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@@ -29,6 +29,8 @@ ENV PORT=7860
 ENV HF_HOME=/app/.cache
 ENV HF_DATASETS_CACHE=/app/.cache
 ENV TRANSFORMERS_CACHE=/app/.cache
+ENV NUMBA_CACHE_DIR=/app/.cache/numba
+ENV NUMBA_DISABLE_JIT=1
 
 # Run the FastAPI application
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] 

app.py

@@ -1,531 +1,535 @@
-#!/usr/bin/env python3
-"""
-Madverse Music API
-AI Music Detection Service
-"""
-
-from fastapi import FastAPI, HTTPException, BackgroundTasks, Header, Depends
-from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
-from pydantic import BaseModel, HttpUrl
-import torch
-import librosa
-import tempfile
-import os
-import requests
-from pathlib import Path
-import time
-from typing import Optional, Annotated, List, Union
-import uvicorn
-import asyncio
-from contextlib import asynccontextmanager
-import socket
-
-# Global model variable
-model = None
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    """Application lifespan management"""
-    # Startup
-    global model
-    try:
-        from sonics import HFAudioClassifier
-        print("🔄 Loading Madverse Music AI model...")
-        
-        # Set cache directory to a writable location
-        cache_dir = "/app/.cache" if os.path.exists("/app") else "./cache"
-        os.makedirs(cache_dir, exist_ok=True)
-        
-        # Load model with explicit cache directory
-        model = HFAudioClassifier.from_pretrained(
-            "awsaf49/sonics-spectttra-alpha-120s",
-            cache_dir=cache_dir
-        )
-        model.eval()
-        print("✅ Model loaded successfully!")
-    except Exception as e:
-        print(f"❌ Failed to load model: {e}")
-        import traceback
-        traceback.print_exc()
-        raise
-    
-    yield
-    
-    # Shutdown
-    print("🔄 Shutting down...")
-
-# Initialize FastAPI app with lifespan
-app = FastAPI(
-    title="Madverse Music API",
-    description="AI-powered music detection API to identify AI-generated vs human-created music",
-    version="1.0.0",
-    docs_url="/",
-    redoc_url="/docs",
-    lifespan=lifespan
-)
-
-# API Key Configuration
-API_KEY = os.getenv("MADVERSE_API_KEY", "madverse-music-api-key-2024")  # Default key for demo
-
-async def verify_api_key(x_api_key: Annotated[Union[str, None], Header()] = None):
-    """Verify API key from header"""
-    if x_api_key is None:
-        raise HTTPException(
-            status_code=401,
-            detail="Missing API key. Please provide a valid X-API-Key header."
-        )
-    if x_api_key != API_KEY:
-        raise HTTPException(
-            status_code=401,
-            detail="Invalid API key. Please provide a valid X-API-Key header."
-        )
-    return x_api_key
-
-class MusicAnalysisRequest(BaseModel):
-    urls: List[HttpUrl]
-
-def check_api_key_first(request: MusicAnalysisRequest, x_api_key: Annotated[Union[str, None], Header()] = None):
-    """Check API key before processing request"""
-    if x_api_key is None:
-        raise HTTPException(
-            status_code=401,
-            detail="Missing API key. Please provide a valid X-API-Key header."
-        )
-    if x_api_key != API_KEY:
-        raise HTTPException(
-            status_code=401,
-            detail="Invalid API key. Please provide a valid X-API-Key header."
-        )
-    return request
-    
-class FileAnalysisResult(BaseModel):
-    url: str
-    success: bool
-    classification: Optional[str] = None  # "Real" or "Fake"
-    confidence: Optional[float] = None    # 0.0 to 1.0
-    probability: Optional[float] = None   # Raw sigmoid probability
-    raw_score: Optional[float] = None     # Raw model output
-    duration: Optional[float] = None      # Audio duration in seconds
-    message: str
-    processing_time: Optional[float] = None
-    error: Optional[str] = None
-
-class MusicAnalysisResponse(BaseModel):
-    success: bool
-    total_files: int
-    successful_analyses: int
-    failed_analyses: int
-    results: List[FileAnalysisResult]
-    total_processing_time: float
-    message: str
-
-class ErrorResponse(BaseModel):
-    success: bool
-    error: str
-    message: str
-
-def cleanup_file(file_path: str):
-    """Background task to cleanup temporary files"""
-    try:
-        if os.path.exists(file_path):
-            os.unlink(file_path)
-    except:
-        pass
-
-def download_audio(url: str, max_size_mb: int = 100) -> str:
-    """Download audio file from URL with size validation"""
-    try:
-        print(f"🔽 Downloading audio from: {url}")
-        
-        # Check if URL is accessible
-        response = requests.head(str(url), timeout=10)
-        print(f"📊 Head response status: {response.status_code}")
-        
-        # Check content size
-        content_length = response.headers.get('Content-Length')
-        if content_length:
-            size_mb = int(content_length) / (1024 * 1024)
-            print(f"📏 File size: {size_mb:.2f}MB")
-            if int(content_length) > max_size_mb * 1024 * 1024:
-                raise HTTPException(
-                    status_code=413, 
-                    detail=f"File too large. Maximum size: {max_size_mb}MB"
-                )
-        
-        # Download file
-        print("🔽 Starting download...")
