Create tb_audio_processor.py

modules/tb_audio_processor.py

@@ -0,0 +1,40 @@
+import librosa
+import numpy as np
+import logging
+from tensorflow.keras.models import load_model
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TBAudioProcessor:
+    """Processes real cough audio for TB detection"""
+
+    def __init__(self, model_path="tb_cough_model.h5"):
+        try:
+            self.model = load_model(model_path)
+            logger.info("TB Audio Processor Model Loaded Successfully.")
+        except Exception as e:
+            logger.error(f"Failed to load TB Audio Model: {e}")
+            self.model = None
+
+    def process_audio(self, audio_path):
+        """Analyze cough audio and return TB risk assessment."""
+        if not self.model:
+            return {"error": "Model not loaded. Cannot process audio."}
+
+        try:
+            y, sr = librosa.load(audio_path, sr=16000)
+            mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)
+            mfccs = np.mean(mfccs.T, axis=0).reshape(1, -1)  # Flatten MFCCs
+
+            prediction = self.model.predict(mfccs)
+            confidence = float(prediction[0][0])
+            result = "TB Detected" if confidence > 0.5 else "No TB"
+            
+            return {
+                "result": result,
+                "confidence": confidence
+            }
+        except Exception as e:
+            logger.error(f"Error processing audio: {e}")
+            return {"error": "Audio processing failed."}