Create models.py

models.py

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+# Import the necessary libraries
+from transformers import pipeline
+
+# Initialize the text classification model with a pre-trained model
+model_text_emotion = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+
+# Initialize the audio classification model with a pre-trained SER model
+model_speech_emotion = pipeline("audio-classification", model="aherzberg/ser_model_fixed_label")
+
+# Initialize the automatic speech recognition model with a pre-trained model that is capable of converting speech to text
+model_voice2text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")
+
+# A function that uses the initialized text classification model to predict the emotion of a given text input
+def infere_text_emotion(text):
+    return model_text_emotion(text)[0]["label"].capitalize()
+
+# A function that uses the initialized audio classification model to predict the emotion of a given speech input
+def infere_speech_emotion(text):
+    # Dict that maps the speech model emotions with the text's ones
+    emotions_dict = {"angry": "Anger", "disgust": "Disgust", "fear": "Fear", "happy": "Joy", "neutral": "Neutral", "sad": "Sadness"}
+    inference = model_speech_emotion(text)[0]["label"]
+    return emotions_dict[inference]
+
+# A function that uses the initialized automatic speech recognition model to convert speech (as an audio file) to text
+def infere_voice2text(audio_file):
+    return model_voice2text(audio_file)["text"]