-        response = requests.get(str(url), timeout=30, stream=True)
-        response.raise_for_status()
-        print(f"✅ Download response status: {response.status_code}")
-        
-        # Create temporary file
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
-            downloaded_size = 0
-            for chunk in response.iter_content(chunk_size=8192):
-                downloaded_size += len(chunk)
-                if downloaded_size > max_size_mb * 1024 * 1024:
-                    os.unlink(tmp_file.name)
-                    raise HTTPException(
-                        status_code=413,
-                        detail=f"File too large. Maximum size: {max_size_mb}MB"
-                    )
-                tmp_file.write(chunk)
-            
-            print(f"💾 Downloaded {downloaded_size} bytes to {tmp_file.name}")
-            return tmp_file.name
-            
-    except requests.exceptions.RequestException as e:
-        error_msg = f"Failed to download audio: {str(e)}"
-        print(f"❌ Download error: {error_msg}")
-        raise HTTPException(
-            status_code=400,
-            detail=error_msg
-        )
-    except Exception as e:
-        error_msg = f"Error downloading file: {str(e)}"
-        print(f"❌ Unexpected download error: {error_msg}")
-        raise HTTPException(
-            status_code=500,
-            detail=error_msg
-        )
-
-def classify_audio(file_path: str) -> dict:
-    """Classify audio file using the AI model"""
-    try:
-        print(f"🎵 Loading audio file: {file_path}")
-        
-        # Check if file exists
-        if not os.path.exists(file_path):
-            raise ValueError(f"Audio file not found: {file_path}")
-        
-        # Check file size
-        file_size = os.path.getsize(file_path)
-        print(f"📏 Audio file size: {file_size} bytes")
-        
-        if file_size == 0:
-            raise ValueError("Audio file is empty")
-        
-        # Load audio (model uses 16kHz sample rate)
-        print("🔊 Loading audio with librosa...")
-        audio, sr = librosa.load(file_path, sr=16000)
-        print(f"🎼 Audio loaded: {len(audio)} samples at {sr}Hz, duration: {len(audio)/sr:.2f}s")
-        
-        if len(audio) == 0:
-            raise ValueError("Audio file contains no audio data")
-        
-        # Convert to tensor and add batch dimension
-        print("🧮 Converting to tensor...")
-        audio_tensor = torch.FloatTensor(audio).unsqueeze(0)
-        print(f"📊 Tensor shape: {audio_tensor.shape}")
-        
-        # Get prediction
-        print("🤖 Running model inference...")
