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="AzVoiceSent: Sentiment Classification from Voice Transcriptions in Azerbaijani",
    description="AzVoiceSent is research project focused on sentiment classification from voice transcriptions in Azerbaijani. The project has the potential to provide valuable insights into the sentiment expressed by speakers in various domains and applications. ",
    allow_flagging='never',
)
iface.launch(enable_queue=True)