import whisper
import gradio as gr
import time
import google.generativeai as palm
palm.configure(api_key='AIzaSyCLy2IgNwMBDbhYH_zvUDo0AMWQdRLQI0E')
model = whisper.load_model("base")
print(model.device)

def transcribe(audio):
    #time.sleep(3)
    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    # # detect the spoken language
    # _, probs = model.detect_language(mel)
    # print(f"Detected language: {max(probs, key=probs.get)}")

    # decode the audio
    # options = whisper.DecodingOptions()
    # for cpu
    options = whisper.DecodingOptions(fp16=False)
    result = whisper.decode(model, mel, options)

    print(result.text)
    completion = palm.generate_text(
        model='models/text-bison-001',
        prompt=result.text,
        temperature=0,
        # The maximum length of the response
        max_output_tokens=500,
    ).result
    return completion

gr.Interface(
    title = 'Real-time AI-based Audio Transcription, Recognition, Answerer Web App',
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath")
    ],
    outputs=[
        "textbox"
    ],
    live=True).launch(share=True)