-        with torch.no_grad():
-            output = model(audio_tensor)
-            print(f"📈 Model output: {output}")
-            
-            # Convert logit to probability using sigmoid
-            prob = torch.sigmoid(output).item()
-            print(f"📊 Sigmoid probability: {prob}")
-            
-            # Classify: prob < 0.5 = Real, prob >= 0.5 = Fake
-            if prob < 0.5:
-                classification = "Real"
-                confidence = (1 - prob) * 2  # Convert to 0-1 scale
-            else:
-                classification = "Fake"
-                confidence = (prob - 0.5) * 2  # Convert to 0-1 scale
-        
-        result = {
-            "classification": classification,
-            "confidence": min(confidence, 1.0),  # Cap at 1.0
-            "probability": prob,
-            "raw_score": output.item(),
-            "duration": len(audio) / sr
-        }
-        print(f"✅ Classification result: {result}")
-        return result
-        
-    except Exception as e:
-        error_msg = f"Error analyzing audio: {str(e)}"
-        print(f"❌ Audio analysis error: {error_msg}")
-        import traceback
-        print(f"🔍 Traceback: {traceback.format_exc()}")
-        raise HTTPException(
-            status_code=500,
-            detail=error_msg
-        )
-
-async def process_single_url(url: str) -> FileAnalysisResult:
-    """Process a single URL and return result"""
-    start_time = time.time()
-    temp_file = None
-    
-    try:
-        print(f"🚀 Processing URL: {url}")
-        
-        # Download audio file
-        temp_file = download_audio(url)
-        print(f"✅ Download completed: {temp_file}")
-        
-        # Classify audio
-        result = classify_audio(temp_file)
-        print(f"✅ Classification completed: {result}")
-        
-        # Calculate processing time
-        processing_time = time.time() - start_time
-        
-        # Prepare response
-        emoji = "🎤" if result["classification"] == "Real" else "🤖"
-        message = f'{emoji} Detected as {result["classification"].lower()} music'
-        
-        return FileAnalysisResult(
-            url=str(url),
-            success=True,
-            classification=result["classification"],
-            confidence=result["confidence"],
-            probability=result["probability"],
-            raw_score=result["raw_score"],
-            duration=result["duration"],
-            message=message,
-            processing_time=processing_time
-        )
-        
-    except Exception as e:
-        processing_time = time.time() - start_time
-        error_msg = str(e)
-        
-        print(f"❌ Processing failed for {url}: {error_msg}")
-        import traceback
-        print(f"🔍 Full traceback: {traceback.format_exc()}")
-        
-        return FileAnalysisResult(
-            url=str(url),
-            success=False,
-            message=f"❌ Failed to process: {error_msg}",
-            processing_time=processing_time,
-            error=error_msg
-        )
-    finally:
-        # Cleanup file in background
-        if temp_file:
-            try:
-                print(f"🧹 Cleaning up temporary file: {temp_file}")
-                os.unlink(temp_file)
-            except Exception as cleanup_error:
-                print(f"⚠️ Failed to cleanup {temp_file}: {cleanup_error}")
-
-@app.post("/analyze", response_model=MusicAnalysisResponse)
-async def analyze_music(
-    request: MusicAnalysisRequest = Depends(check_api_key_first)
-):
-    """
-    Analyze music from URL(s) to detect if it's AI-generated or human-created
-    
-    - **urls**: Array of direct URLs to audio files (MP3, WAV, FLAC, M4A, OGG)
-    - Returns classification results for each file
-    - Processes files concurrently for better performance when multiple URLs provided
-    """
-    start_time = time.time()
-    
-    if not model:
-        raise HTTPException(
-            status_code=503,
-            detail="Model not loaded. Please try again later."
-        )
-    
-    if len(request.urls) > 50:  # Limit processing
-        raise HTTPException(
-            status_code=400,
-            detail="Too many URLs. Maximum 50 files per request."
-        )
-    
-    if len(request.urls) == 0:
-        raise HTTPException(
-            status_code=400,
-            detail="At least one URL is required."
-        )
-    
-    try:
-        # Process all URLs concurrently with limited concurrency
-        semaphore = asyncio.Semaphore(5)  # Limit to 5 concurrent downloads
-        
-        async def process_with_semaphore(url):
-            async with semaphore:
-                return await process_single_url(str(url))
-        
-        # Create tasks for all URLs
-        tasks = [process_with_semaphore(url) for url in request.urls]
-        
-        # Wait for all tasks to complete
-        results = await asyncio.gather(*tasks, return_exceptions=True)
-        
-        # Process results and handle any exceptions
-        processed_results = []
-        successful_count = 0
-        failed_count = 0
-        
-        for i, result in enumerate(results):
-            if isinstance(result, Exception):
-                # Handle exception case
-                processed_results.append(FileAnalysisResult(
-                    url=str(request.urls[i]),
