Delete main.py

main.py

@@ -1,109 +0,0 @@
-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')
-
-  
-with open('scaler3.pickle', 'rb') as f:
-    scaler3 = pickle.load(f)
-    
-with open('encoder3.pickle', 'rb') as f:
-    encoder3 = pickle.load(f)
-
-def zcr(data,frame_length,hop_length):
-    zcr=librosa.feature.zero_crossing_rate(data,frame_length=frame_length,hop_length=hop_length)
-    return np.squeeze(zcr)
-def rmse(data,frame_length=2048,hop_length=512):
-    rmse=librosa.feature.rms(y=data,frame_length=frame_length,hop_length=hop_length)
-    return np.squeeze(rmse)
-def mfcc(data,sr,frame_length=2048,hop_length=512,flatten:bool=True):
-    mfcc=librosa.feature.mfcc(y=data,sr=sr)
-    return np.squeeze(mfcc.T)if not flatten else np.ravel(mfcc.T)
-
-def extract_features(data,sr=22050,frame_length=2048,hop_length=512):
-    result=np.array([])
-
-    result=np.hstack((result,
-                      zcr(data,frame_length,hop_length),
-                      rmse(data,frame_length,hop_length),
-                      mfcc(data,sr,frame_length,hop_length)
-                     ))
-    return result
-
-def get_predict_feat(path):
-    d, s_rate= librosa.load(path, duration=2.5, offset=0.6)
-    res=extract_features(d)
-    result=np.array(res)
-    result=np.reshape(result,newshape=(1,2376))
-    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=loaded_model.predict(res)
-    y_pred = encoder3.inverse_transform(predictions)
-    print(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}