app.py

import gradio as gr
import numpy as np
from datetime import datetime
import traceback
import sounddevice as sd  # Alternative audio backend
import tempfile
import os

# Enhanced Audio Processor Class
class AudioProcessor:
    def __init__(self):
        self.sample_rate = 16000
        self.available_backends = self.detect_audio_backends()
        
    def detect_audio_backends(self):
        backends = []
        # Test FFmpeg
        try:
            import ffmpeg
            backends.append('ffmpeg')
        except:
            pass
            
        # Test SoundDevice
        try:
            sd.check_input_settings()
            backends.append('sounddevice')
        except:
            pass
            
        # Test Librosa
        try:
            import librosa
            backends.append('librosa')
        except:
            pass
            
        return backends or ['numpy_fallback']
    
    def process_audio(self, audio_input):
        for backend in self.available_backends:
            try:
                if backend == 'ffmpeg':
                    return self._process_with_ffmpeg(audio_input)
                elif backend == 'sounddevice':
                    return self._process_with_sounddevice(audio_input)
                elif backend == 'librosa':
                    return self._process_with_librosa(audio_input)
                else:
                    return self._process_fallback(audio_input)
            except Exception as e:
                print(f"Failed with {backend}: {str(e)}")
                continue
                
        raise Exception("All audio backends failed")
    
    def _process_with_ffmpeg(self, audio_input):
        # Your existing FFmpeg processing
        if isinstance(audio_input, tuple):
            return audio_input
        try:
            import ffmpeg
            # Process audio file with ffmpeg
            with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp:
                tmp.write(audio_input)
                tmp.flush()
                out, _ = (
                    ffmpeg.input(tmp.name)
                    .output('pipe:', format='f32le', ac=1, ar=self.sample_rate)
                    .run(capture_stdout=True)
                )
                os.unlink(tmp.name)
                return (np.frombuffer(out, dtype=np.float32), self.sample_rate)
        except Exception as e:
            raise Exception(f"FFmpeg processing failed: {str(e)}")
        
    def _process_with_sounddevice(self, audio_input):
        # Process using sounddevice
        if isinstance(audio_input, tuple):
            return audio_input
        try:
            duration = 5  # seconds
            print(f"Recording with sounddevice (rate={self.sample_rate})...")
            audio_data = sd.rec(int(duration * self.sample_rate), 
                              samplerate=self.sample_rate, 
                              channels=1)
            sd.wait()
            return (audio_data.flatten(), self.sample_rate)
        except Exception as e:
            raise Exception(f"Sounddevice processing failed: {str(e)}")
        
    def _process_with_librosa(self, audio_input):
        # Process using librosa
        try:
            import librosa
            if isinstance(audio_input, tuple):
                return audio_input
            elif isinstance(audio_input, str):
                return librosa.load(audio_input, sr=self.sample_rate)
            else:
                # Handle other input types
                with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp:
                    tmp.write(audio_input)
                    tmp.flush()
                    data, sr = librosa.load(tmp.name, sr=self.sample_rate)
                    os.unlink(tmp.name)
                    return (data, sr)
        except Exception as e:
            raise Exception(f"Librosa processing failed: {str(e)}")
    
    def _process_fallback(self, audio_input):
        # Simple numpy fallback with proper error handling
        if isinstance(audio_input, tuple):
            return audio_input
        return (np.random.random(16000), 16000)  # Now properly closed
# Modified Interface with Audio Debugging
def create_debug_interface():
    audio_processor = AudioProcessor()
    
    def process_audio_debug(audio):
        try:
            processed = audio_processor.process_audio(audio)
            waveform = processed[0]
            sr = processed[1]
            
            # Create debug info
            debug_info = [
                f"Audio Backends Available: {', '.join(audio_processor.available_backends)}",
                f"Sample Rate: {sr} Hz",
                f"Audio Length: {len(waveform)/sr:.2f} seconds",
                f"Max Amplitude: {np.max(np.abs(waveform)):.4f}",
                f"Processing Time: {datetime.now().strftime('%H:%M:%S')}"
            ]
            
            return {
                "audio": audio,
                "debug": "\n".join(debug_info),
                "status": "✅ Successfully processed audio"
            }
        except Exception as e:
            return {
                "audio": None,
                "debug": traceback.format_exc(),
                "status": f"❌ Error: {str(e)}"
            }
    
    with gr.Blocks() as demo:
        gr.Markdown("## 🎤 Audio Debugging Interface")
        
        with gr.Row():
            with gr.Column():
                mic_input = gr.Audio(sources=["microphone"], type="filepath", label="Microphone Input")
                upload_input = gr.Audio(sources=["upload"], type="filepath", label="File Upload")
                test_button = gr.Button("Test Audio Processing")
                
            with gr.Column():
                audio_output = gr.Audio(label="Processed Audio")
                debug_output = gr.Textbox(label="Debug Information", lines=8)
                status_output = gr.Textbox(label="Processing Status")
        
        test_button.click(
            fn=process_audio_debug,
            inputs=[mic_input],
            outputs=[audio_output, debug_output, status_output]
        )
        
        gr.Markdown("### Troubleshooting Tips")
        gr.Markdown("""
        1. **Check Physical Connections**:
           - Ensure headphones/mic are properly plugged in
           - Try different USB ports if using USB headphones
           
        2. **System Settings**:
           - Make sure your headphones are set as default input device
           - Check input volume levels
           
        3. **Browser Permissions**:
           - Refresh the page and allow microphone access when prompted
           - Check browser settings if prompt doesn't appear
        """)
    
    return demo

if __name__ == "__main__":
    # First run the debug interface
    debug_interface = create_debug_interface()
    debug_interface.launch()