-                    success=False,
-                    message=f"❌ Processing failed: {str(result)}",
-                    error=str(result)
-                ))
-                failed_count += 1
-            else:
-                processed_results.append(result)
-                if result.success:
-                    successful_count += 1
-                else:
-                    failed_count += 1
-        
-        # Calculate total processing time
-        total_processing_time = time.time() - start_time
-        
-        # Prepare summary message
-        total_files = len(request.urls)
-        if total_files == 1:
-            # Single file message
-            if successful_count == 1:
-                message = processed_results[0].message
-            else:
-                message = processed_results[0].message
-        else:
-            # Multiple files message
-            if successful_count == total_files:
-                message = f"✅ Successfully analyzed all {total_files} files"
-            elif successful_count > 0:
-                message = f"⚠️ Analyzed {successful_count}/{total_files} files successfully"
-            else:
-                message = f"❌ Failed to analyze any files"
-        
-        return MusicAnalysisResponse(
-            success=successful_count > 0,
-            total_files=total_files,
-            successful_analyses=successful_count,
-            failed_analyses=failed_count,
-            results=processed_results,
-            total_processing_time=total_processing_time,
-            message=message
-        )
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Internal server error during processing: {str(e)}"
-        )
-
-@app.get("/health")
-async def health_check():
-    """Health check endpoint"""
-    return {
-        "status": "healthy",
-        "model_loaded": model is not None,
-        "service": "Madverse Music API"
-    }
-
-@app.get("/info")
-async def get_info():
-    """Get API information"""
-    return {
-        "name": "Madverse Music API",
-        "version": "1.0.0",
-        "description": "AI-powered music detection to identify AI-generated vs human-created music",
-        "model": "SpecTTTra-α (120s)",
-        "accuracy": {
-            "f1_score": 0.97,
-            "sensitivity": 0.96,
-            "specificity": 0.99
-        },
-        "supported_formats": ["MP3", "WAV", "FLAC", "M4A", "OGG"],
-        "max_file_size": "100MB",
-        "max_duration": "120 seconds",
-        "authentication": {
-            "required": True,
-            "type": "API Key",
-            "header": "X-API-Key",
-            "example": "X-API-Key: your-api-key-here"
-        },
-        "usage": {
-            "curl_example": "curl -X POST 'http://localhost:8000/analyze' -H 'X-API-Key: your-api-key' -H 'Content-Type: application/json' -d '{\"url\":\"https://example.com/song.mp3\"}'"
-        }
-    }
-
-def find_available_port(start_port: int = 8000, max_attempts: int = 10) -> int:
-    """Find an available port starting from start_port"""
-    import random
-    import time
-    
-    # Add some randomization to avoid race conditions
-    time.sleep(random.uniform(0.1, 0.5))
-    
-    for port in range(start_port, start_port + max_attempts):
-        try:
-            # Try to bind to the port with proper error handling
-            with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-                s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-                s.bind(('0.0.0.0', port))
-                s.listen(1)
-                print(f"✅ Port {port} is available")
-                return port
-        except OSError as e:
-            print(f"❌ Port {port} is busy: {e}")
-            continue
-    
-    # If no port found, raise an exception
-    raise RuntimeError(f"No available port found in range {start_port}-{start_port + max_attempts - 1}")
-
-if __name__ == "__main__":
-    try:
-        # Check if we're in a Hugging Face environment
-        is_hf_space = os.getenv('SPACE_ID') is not None
-        hf_port = os.getenv('PORT')  # HF Spaces sets this
-        
-        if is_hf_space and hf_port:
-            # Use HF Spaces assigned port
-            port = int(hf_port)
-            print(f"🤗 Running in Hugging Face Spaces on port {port}")
-        elif is_hf_space:
-            print("🤗 Running in Hugging Face Spaces environment")
-            # Use standard HF Spaces port
-            port = 7860
-        else:
-            # Find an available port for local development
-            port = find_available_port(8000, 10)
-            
-        print(f"🚀 Starting server on port {port}")
-        
-        # For HF Spaces, don't use the retry logic as it might cause issues
-        if is_hf_space:
-            uvicorn.run(app, host="0.0.0.0", port=port)
-        else:
-            # Add retry logic for local development
-            max_retries = 3
-            for attempt in range(max_retries):
-                try:
-                    uvicorn.run(app, host="0.0.0.0", port=port)
-                    break  # If successful, break out of retry loop
-                except OSError as e:
-                    if "Address already in use" in str(e) and attempt < max_retries - 1:
-                        print(f"⚠️ Port {port} became busy, trying next port...")
