app.py

import os
import torch
import gradio as gr
from huggingface_hub import InferenceClient
from model import predict_params, AudioDataset
from interfaz import estilo, my_theme

token = os.getenv("HF_TOKEN")
client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct", token=token)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_class, id2label_class = predict_params(model_path="A-POR-LOS-8000/distilhubert-finetuned-mixed-data", dataset_path="A-POR-LOS-8000/data/mixed_data", filter_white_noise=True)
model_mon, id2label_mon = predict_params(model_path="A-POR-LOS-8000/distilhubert-finetuned-cry-detector", dataset_path="A-POR-LOS-8000/data/baby_cry_detection", filter_white_noise=False)

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="A-POR-LOS-8000/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 predict_stream(audio_path_stream):
    with torch.no_grad():
        logits = call(audio_path_stream, model=model_mon, dataset_path="A-POR-LOS-8000/data/baby_cry_detection", filter_white_noise=False)
        probabilities = torch.nn.functional.softmax(logits, dim=-1)
        crying_probabilities = probabilities[:, 1]
        avg_crying_probability = crying_probabilities.mean()*100
        if avg_crying_probability < 15:
            label_class = predict(audio_path_stream)
            return "Está llorando por:", f"{label_class}. Probabilidad: {avg_crying_probability:.1f}%"
        else:
            return "No está llorando.", f"Probabilidad: {avg_crying_probability:.1f}%"

def decibelios(audio_path_stream):
    with torch.no_grad():
        logits = call(audio_path_stream, model=model_mon, dataset_path="A-POR-LOS-8000/data/baby_cry_detection", filter_white_noise=False)
        rms = torch.sqrt(torch.mean(torch.square(logits)))
        db_level = 20 * torch.log10(rms + 1e-6).item()
        return db_level

def mostrar_decibelios(audio_path_stream, visual_threshold):
    db_level = decibelios(audio_path_stream)
    if db_level < visual_threshold:
        return f"Prediciendo. Decibelios: {db_level:.2f}"
    elif db_level > visual_threshold:
        return "No detectamos ruido..."

def predict_stream_decib(audio_path_stream, visual_threshold):
    db_level = decibelios(audio_path_stream)
    if db_level < visual_threshold:
        llorando, probabilidad = predict_stream(audio_path_stream)
        return f"{llorando} {probabilidad}"
    else:
        return ""

def chatbot_config(message, history: list[tuple[str, str]]):
    system_message = "You are a Chatbot specialized in baby health and care."
    max_tokens = 512
    temperature = 0.7
    top_p = 0.95
    messages = [{"role": "system", "content": system_message}]
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
    messages.append({"role": "user", "content": message})
    response = ""
    for message_response in client.chat_completion(messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p):
        token = message_response.choices[0].delta.content
        response += token
        yield response

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=True) as chatbot:    
        gr.Markdown("<h2>Asistente</h2>")
        gr.ChatInterface(
            chatbot_config # TODO: Mirar argumentos
            )
        gr.Markdown("Este chatbot no sustituye a un profesional de la salud. Ante cualquier preocupación o duda, consulta con tu pediatra.")
        with gr.Row():
            with gr.Column():
                gr.Markdown("<h2>Predictor</h2>")
                boton_predictor = gr.Button("Prueba el predictor")
                gr.Markdown("<p>Descubre por qué llora tu bebé</p>")
            with gr.Column():
                gr.Markdown("<h2>Monitor</h2>")
                boton_monitor = gr.Button("Prueba el monitor")
                gr.Markdown("<p>Monitoriza si tu hijo está llorando y por qué, sin levantarte del sofá</p>")
    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:")
            )
        gr.Button("Volver a la pantalla inicial").click(cambiar_pestaña, outputs=[pag_predictor, chatbot])
    with gr.Column(visible=False) as pag_monitor:
        gr.Markdown("<h2>Monitor</h2>")
        audio_stream = gr.Audio(
                format="wav",
                label="Baby recorder",
                type="filepath",
                streaming=True
            )
        threshold_db = gr.Slider(
            minimum=0,
            maximum=100,
            step=1,
            value=30,
            label="Umbral de dB para activar la predicción"
            )
        audio_stream.stream(
            mostrar_decibelios,
            inputs=[audio_stream, threshold_db],
            outputs=gr.Textbox(value="Esperando...", label="Estado")
            )
        audio_stream.stream(
            predict_stream_decib,
            inputs=[audio_stream, threshold_db],
            outputs=gr.Textbox(value="", label="Tu bebé:")
        )
        gr.Button("Volver a la pantalla inicial").click(cambiar_pestaña, outputs=[pag_monitor, chatbot])
    boton_predictor.click(cambiar_pestaña, outputs=[chatbot, pag_predictor])
    boton_monitor.click(cambiar_pestaña, outputs=[chatbot, pag_monitor])
demo.launch(share=True)