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import gradio as gr
import groq
import io
import numpy as np
import soundfile as sf
import pyttsx3 # Text-to-speech conversion
# Initialize text-to-speech engine
tts_engine = pyttsx3.init()
def transcribe_audio(audio, api_key):
if audio is None:
return ""
client = groq.Client(api_key=api_key)
# Convert audio to the format expected by the model
audio_data = audio[1] # Get the numpy array from the tuple
buffer = io.BytesIO()
sf.write(buffer, audio_data, audio[0], format='wav')
buffer.seek(0)
try:
# Use Distil-Whisper English powered by Groq for transcription
completion = client.audio.transcriptions.create(
model="distil-whisper-large-v3-en",
file=("audio.wav", buffer),
response_format="text"
)
return completion
except Exception as e:
return f"Error in transcription: {str(e)}"
def generate_response(transcription, api_key):
if not transcription:
return "No transcription available. Please try speaking again."
client = groq.Client(api_key=api_key)
try:
# Use Llama 3 70B powered by Groq for text generation
completion = client.chat.completions.create(
model="llama3-70b-8192",
messages=[{"role": "user", "content": transcription}]
)
return completion.choices[0].message.content
except Exception as e:
return f"Error in response generation: {str(e)}"
def convert_text_to_speech(text):
tts_engine.save_to_file(text, 'response_output.wav')
tts_engine.runAndWait()
with open("response_output.wav", "rb") as f:
audio_bytes = f.read()
return audio_bytes
def process_audio(audio, api_key):
if not api_key:
return "Please enter your Groq API key.", "API key is required."
transcription = transcribe_audio(audio, api_key)
response = generate_response(transcription, api_key)
if "Error" in response:
return transcription, response, None # In case of error, return empty audio
audio_output = convert_text_to_speech(response)
return transcription, response, audio_output
# Custom CSS
custom_css = """
.gradio-container {
background-color: #f5f5f5;
}
.gr-button-primary {
background-color: #f55036 !important;
border-color: #f55036 !important;
}
.gr-button-secondary {
color: #f55036 !important;
border-color: #f55036 !important;
}
#groq-badge {
position: fixed;
bottom: 20px;
right: 20px;
z-index: 1000;
}
"""
# Gradio Interface
with gr.Blocks(theme=gr.themes.Default()) as demo:
gr.Markdown("# 🎙️ Groq x Gradio Voice-Powered AI Assistant")
api_key_input = gr.Textbox(type="password", label="Enter your Groq API Key")
with gr.Row():
audio_input = gr.Audio(label="Speak!", type="numpy")
with gr.Row():
transcription_output = gr.Textbox(label="Transcription")
response_output = gr.Textbox(label="AI Assistant Response")
audio_output = gr.Audio(label="Voice Response", type="file")
submit_button = gr.Button("Process", variant="primary")
gr.HTML("""
<div id="groq-badge">
<div style="color: #f55036; font-weight: bold;">POWERED BY GROQ</div>
</div>
""")
submit_button.click(
process_audio,
inputs=[audio_input, api_key_input],
outputs=[transcription_output, response_output, audio_output]
)
gr.Markdown("""
## How to use this app:
1. Enter your [Groq API Key](https://console.groq.com/keys) in the provided field.
2. Click on the microphone icon and speak your message (or upload an audio file).
3. Click the "Process" button to transcribe your speech and generate a response from our AI assistant.
4. The transcription, AI assistant response, and voice response will appear.
""")
demo.launch()
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