Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 import librosa
 import numpy as np
 import torch
-from transformers import WhisperProcessor, WhisperForConditionalGeneration, pipeline
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
 from simple_salesforce import Salesforce
 import os
 from datetime import datetime
 import logging
-import soundfile
+from scipy.io import wavfile
 import webrtcvad
 
 # Set up logging
@@ -101,7 +101,6 @@ def transcribe_audio(audio_file):
 
 def analyze_symptoms(text):
     """Mock symptom-to-disease analysis (placeholder for symptom-2-disease-net)."""
-    # Since abhirajeshbhai/symptom-2-disease-net is not locally available, use rule-based analysis
     text = text.lower()
     feedback = []
     if "cough" in text or "difficulty breathing" in text:
@@ -212,12 +211,14 @@ def test_with_sample_audio():
         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")
+        # Normalize to int16 for scipy.io.wavfile
+        dummy_audio = (dummy_audio * 32767).astype(np.int16)
         logger.info(f"Dummy audio shape: {dummy_audio.shape}, type: {type(dummy_audio)}, dtype: {dummy_audio.dtype}")
         sample_audio_path = "audio_samples/dummy_test.wav"
         os.makedirs("audio_samples", exist_ok=True)
         try:
-            # Test audio writing
-            soundfile.write(dummy_audio, sr, sample_audio_path)
+            # Write audio using scipy.io.wavfile
+            wavfile.write(sample_audio_path, sr, dummy_audio)
             logger.info(f"Generated dummy audio at: {sample_audio_path}")
             # Verify file exists
             if not os.path.exists(sample_audio_path):