-                        port = find_available_port(port + 1, 10)
-                        print(f"🔄 Retrying on port {port}")
-                    else:
-                        raise
-                    
-    except RuntimeError as e:
-        print(f"❌ {e}")
-        print("💡 Suggestions:")
-        print("   1. Wait a moment and try again (another instance might be shutting down)")
-        print("   2. Manually specify a different port:")
-        print("      uvicorn app:app --host 0.0.0.0 --port 8001")
-        print("   3. Check for running processes: ps aux | grep python")
-    except KeyboardInterrupt:
-        print("\n🛑 Server stopped by user")
-    except Exception as e:
-        print(f"❌ Failed to start server: {e}")
-        import traceback
+#!/usr/bin/env python3
+"""
+Madverse Music API
+AI Music Detection Service
+"""
+
+# Configure numba before any other imports to avoid caching issues
+import os
+os.environ.setdefault('NUMBA_DISABLE_JIT', '1')
+os.environ.setdefault('NUMBA_CACHE_DIR', '/app/.cache/numba')
+
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Header, Depends
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from pydantic import BaseModel, HttpUrl
+import torch
+import librosa
+import tempfile
+import requests
+from pathlib import Path
+import time
+from typing import Optional, Annotated, List, Union
+import uvicorn
+import asyncio
+from contextlib import asynccontextmanager
+import socket
+
+# Global model variable
+model = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan management"""
+    # Startup
+    global model
+    try:
+        from sonics import HFAudioClassifier
+        print("🔄 Loading Madverse Music AI model...")
+        
+        # Set cache directory to a writable location
+        cache_dir = "/app/.cache" if os.path.exists("/app") else "./cache"
+        os.makedirs(cache_dir, exist_ok=True)
+        
+        # Load model with explicit cache directory
+        model = HFAudioClassifier.from_pretrained(
+            "awsaf49/sonics-spectttra-alpha-120s",
+            cache_dir=cache_dir
+        )
+        model.eval()
+        print("✅ Model loaded successfully!")
+    except Exception as e:
+        print(f"❌ Failed to load model: {e}")
+        import traceback
+        traceback.print_exc()
+        raise
+    
+    yield
+    
+    # Shutdown
+    print("🔄 Shutting down...")
+
+# Initialize FastAPI app with lifespan
+app = FastAPI(
+    title="Madverse Music API",
+    description="AI-powered music detection API to identify AI-generated vs human-created music",
+    version="1.0.0",
+    docs_url="/",
+    redoc_url="/docs",
+    lifespan=lifespan
+)
+
+# API Key Configuration
+API_KEY = os.getenv("MADVERSE_API_KEY", "madverse-music-api-key-2024")  # Default key for demo
+
+async def verify_api_key(x_api_key: Annotated[Union[str, None], Header()] = None):
+    """Verify API key from header"""
+    if x_api_key is None:
+        raise HTTPException(
+            status_code=401,
+            detail="Missing API key. Please provide a valid X-API-Key header."
+        )
+    if x_api_key != API_KEY:
+        raise HTTPException(
+            status_code=401,
+            detail="Invalid API key. Please provide a valid X-API-Key header."
+        )
+    return x_api_key
+
+class MusicAnalysisRequest(BaseModel):
+    urls: List[HttpUrl]
+
+def check_api_key_first(request: MusicAnalysisRequest, x_api_key: Annotated[Union[str, None], Header()] = None):
+    """Check API key before processing request"""
+    if x_api_key is None:
+        raise HTTPException(
+            status_code=401,
+            detail="Missing API key. Please provide a valid X-API-Key header."
+        )
+    if x_api_key != API_KEY:
+        raise HTTPException(
+            status_code=401,
+            detail="Invalid API key. Please provide a valid X-API-Key header."
+        )
+    return request
+    
+class FileAnalysisResult(BaseModel):
+    url: str
+    success: bool
+    classification: Optional[str] = None  # "Real" or "Fake"
+    confidence: Optional[float] = None    # 0.0 to 1.0
+    probability: Optional[float] = None   # Raw sigmoid probability
+    raw_score: Optional[float] = None     # Raw model output
+    duration: Optional[float] = None      # Audio duration in seconds
+    message: str
+    processing_time: Optional[float] = None
+    error: Optional[str] = None
+
+class MusicAnalysisResponse(BaseModel):
+    success: bool
+    total_files: int
+    successful_analyses: int
+    failed_analyses: int
+    results: List[FileAnalysisResult]
+    total_processing_time: float
+    message: str
+
+class ErrorResponse(BaseModel):
+    success: bool
+    error: str
+    message: str
+
+def cleanup_file(file_path: str):
+    """Background task to cleanup temporary files"""
+    try:
+        if os.path.exists(file_path):
+            os.unlink(file_path)
+    except:
+        pass
+
+def download_audio(url: str, max_size_mb: int = 100) -> str:
+    """Download audio file from URL with size validation"""
+    try:
+        print(f"🔽 Downloading audio from: {url}")
+        
+        # Check if URL is accessible
+        response = requests.head(str(url), timeout=10)
+        print(f"📊 Head response status: {response.status_code}")
+        
+        # Check content size
+        content_length = response.headers.get('Content-Length')
+        if content_length:
+            size_mb = int(content_length) / (1024 * 1024)
+            print(f"📏 File size: {size_mb:.2f}MB")
+            if int(content_length) > max_size_mb * 1024 * 1024:
+                raise HTTPException(
+                    status_code=413, 
+                    detail=f"File too large. Maximum size: {max_size_mb}MB"
+                )
+        
+        # Download file
+        print("🔽 Starting download...")
