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app.py

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+# Imports
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import cross_val_predict
+from sklearn.metrics import accuracy_score
+import joblib
+import os
+import pandas as pd
+import numpy as np
+import random
+import tensorflow as tf
+import torch
+from tqdm import tqdm
+import tensorflow_io as tfio
+from pathlib import Path
+from IPython import display
+from speechbrain.pretrained import EncoderClassifier
+import torchaudio
+import joblib
+
+
+
+# Defining the transcription function
+def snoring(audio):
+    return text
+
+# Defining the audio filepaths
+audio = gr.inputs.Audio(type="filepath")
+
+# Loading the gradio framwork
+iface = gr.Interface(fn=snoring,inputs=audio, outputs="text", title="Snore.AI", description="Detect Snotring with artificial intelligence.")
+iface.launch()

requirements.txt

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+sklearn.linear_model import LogisticRegression
+sklearn.model_selection import cross_val_predict
+sklearn.metrics import accuracy_score
+joblib
+os
+pandas as pd
+numpy as np
+random
+tensorflow as tf
+torch
+tqdm import tqdm
+tensorflow_io as tfio
+pathlib import Path
+IPython import display
+speechbrain.pretrained import EncoderClassifier
+torchaudio
+joblib