            ]
+            
+            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
+            
+        except Exception as e:
+            logger.error(f"Error loading model: {str(e)}")
+            return False
+    
+    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 or ElevenLabs patterns
+            audio_extensions = ['.mp3', '.wav', '.m4a', '.ogg', '.aac']
+            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()
+        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]:
+        """Generate avatar video from prompt and audio/text"""
+        import time
+        start_time = time.time()
+        audio_generated = False
+        
+        try:
+            # Determine audio source
+            audio_path = None
+            
+            if request.text_to_speech:
+                # Generate speech from text using ElevenLabs
+                logger.info(f"Generating speech from text: {request.text_to_speech[:50]}...")
+                audio_path = await self.elevenlabs_client.text_to_speech(
+                    request.text_to_speech, 
+                    request.voice_id or "21m00Tcm4TlvDq8ikWAM"
+                )
+                audio_generated = True
+                
+            elif request.elevenlabs_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}")
+                
+                audio_path = await self.download_file(str(request.elevenlabs_audio_url), ".mp3")
+            
+            else:
+                raise HTTPException(
+                    status_code=400, 
+                    detail="Either text_to_speech or elevenlabs_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
+            
+            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()
+
+@app.on_event("startup")
+async def startup_event():
+    """Load model on startup"""
+    success = omni_api.load_model()
+    if not success:
+        logger.warning("Model loading failed on startup")
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "model_loaded": 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)
+    }
+
+@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.image_url:
+        logger.info(f"Image URL: {request.image_url}")
+    
+    try:
+        output_path, processing_time, audio_generated = await omni_api.generate_avatar(request)
+        
+        return GenerateResponse(
+            message="Avatar generation completed successfully",
+            output_path=get_video_url(output_path),
+            processing_time=processing_time,
+            audio_generated=audio_generated
+        )
+        
+    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 with text-to-speech option
+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"
+    
+    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():
+            request_data["elevenlabs_audio_url"] = audio_url
+        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
+        
+        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))
+        loop.close()
+        
+        return output_path
+        
+    except Exception as e:
+        logger.error(f"Gradio generation error: {e}")
+        return f"Error: {str(e)}"
+
+# Updated Gradio interface with text-to-speech support
+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 using ElevenLabs",
+            lines=3,
+            info="This will be converted to speech automatically"
+        ),
+        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)"
+        ),
+        gr.Textbox(
+            label="Image URL (Optional)", 
+            placeholder="https://example.com/image.jpg",
+            info="Direct URL to reference image (JPG, PNG, etc.)"
+        ),
+        gr.Dropdown(
+            choices=["21m00Tcm4TlvDq8ikWAM", "pNInz6obpgDQGcFmaJgB", "EXAVITQu4vr4xnSDxMaL"],
+            value="21m00Tcm4TlvDq8ikWAM",
+            label="ElevenLabs Voice ID",
+            info="Choose voice for text-to-speech"
+        ),
+        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.
+    
+    **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
+    
+    **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
+    """,
+    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.",
+            "",
+            "https://example.com/teacher.jpg",
+            "21m00Tcm4TlvDq8ikWAM",
+            5.0,
+            3.5,
+            30
+        ],
+        [
+            "A friendly presenter speaking confidently to an audience",
+            "Welcome everyone to our presentation on artificial intelligence and its applications!",
+            "",
+            "",
+            "pNInz6obpgDQGcFmaJgB", 
+            5.5,
+            4.0,
+            35
+        ]
+    ]
+)
+
+# 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)
+
+