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# Import libraries
import whisper
import os
import gradio as gr
from groq import Groq
from gtts import gTTS
import traceback
# Step 1: Load Whisper Model for Transcription
try:
model = whisper.load_model("base")
except Exception as e:
print("Error loading Whisper model:", e)
model = None
# Step 2: Initialize Groq Client
try:
client = Groq(api_key = os.getenv("MY_API_KEY"))
except Exception as e:
print("Error initializing Groq client:", e)
client = None
# Function to get response from Groq API using LLaMA model
def get_response_from_groq(user_input):
try:
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": user_input}],
model="llama3-8b-8192",
)
return chat_completion.choices[0].message.content
except Exception as e:
print("Error getting response from Groq API:", e)
return "Sorry, I couldn't generate a response at this time."
# Step 3: Function to convert text to speech using gTTS
def text_to_speech(text):
try:
tts = gTTS(text)
tts.save("response.mp3")
return "response.mp3"
except Exception as e:
print("Error converting text to speech:", e)
return None
# Step 4: Define the Chatbot Function for Gradio
def chatbot(audio_input):
try:
if model is None:
return "Whisper model not loaded.", "Unable to transcribe.", None
# Transcribe audio input using Whisper
transcription = model.transcribe(audio_input)["text"]
# Get response from Groq API using LLaMA model
response = get_response_from_groq(transcription)
# Convert response to speech
speech_file = text_to_speech(response)
return transcription, response, speech_file
except Exception as e:
print("Error in chatbot function:", e)
traceback.print_exc()
return "Error occurred during processing.", "Please try again.", None
# Step 5: Create Gradio Interface
try:
gr.Interface(
fn=chatbot,
inputs=gr.Audio(type="filepath"),
outputs=["text", "text", "audio"],
live=True
).launch()
except Exception as e:
print("Error launching Gradio interface:", e) |