import whisper
import gradio as gr
from transformers import pipeline


model = whisper.load_model("base")
sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english")

def process_audio_file(file):
    with open(file, "rb") as f:
        inputs = f.read()

    audio = ffmpeg_read(inputs, sampling_rate)
    return audio


def transcribe(Microphone, File_Upload):
    warn_output = ""
    if (Microphone is not None) and (File_Upload is not None):
        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        file = Microphone

    elif (Microphone is None) and (File_Upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    elif Microphone is not None:
        file = Microphone
    else:
        file = File_Upload

    result = model.transcribe(file, task="translate")
    text = sentiment_analysis(result['text'])
    
    label = text[0]['label']
    score = text[0]['score']
    return label, score

iface = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
        gr.inputs.Audio(source="upload", type='filepath', optional=True),
    ],
    outputs=[
        gr.outputs.Textbox(label="Sentiment"),
        gr.outputs.Textbox(label="Score")
    ],
    layout="horizontal",
    theme="huggingface",
    title="Whisper Language Identification",
    description="Demo for Language Identification using OpenAI's [Whisper Large V2](https://huggingface.co/openai/whisper-large-v2).",
    allow_flagging='never',
)
iface.launch(enable_queue=True)