Upload 3 files

dockerfile

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+FROM python:3.9
+WORKDIR /code
+COPY . .
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import pandas as pd
+import numpy as np
+import joblib
+import os
+import sys
+
+# 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
+
+
+scaler = joblib.load('scaler.joblib')
+encoder = joblib.load('encoder.joblib')
+
+
+def get_predict_feat(path):
+    d, s_rate = librosa.load(path, duration=2.5, offset=0.6)
+    res = feat_ext(d)
+    result = np.array(res)
+    result = np.reshape(result, newshape=(1, 41))
+    i_result = scaler.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 = encoder.inverse_transform(predictions)
+    return y_pred[0][0]
+
+
+app = FastAPI()
+
+
+@app.post("/")
+async def read_root(request: Request, file: UploadFile = File(...)):
+    json_data = await request.json()
+
+    return {"filename": file.filename, "filepath": f"/app/{file.filename}"}

requirements.txt

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+keras
+pandas
+numpy
+librosa
+seaborn
+matplotlib
+tensorflow
+fastapi==0.74.*
+requests==2.27.*
+sentencepiece==0.1.*
+uvicorn[standard]==0.17.*