Create app.py

app.py

@@ -0,0 +1,42 @@
+from datetime import datetime
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+import librosa
+
+# Load pre-trained models
+speech_to_text_model = load_model('speech_to_text_model.h5')
+translation_model = load_model('translation_model.h5')
+
+def preprocess_audio(file_path):
+    # Load and preprocess the audio file
+    audio, sr = librosa.load(file_path, sr=16000)
+    mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
+    return np.expand_dims(mfccs, axis=0)
+
+def translate_speech_to_text(audio_file):
+    # Preprocess the audio file
+    audio_features = preprocess_audio(audio_file)
+    
+    # Predict text from audio
+    predicted_text = speech_to_text_model.predict(audio_features)
+    
+    # Translate text
+    translated_text = translation_model.predict([predicted_text])
+    
+    return translated_text
+
+def is_after_six_pm():
+    current_time = datetime.now()
+    return current_time.hour >= 18
+
+def main(audio_file):
+    if is_after_six_pm():
+        translated_text = translate_speech_to_text(audio_file)
+        print("Translated Text:", translated_text)
+    else:
+        print("Service available only after 6 PM IST.")
+
+# Example usage
+audio_file_path = 'path/to/your/audiofile.wav'
+main(audio_file_path)