Delete voice_processor.py

voice_processor.py

@@ -1,58 +0,0 @@
-import logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-try:
-    from transformers import pipeline
-    import librosa
-    import numpy as np
-except ImportError as e:
-    logger.error(f"Failed to import required libraries: {str(e)}. Please run 'pip install -r requirements.txt' in the Space terminal.")
-    raise
-
-class VoiceProcessor:
-    def __init__(self, cipher):
-        self.cipher = cipher
-        try:
-            # Initialize Hugging Face speech recognition pipeline
-            self.asr_pipeline = pipeline(
-                "automatic-speech-recognition",
-                model="openai/whisper-tiny",
-                device=-1  # CPU for Hugging Face Spaces
-            )
-            logger.info("Initialized Whisper speech recognition pipeline")
-        except Exception as e:
-            logger.error(f"Error initializing ASR pipeline: {str(e)}. Ensure transformers and torch are installed.")
-            raise
-
-    def analyze_voice(self, audio_path):
-        try:
-            # Load and preprocess audio
-            audio, sr = librosa.load(audio_path, sr=16000)
-            
-            # Perform speech recognition
-            transcription = self.asr_pipeline(audio)["text"]
-            
-            # Placeholder health analysis (simulated)
-            health_indicators = self._simulate_health_analysis(audio, sr)
-            
-            # Encrypt sensitive data
-            encrypted_transcription = self.cipher.encrypt(transcription.encode()).decode()
-            
-            return {
-                "transcription": transcription,
-                "encrypted_transcription": encrypted_transcription,
-                "health_indicators": health_indicators,
-                "disclaimer": "This is not a medical diagnosis. Consult a doctor for professional advice."
-            }
-        except Exception as e:
-            logger.error(f"Error processing voice: {str(e)}")
-            return {"error": str(e)}
-
-    def _simulate_health_analysis(self, audio, sr):
-        # Placeholder: Simulate health indicator analysis
-        energy = np.mean(librosa.feature.rms(y=audio).flatten())
-        return {
-            "respiratory": "Normal" if energy > 0.01 else "Possible issue detected",
-            "mental_health": "Stable" if len(audio) / sr > 5 else "Possible stress detected"
-        }