+        response = requests.get(str(url), timeout=30, stream=True)
+        response.raise_for_status()
+        print(f"✅ Download response status: {response.status_code}")
+        
+        # Create temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
+            downloaded_size = 0
+            for chunk in response.iter_content(chunk_size=8192):
+                downloaded_size += len(chunk)
+                if downloaded_size > max_size_mb * 1024 * 1024:
+                    os.unlink(tmp_file.name)
+                    raise HTTPException(
+                        status_code=413,
+                        detail=f"File too large. Maximum size: {max_size_mb}MB"
+                    )
+                tmp_file.write(chunk)
+            
+            print(f"💾 Downloaded {downloaded_size} bytes to {tmp_file.name}")
+            return tmp_file.name
+            
+    except requests.exceptions.RequestException as e:
+        error_msg = f"Failed to download audio: {str(e)}"
+        print(f"❌ Download error: {error_msg}")
+        raise HTTPException(
+            status_code=400,
+            detail=error_msg
+        )
+    except Exception as e:
+        error_msg = f"Error downloading file: {str(e)}"
+        print(f"❌ Unexpected download error: {error_msg}")
+        raise HTTPException(
+            status_code=500,
+            detail=error_msg
+        )
+
+def classify_audio(file_path: str) -> dict:
+    """Classify audio file using the AI model"""
+    try:
+        print(f"🎵 Loading audio file: {file_path}")
+        
+        # Check if file exists
+        if not os.path.exists(file_path):
+            raise ValueError(f"Audio file not found: {file_path}")
+        
+        # Check file size
+        file_size = os.path.getsize(file_path)
+        print(f"📏 Audio file size: {file_size} bytes")
+        
+        if file_size == 0:
+            raise ValueError("Audio file is empty")
+        
+        # Load audio (model uses 16kHz sample rate)
+        print("🔊 Loading audio with librosa...")
+        audio, sr = librosa.load(file_path, sr=16000)
+        print(f"🎼 Audio loaded: {len(audio)} samples at {sr}Hz, duration: {len(audio)/sr:.2f}s")
+        
+        if len(audio) == 0:
+            raise ValueError("Audio file contains no audio data")
+        
+        # Convert to tensor and add batch dimension
+        print("🧮 Converting to tensor...")
+        audio_tensor = torch.FloatTensor(audio).unsqueeze(0)
+        print(f"📊 Tensor shape: {audio_tensor.shape}")
+        
+        # Get prediction
+        print("🤖 Running model inference...")
