Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import tensorflow as tf
 import librosa
 import numpy as np
+import tempfile
 
 # Diccionario de etiquetas
 labels = ['down', 'go', 'left', 'no', 'off', 'on', 'right', 'stop', 'up', 'yes']
@@ -13,19 +14,25 @@ def extract_features(file_name):
         mfccsscaled = np.mean(mfccs.T,axis=0)
         
     except Exception as e:
-        print("Error encountered while parsing file: ", file_name)
+        print(f"Error encountered while parsing file: {file_name}")
+        print(e)  # Imprime la excepción completa
         return None 
      
     return mfccsscaled
 
 def classify_audio(audio_file):
-    # Carga el modelo
-    model = tf.keras.models.load_model('my_model.h5') 
+    print(f"Tipo de audio_file: {type(audio_file)}") 
 
-    # Preprocesa el audio
-    features = extract_features(audio_file)
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file:
+        tmp_file.write(audio_file.read())
+        tmp_file_path = tmp_file.name
+
+    # Preprocesa el audio (con extract_features())
+    features = extract_features(tmp_file_path)
+
+    # Si features es None, hubo un error en extract_features
     if features is None:
-        return "Error al procesar el audio" # Manejo de error
+        return "Error al procesar el audio" 
     
     features = features.reshape(1, -1) # Redimensiona a (1, 40)
     
@@ -33,6 +40,7 @@ def classify_audio(audio_file):
     # features = features.reshape(1, 40, 1)
     
     # Realiza la predicción
+    model = tf.keras.models.load_model('my_model.h5')  # Carga del modelo dentro de la función
     prediction = model.predict(features)
     predicted_label_index = np.argmax(prediction)