Delete app2.py

app2.py

@@ -1,42 +0,0 @@
-import os
-from speechbrain.pretrained.interfaces import foreign_class
-import gradio as gr
-
-import warnings
-warnings.filterwarnings("ignore")
-
-# Loading the speechbrain emotion detection model
-learner = foreign_class(
-    source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
-    pymodule_file="custom_interface.py", 
-    classname="CustomEncoderWav2vec2Classifier"
-)
-
-# Building prediction function for gradio
-emotion_dict = {
-    'sad': 'Sad', 
-    'hap': 'Happy',
-    'ang': 'Anger',
-    'fea': 'Fear',
-    'sur': 'Surprised',
-    'neu': 'Neutral'
-}
-
-def predict_emotion(file_path):
-    # Since we get the file path from the dropdown, we don't need to access the `.name` property
-    out_prob, score, index, text_lab = learner.classify_file(file_path)
-    return emotion_dict[text_lab[0]]
-
-# Folder containing audio files
-folder = "prerecorded"
-
-# Assuming that the 'prerecorded' folder is in the current working directory
-# Change the working directory path if necessary
-audio_files = [os.path.join(folder, file) for file in os.listdir(folder) if file.endswith('.wav')]
-
-# Loading gradio interface with dropdown for audio selection
-inputs = gr.inputs.Dropdown(audio_files, label="Select Audio File")
-outputs = "text"
-title = "Machine Learning Emotion Detection"
-description = "Gradio demo for Emotion Detection. To use it, select an audio file from the dropdown and click 'Submit'. Read more at the links below."
-gr.Interface(predict_emotion, inputs, outputs, title=title, description=description).launch()