!pip install opensmile
!pip install scikit-learn==1.3.2
!pip install gradio

import opensmile
import joblib
import wave
import datetime
import os
import pandas as pd
from sklearn.preprocessing import StandardScaler


from IPython.display import Javascript, Audio

from base64 import b64decode

import gradio as gr


model_path = "RF_emobase_20_model_top1_score0.6863_20231207_1537.joblib"
model = joblib.load(model_path)

def extract_features(audio_path):
    smile = opensmile.Smile(
    #feature_set=opensmile.FeatureSet.GeMAPSv01b,
    feature_set=opensmile.FeatureSet.emobase,
    feature_level=opensmile.FeatureLevel.Functionals,
    )
    feature_df = smile.process_files(audio_path)
    output_features = ['F0env_sma_de_amean', 'lspFreq_sma_de[5]_linregc1', 'mfcc_sma[3]_linregc1', 'lspFreq_sma[6]_quartile1', 'lspFreq_sma_de[6]_linregerrQ', 'lspFreq_sma_de[6]_maxPos', 'lspFreq_sma_de[6]_iqr2-3', 'lspFreq_sma_de[7]_minPos', 'lspFreq_sma_de[4]_linregc1', 'lspFreq_sma_de[6]_linregerrA', 'lspFreq_sma_de[6]_linregc2', 'lspFreq_sma[5]_amean', 'lspFreq_sma_de[6]_iqr1-2', 'mfcc_sma[1]_minPos', 'mfcc_sma[4]_linregc1', 'mfcc_sma[9]_iqr2-3', 'lspFreq_sma[5]_kurtosis', 'lspFreq_sma_de[3]_skewness', 'mfcc_sma[3]_minPos', 'mfcc_sma[12]_linregc1']
    df = pd.DataFrame(feature_df.values[0], index=feature_df.columns)
    df = df[df.index.isin(output_features)]
    df = df.T
    scaler = StandardScaler()
    feature = scaler.fit_transform(df)
    print(df.shape)

    return feature

def main(input):
  # openSMILEで特徴量抽出
  feature_vector = extract_features([input])

  # ロードしたモデルで推論
  prediction = model.predict(feature_vector)
  #print(f"Prediction: {prediction}")
  return prediction

gr.Interface(
    title = 'Question Classifier Model',
    fn = main,
    inputs=[
        gr.Audio(sources=["microphone","upload"], type="filepath")
    ],
    outputs=[
        "textbox"
    ],
    live=True
    ).launch(debug=True)