Update app.py

app.py

@@ -1,59 +1,36 @@
 import streamlit as st
-import requests
-import json
-import soundfile as sf
-import io
+from gtts import gTTS
+import pyttsx3
+import os
 
-# Set your Groq API key here
-GROQ_API_KEY = "YOUR_GROQ_API_KEY"
+# Function to use Google Text-to-Speech (gTTS)
+def gtts_text_to_speech(text):
+    tts = gTTS(text=text, lang='en', slow=False)
+    tts.save("output.mp3")
+    return "output.mp3"
 
-# Function to interact with Groq API for Text-to-Speech
-def groq_tts(text):
-    url = "https://api.groq.com/openai/v1/chat/completions"
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {GROQ_API_KEY}"
-    }
-    payload = {
-        "model": "llama3-8b-8192",
-        "messages": [{
-            "role": "user",
-            "content": text
-        }]
-    }
-    
-    response = requests.post(url, headers=headers, json=payload)
-    
-    if response.status_code == 200:
-        data = response.json()
-        # Assuming the audio is returned as base64, decode it
-        audio_base64 = data.get("choices")[0].get("message").get("content")
-        return audio_base64
-    else:
-        st.error(f"Error with Groq API: {response.status_code}")
-        return None
+# Function to use pyttsx3 (Offline TTS)
+def pyttsx3_text_to_speech(text):
+    engine = pyttsx3.init()
+    engine.save_to_file(text, "output.mp3")
+    engine.runAndWait()
+    return "output.mp3"
 
-# Function to save audio and display in Streamlit
-def save_audio_from_base64(audio_base64):
-    if audio_base64:
-        audio_data = io.BytesIO(audio_base64.decode('base64'))
-        audio_path = "groq_audio.wav"
-        sf.write(audio_path, audio_data, 22050)  # Save audio to WAV
-        return audio_path
-    return None
+# Streamlit UI
+st.title("Text-to-Speech Converter")
 
-# Streamlit app UI
-st.title("Groq TTS Integration with Streamlit")
-st.write("Enter some text to convert it into speech using Groq API!")
+# Input text
+user_input = st.text_area("Enter text:", "Hello, how can I assist you today?")
 
-# Text input
-text_input = st.text_area("Enter Text", "Explain the importance of fast language models")
-
-if st.button("Generate Speech"):
-    audio_base64 = groq_tts(text_input)
-    if audio_base64:
-        audio_path = save_audio_from_base64(audio_base64)
-        if audio_path:
-            st.subheader("Generated Speech:")
-            st.audio(audio_path, format="audio/wav")
+# Select TTS option
+tts_option = st.radio("Choose Text-to-Speech Method", ("Google TTS", "Offline TTS"))
 
+# Button to convert to speech
+if st.button("Convert to Speech"):
+    if tts_option == "Google TTS":
+        output_file = gtts_text_to_speech(user_input)
+    else:
+        output_file = pyttsx3_text_to_speech(user_input)
+    
+    st.audio(output_file, format="audio/mp3")
+    st.success("Speech generated successfully!")