Update app.py

app.py

@@ -1,60 +1,3 @@
-# Import libraries
-# Import libraries
-import whisper
-import os
-from gtts import gTTS
-import gradio as gr
-from groq import Groq
-
-
-# Load Whisper model for transcription
-model = whisper.load_model("base")
-
-# Set up Groq API client (ensure GROQ_API_KEY is set in your environment)
-
-GROQ_API_KEY = "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"
-client = Groq(api_key=GROQ_API_KEY)  
-# Function to get the LLM response from Groq
-def get_llm_response(user_input):
-    chat_completion = client.chat.completions.create(
-        messages=[{"role": "user", "content": user_input}],
-        model="llama3-8b-8192",  # Replace with your desired model
-    )
-    return chat_completion.choices[0].message.content
-
-# Function to convert text to speech using gTTS
-def text_to_speech(text, output_audio="output_audio.mp3"):
-    tts = gTTS(text)
-    tts.save(output_audio)
-    return output_audio
-
-# Main chatbot function to handle audio input and output
-def chatbot(audio):
-    # Step 1: Transcribe the audio using Whisper
-    result = model.transcribe(audio)
-    user_text = result["text"]
-
-    # Step 2: Get LLM response from Groq
-    response_text = get_llm_response(user_text)
-
-    # Step 3: Convert the response text to speech
-    output_audio = text_to_speech(response_text)
-
-    return response_text, output_audio
-
-# Gradio interface for real-time interaction
-iface = gr.Interface(
-    fn=chatbot,
-    inputs=gr.Audio(type="filepath"),  # Input from mic
-    outputs=[gr.Textbox(), gr.Audio(type="filepath")],  # Output: response text and audio
-    live=True
-)
-
-# Launch the Gradio app
-iface.launch()
-
-
-
 # Import libraries
 import whisper
 import os