Update app.py

app.py

@@ -1,30 +1,33 @@
 import streamlit as st
-from transformers import pipeline
+import pyttsx3  # For text-to-speech
+import os
 
-# Initialize text-to-speech model (small lightweight model)
-tts_model = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech")
+# Initialize pyttsx3 TTS engine
+tts_engine = pyttsx3.init()
+
+# Configure TTS engine (optional)
+voices = tts_engine.getProperty("voices")
+tts_engine.setProperty("voice", voices[0].id)  # Choose a voice
+tts_engine.setProperty("rate", 150)  # Set speech rate
 
 # Streamlit app UI
 st.title("Text-to-Audio App")
-st.text("This app generates audio from text input using Hugging Face models.")
+st.text("This app converts your text input into audio using TTS.")
 
 # User input
-text_input = st.text_area("Enter some text for the model:")
+text_input = st.text_area("Enter some text:")
 
 if st.button("Generate Audio"):
     if not text_input.strip():
         st.error("Please enter some text!")
     else:
-        # Generate response
-        st.text("Generating audio response...")
-        tts_audio = tts_model(text_input)
+        # Generate speech
+        audio_file = "output.mp3"
+        tts_engine.save_to_file(text_input, audio_file)
+        tts_engine.runAndWait()
 
-        # Save the audio output
-        audio_file = "response.wav"
-        with open(audio_file, "wb") as f:
-            f.write(tts_audio["wav"])
+        # Play the audio in the app
+        st.audio(audio_file, format="audio/mp3")
 
-        # Display audio response
-        st.audio(audio_file, format="audio/wav")
         st.success("Audio generated successfully!")