Delete app.py

app.py

@@ -1,27 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import numpy as np
-from model import SAMPLING_RATE, clasificador, monitor
-# modelo = monitor
-modelo = clasificador
-pipe = pipeline("audio-classification", model=f"A-POR-LOS-8000/distilhubert-finetuned-cry-detector", device="cuda")
-
-def transcribe(audio):
-    _, y = audio
-    y = y.astype(np.float32) # con torch.float32 da error
-    y /= np.max(np.abs(y))
-    results = pipe({"sampling_rate": SAMPLING_RATE, "raw": y})
-    top_result = results[0]  # Get the top result (most likely classification)
-    label = top_result["label"]  # Extract the label from the top result
-    return label
-
-demo = gr.Interface(
-    transcribe,
-    gr.Audio(
-        min_length=1.0,
-        max_length=10.0,
-        format="wav",
-        ),
-    "text",
-)
-demo.launch()