app.py

import gradio as gr
from transformers import pipeline

# Create pipelines for ASR, QA, and TTS
asr_pipeline = pipeline("automatic-speech-recognition", model="canary/asr-small-librispeech", device=0)  # Adjust device based on your hardware
qa_pipeline = pipeline("question-answering", model="LLAMA/llama3-base-qa", tokenizer="LLAMA/llama3-base-qa")
tts_pipeline = pipeline("text-to-speech", model="patrickvonplaten/vits-large", device=0)  # Adjust device based on your hardware

def ai_assistant(audio_input):
    # Perform automatic speech recognition (ASR)
    transcribed_text = asr_pipeline(audio_input)[0]['transcription']

    # Perform question answering (QA)
    question = transcribed_text
    context = "Insert your context here"  # Provide the context for the question answering model
    answer = qa_pipeline(question=question, context=context)

    # Convert the answer to speech using text-to-speech (TTS)
    tts_output = tts_pipeline(answer['answer'])

    # Output the speech
    return tts_output[0]['audio']

if __name__ == "__main__":
    # Create a Gradio interface
    gr.Interface(ai_assistant, 
                 inputs=gr.inputs.Audio(source="microphone", type="microphone", label="Speak Here"),
                 outputs=gr.outputs.Audio(type="audio", label="Assistant's Response"),
                 title="AI Assistant",
                 description="An AI Assistant that answers questions based on your speech input.")
    .launch()