Delete mini_app.py

mini_app.py

@@ -1,47 +0,0 @@
-import torch
-import gradio as gr
-from model import predict_params, AudioDataset
-from interfaz import estilo, my_theme
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model_class, id2label_class = predict_params(model_path="distilhubert-finetuned-mixed-data", dataset_path="data/mixed_data", filter_white_noise=True)
-
-def call(audiopath, model, dataset_path, filter_white_noise):
-    model.to(device)
-    model.eval()
-    audio_dataset = AudioDataset(dataset_path, {}, filter_white_noise,)
-    processed_audio = audio_dataset.preprocess_audio(audiopath)
-    inputs = {"input_values": processed_audio.to(device).unsqueeze(0)}
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-    return logits
-
-def predict(audio_path_pred):
-    with torch.no_grad():
-        logits = call(audio_path_pred, model=model_class, dataset_path="data/mixed_data", filter_white_noise=True)
-        predicted_class_ids_class = torch.argmax(logits, dim=-1).item()
-        label_class = id2label_class[predicted_class_ids_class]
-        label_mapping = {0: 'Hambre', 1: 'Problemas para respirar', 2: 'Dolor', 3: 'Cansancio/Incomodidad'}
-        label_class = label_mapping.get(predicted_class_ids_class, label_class)
-    return label_class
-
-def cambiar_pestaña():
-    return gr.update(visible=False), gr.update(visible=True)
-
-with gr.Blocks(theme=my_theme) as demo:
-    estilo()
-    with gr.Column(visible=False) as pag_predictor:
-        gr.Markdown("<h2>Predictor</h2>")
-        audio_input = gr.Audio(
-            min_length=1.0,
-            format="wav",
-            label="Baby recorder",
-            type="filepath",
-            )
-        gr.Button("¿Por qué llora?").click(
-            predict,
-            inputs=audio_input,
-            outputs=gr.Textbox(label="Tu bebé llora por:")
-            )
-demo.launch(share=True)