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Create app.py

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  1. app.py +68 -0
app.py ADDED
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+ import os
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+ import whisper
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+ from gtts import gTTS
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+ from groq import Groq
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+ import gradio as gr
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+
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+ # Initialize Whisper model
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+ model = whisper.load_model("base")
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+
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+ # Initialize Groq API (set your GROQ_API_KEY in the environment)
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+ client = Groq(api_key=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)
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+
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+ # Step 1: Transcribe Audio (Speech-to-Text using Whisper)
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+ def transcribe_audio(audio_path):
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+ result = model.transcribe(audio_path)
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+ return result['text']
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+
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+ # Step 2: Interact with LLM (Groq API)
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+ def interact_with_llm(user_input):
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+ chat_completion = client.chat.completions.create(
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+ messages=[
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+ {
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+ "role": "user",
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+ "content": user_input,
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+ }
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+ ],
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+ model="llama3-8b-8192",
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+ stream=False,
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+ )
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+ response = chat_completion.choices[0].message.content
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+ return response
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+
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+ # Step 3: Convert Text to Speech using gTTS
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+ def text_to_speech(text):
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+ tts = gTTS(text, lang="en")
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+ audio_file = "response.mp3"
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+ tts.save(audio_file)
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+ return audio_file
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+
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+ # Combined workflow: Transcribe -> Interact with LLM -> Convert to Speech
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+ def chatbot(audio):
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+ # Step 1: Transcribe Audio to Text
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+ transcription = transcribe_audio(audio)
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+
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+ # Step 2: Get LLM response based on transcription
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+ llm_response = interact_with_llm(transcription)
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+
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+ # Step 3: Convert LLM response to audio (text-to-speech)
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+ audio_output = text_to_speech(llm_response)
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+
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+ return transcription, llm_response, audio_output
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+
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+ # Gradio Interface setup
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+ interface = gr.Interface(
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+ fn=chatbot,
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+ inputs=gr.Audio(type="filepath", label="Speak into the microphone"),
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+ outputs=[
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+ "text", # Transcription output
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+ "text", # LLM response output
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+ gr.Audio(type="filepath", label="Response Audio") # Final audio output
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+ ],
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+ live=True,
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+ title="Real-Time Voice-to-Voice Chatbot",
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+ description="Talk to an AI in real-time! Speak into the microphone, get a response, and hear it back.",
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+ )
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+
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+ # Launch Gradio app
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+ interface.launch()