Update app.py

app.py

@@ -1,42 +1,59 @@
 import streamlit as st
-from transformers import pipeline
+import requests
+import json
 import soundfile as sf
+import io
 
-# Initialize translation model (English <-> Urdu)
-translation_model = pipeline("translation_en_to_ur", model="Helsinki-NLP/opus-mt-en-ur")
-reverse_translation_model = pipeline("translation_ur_to_en", model="Helsinki-NLP/opus-mt-ur-en")
+# Set your Groq API key here
+GROQ_API_KEY = "YOUR_GROQ_API_KEY"
 
-# Initialize text-to-speech model (English to Speech)
-tts_model = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech")
-
-# Function to translate text and provide feedback
-def translate_and_speak(text, direction):
-    if direction == "English to Urdu":
-        translated_text = translation_model(text)[0]['translation_text']
-    else:
-        translated_text = reverse_translation_model(text)[0]['translation_text']
+# 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
+        }]
+    }
     
-    # Use TTS to synthesize speech from translated text
-    audio = tts_model(translated_text)
-    audio_path = "output.wav"
-    sf.write(audio_path, audio["array"], 22050)  # Save audio to file
-    return translated_text, audio_path
+    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 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 app UI
-st.title("AI-Powered Language Tutor")
-st.write("An interactive tutor to help you practice English-Urdu translations with speech feedback!")
+st.title("Groq TTS Integration with Streamlit")
+st.write("Enter some text to convert it into speech using Groq API!")
 
-# User input for translation
-text_input = st.text_area("Enter Text", "Hello, how are you?")
-direction = st.radio("Choose Translation Direction", ["English to Urdu", "Urdu to English"])
+# 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")
 
-# Button to process the text and play audio
-if st.button("Translate and Speak"):
-    translated_text, audio_path = translate_and_speak(text_input, direction)
-    
-    # Display translated text and audio
-    st.subheader("Translated Text:")
-    st.write(translated_text)
-    
-    st.subheader("Generated Speech:")
-    st.audio(audio_path, format="audio/wav")