Deploy Speech to Emotion model

Dockerfile

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

cnn_model2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e558903193ebee197ad23f20c7be3d8698bfae03c6fdc012e4f8058558d6f7c
+size 77336

main.py

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+from flask import Flask, request, jsonify
+from werkzeug.utils import secure_filename
+import pandas as pd
+import numpy as np
+import os
+import time
+import sys
+from tensorflow import keras
+#from keras.models import model_from_json
+#from tensorflow.python.keras import optimizers
+
+import os
+import random
+
+import librosa
+
+emotions = {0: 'female_angry', 1 : 'female_calm', 2 : 'female_fearful',  3 : 'female_happy', 4 : 'female_sad', 5 : 'male_angry', 6: 'male_calm',  7 : 'male_fearful', 8 : 'male_happy', 9 : 'male_sad'}
+
+#model = tf.keras.models.load_model('model.keras')
+def make_predictions(inputfile):
+  cnn_model = keras.models.load_model("cnn_model2.h5")
+  prediction_data, prediction_sr = librosa.load(
+          inputfile,
+          res_type="kaiser_fast",
+          duration=3,
+          sr=22050,
+          offset=0.5,
+      )
+  mfccs = np.mean(librosa.feature.mfcc(y=prediction_data, sr=prediction_sr, n_mfcc=40).T, axis=0)
+  x = np.expand_dims(mfccs, axis=1)
+  x = np.expand_dims(x, axis=0)
+  predictions = cnn_model.predict(x)
+
+  emotions_dict = {
+          "0": "neutral",
+          "1": "calm",
+          "2": "happy",
+          "3": "sad",
+          "4": "angry",
+          "5": "fearful",
+          "6": "disgusted",
+          "7": "surprised",
+  }
+  return emotions_dict[str(predictions[0].argmax())]
+# json_file = open('model.json', 'r')
+# loaded_model_json = json_file.read()
+# json_file.close()
+# loaded_model = model_from_json(loaded_model_json)
+# # load weights into new model
+# loaded_model.load_weights("Emotion_Voice_Detection_Model.h5")
+# print("Loaded model from disk")
+# #opt = optimizers.adam(lr=0.00001)
+# # evaluate loaded model on test data
+# loaded_model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
+
+# def data_preprocessing(filename):
+#   X, sample_rate = librosa.load(filename,duration=2.5,sr=22050*2,offset=0.5)
+#   sample_rate = np.array(sample_rate)
+#   mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13),axis=0)
+#   featurelive = mfccs
+#   livedf2 = featurelive
+#   livedf2= pd.DataFrame(data=livedf2)
+#   livedf2 = livedf2.stack().to_frame().T
+#   twodim= np.expand_dims(livedf2, axis=2)
+#   return twodim
+
+# def make_prediction(df):
+#   pred = loaded_model.predict(df,
+#                          batch_size=32,
+#                          verbose=1)
+#   preds1=pred.argmax(axis=1)
+#   abc = preds1.astype(int).flatten()
+#   return emotions[abc[0]]
+
+
+app = Flask(__name__)
+
+
+
+@app.route('/')
+def index():
+    return jsonify({'message': 'Welcome To Speech to Emotion'})
+
+@app.route('/api/media-file', methods=['POST'])
+def predict_emotion():
+    audiofile = request.files['audiofile']
+    filename = secure_filename(audiofile.filename)
+    temp_path = filename
+    audiofile.save(temp_path)
+
+    
+    #twodim = data_preprocessing(temp_path)
+    
+
+    res = jsonify({'result': make_predictions(temp_path)})
+    os.remove(temp_path)
+    return res
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+flask
+gunicorn
+pandas
+numpy
+tensorflow
+librosa