+        with torch.no_grad():
+            output = model(audio_tensor)
+            print(f"📈 Model output: {output}")
+            
+            # Convert logit to probability using sigmoid
+            prob = torch.sigmoid(output).item()
+            print(f"📊 Sigmoid probability: {prob}")
+            
+            # Classify: prob < 0.5 = Real, prob >= 0.5 = Fake
+            if prob < 0.5:
+                classification = "Real"
+                confidence = (1 - prob) * 2  # Convert to 0-1 scale
+            else:
+                classification = "Fake"
+                confidence = (prob - 0.5) * 2  # Convert to 0-1 scale
+        
+        result = {
+            "classification": classification,
+            "confidence": min(confidence, 1.0),  # Cap at 1.0
+            "probability": prob,
+            "raw_score": output.item(),
+            "duration": len(audio) / sr
+        }
+        print(f"✅ Classification result: {result}")
+        return result
+        
+    except Exception as e:
+        error_msg = f"Error analyzing audio: {str(e)}"
+        print(f"❌ Audio analysis error: {error_msg}")
+        import traceback
+        print(f"🔍 Traceback: {traceback.format_exc()}")
+        raise HTTPException(
+            status_code=500,
+            detail=error_msg
+        )
+
+async def process_single_url(url: str) -> FileAnalysisResult:
+    """Process a single URL and return result"""
+    start_time = time.time()
+    temp_file = None
+    
+    try:
+        print(f"🚀 Processing URL: {url}")
+        
+        # Download audio file
+        temp_file = download_audio(url)
+        print(f"✅ Download completed: {temp_file}")
+        
+        # Classify audio
+        result = classify_audio(temp_file)
+        print(f"✅ Classification completed: {result}")
+        
+        # Calculate processing time
+        processing_time = time.time() - start_time
+        
+        # Prepare response
+        emoji = "🎤" if result["classification"] == "Real" else "🤖"
+        message = f'{emoji} Detected as {result["classification"].lower()} music'
+        
+        return FileAnalysisResult(
+            url=str(url),
+            success=True,
+            classification=result["classification"],
+            confidence=result["confidence"],
+            probability=result["probability"],
+            raw_score=result["raw_score"],
+            duration=result["duration"],
+            message=message,
+            processing_time=processing_time
+        )
+        
+    except Exception as e:
+        processing_time = time.time() - start_time
+        error_msg = str(e)
+        
+        print(f"❌ Processing failed for {url}: {error_msg}")
+        import traceback
+        print(f"🔍 Full traceback: {traceback.format_exc()}")
+        
+        return FileAnalysisResult(
+            url=str(url),
+            success=False,
+            message=f"❌ Failed to process: {error_msg}",
+            processing_time=processing_time,
+            error=error_msg
+        )
+    finally:
+        # Cleanup file in background
+        if temp_file:
+            try:
+                print(f"🧹 Cleaning up temporary file: {temp_file}")
+                os.unlink(temp_file)
+            except Exception as cleanup_error:
+                print(f"⚠️ Failed to cleanup {temp_file}: {cleanup_error}")
+
+@app.post("/analyze", response_model=MusicAnalysisResponse)
+async def analyze_music(
+    request: MusicAnalysisRequest = Depends(check_api_key_first)
+):
+    """
+    Analyze music from URL(s) to detect if it's AI-generated or human-created
+    
+    - **urls**: Array of direct URLs to audio files (MP3, WAV, FLAC, M4A, OGG)
+    - Returns classification results for each file
+    - Processes files concurrently for better performance when multiple URLs provided
+    """
+    start_time = time.time()
+    
+    if not model:
+        raise HTTPException(
+            status_code=503,
+            detail="Model not loaded. Please try again later."
+        )
+    
+    if len(request.urls) > 50:  # Limit processing
+        raise HTTPException(
+            status_code=400,
+            detail="Too many URLs. Maximum 50 files per request."
+        )
+    
+    if len(request.urls) == 0:
+        raise HTTPException(
+            status_code=400,
+            detail="At least one URL is required."
+        )
+    
+    try:
+        # Process all URLs concurrently with limited concurrency
+        semaphore = asyncio.Semaphore(5)  # Limit to 5 concurrent downloads
+        
+        async def process_with_semaphore(url):
+            async with semaphore:
+                return await process_single_url(str(url))
+        
+        # Create tasks for all URLs
+        tasks = [process_with_semaphore(url) for url in request.urls]
+        
+        # Wait for all tasks to complete
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        
+        # Process results and handle any exceptions
+        processed_results = []
+        successful_count = 0
+        failed_count = 0
+        
+        for i, result in enumerate(results):
+            if isinstance(result, Exception):
+                # Handle exception case
+                processed_results.append(FileAnalysisResult(
+                    url=str(request.urls[i]),
+                    success=False,
+                    message=f"❌ Processing failed: {str(result)}",
+                    error=str(result)
+                ))
+                failed_count += 1
+            else:
+                processed_results.append(result)
+                if result.success:
+                    successful_count += 1
