Update app.py

app.py

@@ -103,23 +103,20 @@ def analyze_symptoms(text):
     feedback = []
     if "cough" in text or "difficulty breathing" in text:
         feedback.append("Symptoms like cough or difficulty breathing may indicate a respiratory condition, such as bronchitis or asthma. Consult a doctor.")
-    if "stressed" in text or "stress" in text or "fatigue" in text:
+    if "stressed" in text or "stress" in text or "tired" in text or "fatigue" in text:
         feedback.append("Reported stress or fatigue may suggest conditions like anxiety or chronic fatigue syndrome. Seek medical advice.")
     if not feedback:
         feedback.append("No specific conditions detected from reported symptoms.")
     return "\n".join(feedback)
 
-def analyze_voice(audio_file=None, audio_data=None):
+def analyze_voice(audio_file=None):
     """Analyze voice for health indicators."""
     try:
-        # Use provided audio file or in-memory audio data
+        # Load audio from file if provided
         if audio_file and os.path.exists(audio_file):
             audio, sr = librosa.load(audio_file, sr=16000)
-        elif audio_data is not None:
-            audio = audio_data
-            sr = 16000
         else:
-            raise ValueError("No audio input provided")
+            raise ValueError("No valid audio file provided for analysis")
 
         if len(audio) < sr:
             raise ValueError("Audio too short (minimum 1 second)")
@@ -161,7 +158,7 @@ def analyze_voice(audio_file=None, audio_data=None):
         feedback_str = "\n".join(feedback)
 
         # Store in Salesforce
-        if sf and audio_file:
+        if sf:
             store_in_salesforce(audio_file, feedback_str, respiratory_score, mental_health_score, features, transcription)
 
         return feedback_str
@@ -177,7 +174,7 @@ def store_in_salesforce(audio_file, feedback, respiratory_score, mental_health_s
             "Feedback__c": feedback,
             "RespiratoryScore__c": float(respiratory_score),
             "MentalHealthScore__c": float(mental_health_score),
-            "AudioFileName__c": os.path.basename(audio_file) if audio_file else "in_memory_audio",
+            "AudioFileName__c": os.path.basename(audio_file) if audio_file else "user_recorded_audio",
             "Pitch__c": float(features["pitch"]),
             "Jitter__c": float(features["jitter"]),
             "Shimmer__c": float(features["shimmer"]),
@@ -188,34 +185,15 @@ def store_in_salesforce(audio_file, feedback, respiratory_score, mental_health_s
     except Exception as e:
         logger.error(f"Salesforce storage failed: {str(e)}")
 
-def test_with_sample_audio():
-    """Test with dummy audio simulating a user's voice saying 'I have a cough and feel stressed'."""
-    logger.info("Starting test with in-memory audio simulation")
-    # Generate synthetic audio: 150 Hz base frequency with variations to mimic a stressed voice with cough
-    sr = 16000
-    t = np.linspace(0, 2, 2 * sr)
-    freq_mod = 150 + 25 * np.sin(2 * np.pi * 0.5 * t)  # Increased jitter for respiratory hint
-    amplitude_mod = 0.5 + 0.25 * np.sin(2 * np.pi * 0.3 * t)  # Increased shimmer for stress hint
-    noise = 0.05 * np.random.normal(0, 1, len(t))  # Moderate noise
-    dummy_audio = amplitude_mod * np.sin(2 * np.pi * freq_mod * t) + noise
-    # Ensure dummy_audio is a 1D NumPy array
-    dummy_audio = np.asarray(dummy_audio, dtype=np.float32).flatten()
-    if not isinstance(dummy_audio, np.ndarray) or dummy_audio.ndim != 1:
-        logger.error(f"Invalid dummy_audio: type={type(dummy_audio)}, shape={dummy_audio.shape if hasattr(dummy_audio, 'shape') else 'N/A'}")
-        raise ValueError("Generated audio is not a 1D NumPy array")
-    logger.info(f"Dummy audio shape: {dummy_audio.shape}, type: {type(dummy_audio)}, dtype: {dummy_audio.dtype}")
-    return analyze_voice(audio_data=dummy_audio)
-
 # Gradio interface
 iface = gr.Interface(
     fn=analyze_voice,
-    inputs=gr.Audio(type="filepath", label="Record/Upload Voice (WAV, MP3, FLAC, 1+ sec)"),
+    inputs=gr.Audio(type="filepath", label="Record or Upload Your Voice (WAV, MP3, FLAC, 1+ sec)", source="upload", format="wav"),
     outputs=gr.Textbox(label="Health Assessment Results"),
     title="Voice Health Analyzer",
-    description="Analyze your voice for preliminary health insights. Supports WAV, MP3, FLAC in multiple languages. Minimum 1 second."
+    description="Record or upload your voice (minimum 1 second) to receive preliminary health insights. Speak clearly in English about your symptoms."
 )
 
 if __name__ == "__main__":
-    logger.info("Starting Voice Health Analyzer at 10:31 AM IST, June 23, 2025")
-    print(test_with_sample_audio())
+    logger.info("Starting Voice Health Analyzer at 11:58 AM IST, June 23, 2025")
     iface.launch(server_name="0.0.0.0", server_port=7860)