import tensorflow as tf
#from transformers import pipeline
from huggingface_hub import from_pretrained_keras
import pandas as pd
import numpy as np
import joblib
import os
import sys
import pickle
import shutil
# librosa is a Python library for analyzing audio and music. It can be used to extract the data from the audio files we will see it later.
import librosa
import librosa.display
import seaborn as sns
import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split

# to play the audio files


import keras
from keras.preprocessing import sequence
from keras.models import Sequential, model_from_json
from keras.layers import Dense, Embedding
from keras.layers import LSTM, BatchNormalization, GRU
from keras.preprocessing.text import Tokenizer

from tensorflow.keras.utils import to_categorical
from keras.layers import Input, Flatten, Dropout, Activation
from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D
from keras.models import Model
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.optimizers import SGD
from fastapi import FastAPI, Request, UploadFile, File


import warnings
if not sys.warnoptions:
    warnings.simplefilter("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)

model=from_pretrained_keras( 'Mohamed41/MODEL_EMOTION_AR_TEXT_72P')


# def feat_ext(data):
#     # Time_domain_features
#     # ZCR Persody features or Low level ascoustic features
#     result = np.array([])
#     zcr = np.mean(librosa.feature.zero_crossing_rate(y=data).T, axis=0)
#     result = np.hstack((result, zcr))  # stacking horizontally
#     # Frequency_domain_features
#     # Spectral and wavelet Features
#     # MFCC
#     mfcc = np.mean(librosa.feature.mfcc(y=data, sr=22050, n_mfcc=40).T, axis=0)
#     result = np.hstack((result, mfcc))  # stacking horizontally
#     return result


with open('scaler3.pickle', 'rb') as f:
    scaler3 = pickle.load(f)
    
with open('encoder3.pickle', 'rb') as f:
    encoder3 = pickle.load(f)

def feat_ext_test(data):
    #Time_domain_features
    # ZCR Persody features or Low level ascoustic features
    result = np.array([])
    zcr = np.mean(librosa.feature.zero_crossing_rate(y=data).T, axis=0)
    result=np.hstack((result, zcr)) # stacking horizontally
    #Frequency_domain_features
    #Spectral and wavelet Features
    #MFCC
    mfcc = np.mean(librosa.feature.mfcc(y=data, sr=22050,n_mfcc=40).T, axis=0)
    result = np.hstack((result, mfcc)) # stacking horizontally
    return result

def get_predict_feat(path):
    d, s_rate= librosa.load(path, duration=2.5, offset=0.6)
    res=feat_ext_test(d)
    result=np.array(res)
    result=np.reshape(result,newshape=(1,41))
    i_result = scaler3.transform(result)
    final_result=np.expand_dims(i_result, axis=2)
    
    return final_result


emotions1 = {1: 'Neutral', 2: 'Calm', 3: 'Happy', 4: 'Sad',
             5: 'Angry', 6: 'Fear', 7: 'Disgust', 8: 'Surprise'}


def prediction(path1):
    res = get_predict_feat(path1)
    predictions = model.predict(res)
    y_pred = encoder3.inverse_transform(predictions)
    return y_pred[0][0]


app = FastAPI()




@app.post("/")
async def read_root( file: UploadFile = File(...)):
    file_extension = os.path.splitext(file.filename)[1]
    with open("tmp"+file_extension, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)

     
    x = prediction("tmp"+file_extension) 
    return {"filename": file.filename, "filepath": f"/app/{file.filename}","prediction":x}