+                else:
+                    failed_count += 1
+        
+        # Calculate total processing time
+        total_processing_time = time.time() - start_time
+        
+        # Prepare summary message
+        total_files = len(request.urls)
+        if total_files == 1:
+            # Single file message
+            if successful_count == 1:
+                message = processed_results[0].message
+            else:
+                message = processed_results[0].message
+        else:
+            # Multiple files message
+            if successful_count == total_files:
+                message = f"✅ Successfully analyzed all {total_files} files"
+            elif successful_count > 0:
+                message = f"⚠️ Analyzed {successful_count}/{total_files} files successfully"
+            else:
+                message = f"❌ Failed to analyze any files"
+        
+        return MusicAnalysisResponse(
+            success=successful_count > 0,
+            total_files=total_files,
+            successful_analyses=successful_count,
+            failed_analyses=failed_count,
+            results=processed_results,
+            total_processing_time=total_processing_time,
+            message=message
+        )
+        
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Internal server error during processing: {str(e)}"
+        )
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None,
+        "service": "Madverse Music API"
+    }
+
+@app.get("/info")
+async def get_info():
+    """Get API information"""
+    return {
+        "name": "Madverse Music API",
+        "version": "1.0.0",
+        "description": "AI-powered music detection to identify AI-generated vs human-created music",
+        "model": "SpecTTTra-α (120s)",
+        "accuracy": {
+            "f1_score": 0.97,
+            "sensitivity": 0.96,
+            "specificity": 0.99
+        },
+        "supported_formats": ["MP3", "WAV", "FLAC", "M4A", "OGG"],
+        "max_file_size": "100MB",
+        "max_duration": "120 seconds",
+        "authentication": {
+            "required": True,
+            "type": "API Key",
+            "header": "X-API-Key",
+            "example": "X-API-Key: your-api-key-here"
+        },
+        "usage": {
+            "curl_example": "curl -X POST 'http://localhost:8000/analyze' -H 'X-API-Key: your-api-key' -H 'Content-Type: application/json' -d '{\"url\":\"https://example.com/song.mp3\"}'"
+        }
+    }
+
+def find_available_port(start_port: int = 8000, max_attempts: int = 10) -> int:
+    """Find an available port starting from start_port"""
+    import random
+    import time
+    
+    # Add some randomization to avoid race conditions
+    time.sleep(random.uniform(0.1, 0.5))
+    
+    for port in range(start_port, start_port + max_attempts):
+        try:
+            # Try to bind to the port with proper error handling
+            with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+                s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+                s.bind(('0.0.0.0', port))
+                s.listen(1)
+                print(f"✅ Port {port} is available")
+                return port
+        except OSError as e:
+            print(f"❌ Port {port} is busy: {e}")
+            continue
+    
+    # If no port found, raise an exception
+    raise RuntimeError(f"No available port found in range {start_port}-{start_port + max_attempts - 1}")
+
+if __name__ == "__main__":
+    try:
+        # Check if we're in a Hugging Face environment
+        is_hf_space = os.getenv('SPACE_ID') is not None
+        hf_port = os.getenv('PORT')  # HF Spaces sets this
+        
+        if is_hf_space and hf_port:
+            # Use HF Spaces assigned port
+            port = int(hf_port)
+            print(f"🤗 Running in Hugging Face Spaces on port {port}")
+        elif is_hf_space:
+            print("🤗 Running in Hugging Face Spaces environment")
+            # Use standard HF Spaces port
+            port = 7860
+        else:
+            # Find an available port for local development
+            port = find_available_port(8000, 10)
+            
+        print(f"🚀 Starting server on port {port}")
+        
+        # For HF Spaces, don't use the retry logic as it might cause issues
+        if is_hf_space:
+            uvicorn.run(app, host="0.0.0.0", port=port)
+        else:
+            # Add retry logic for local development
+            max_retries = 3
+            for attempt in range(max_retries):
+                try:
+                    uvicorn.run(app, host="0.0.0.0", port=port)
+                    break  # If successful, break out of retry loop
+                except OSError as e:
+                    if "Address already in use" in str(e) and attempt < max_retries - 1:
+                        print(f"⚠️ Port {port} became busy, trying next port...")
+                        port = find_available_port(port + 1, 10)
+                        print(f"🔄 Retrying on port {port}")
+                    else:
+                        raise
+                    
+    except RuntimeError as e:
+        print(f"❌ {e}")
+        print("💡 Suggestions:")
+        print("   1. Wait a moment and try again (another instance might be shutting down)")
+        print("   2. Manually specify a different port:")
+        print("      uvicorn app:app --host 0.0.0.0 --port 8001")
+        print("   3. Check for running processes: ps aux | grep python")
+    except KeyboardInterrupt:
+        print("\n🛑 Server stopped by user")
+    except Exception as e:
+        print(f"❌ Failed to start server: {e}")
+        import traceback
         traceback.print_exc()