Update app.py

app.py

@@ -1,9 +1,11 @@
+# Install required libraries
+!pip install openai-whisper gradio gtts groq
+
 import os
 import whisper
 from groq import Groq
 from gtts import gTTS
 import tempfile
-import IPython.display as ipd
 import gradio as gr
 
 # Step 1: Set up Whisper for transcription
@@ -14,8 +16,10 @@ def transcribe_audio(audio_file):
     result = model.transcribe(audio_file)
     return result["text"]
 
-GROOQ_API_KEY = "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"
-Client = Grooq(api_key=GROOQ_API_KEY)
+# Step 2: Set up Groq API for interacting with the LLM (e.g., Llama 3)
+api_key = "your_groq_api_key"  # Replace with your actual API key
+client = Groq(api_key=api_key)
+
 # Function to get a response from the Groq LLM (Llama 3)
 def get_groq_response(text):
     chat_completion = client.chat.completions.create(
@@ -33,10 +37,8 @@ def text_to_speech(text):
     temp_file = tempfile.NamedTemporaryFile(delete=False)
     tts.save(temp_file.name)
     
-    # Play the audio in Colab (testing)
-    ipd.Audio(temp_file.name)
-    
-    return temp_file.name  # Return file path for further use
+    # Return the file path for Gradio to play the audio
+    return temp_file.name
 
 # Step 4: Integrate everything into a Gradio interface
 def voice_chatbot(audio_input):
@@ -59,4 +61,4 @@ iface = gr.Interface(fn=voice_chatbot,
                      live=True)
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch()