app.py

import streamlit as st
import base64
import io
from huggingface_hub import InferenceClient
from gtts import gTTS
from audiorecorder import audiorecorder
import streamlit_webrtc as webrtc

def recognize_speech(audio_data, show_messages=True):
    recognizer = sr.Recognizer()
    audio_recording = sr.AudioFile(audio_data)

    with audio_recording as source:
        audio = recognizer.record(source)

    try:
        audio_text = recognizer.recognize_google(audio, language="es-ES")
        if show_messages:
            st.subheader("Texto Reconocido:")
            st.write(audio_text)
            st.success("Reconocimiento de voz completado.")
    except sr.UnknownValueError:
        st.warning("No se pudo reconocer el audio. ¿Intentaste grabar algo?")
        audio_text = ""
    except sr.RequestError:
        st.error("No he recibido ningun audio. Por favor, inténtalo de nuevo.")
        audio_text = ""

    return audio_text

def format_prompt(message, history):
    prompt = "<s>"

    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "

    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(audio_text, history, temperature=None, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0):
    client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

    temperature = float(temperature) if temperature is not None else 0.9
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(audio_text, history)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
    response = ""

    for response_token in stream:
        response += response_token.token.text
    
    response = ' '.join(response.split()).replace('</s>', '')
    audio_file = text_to_speech(response, speed=1.3)
    return response, audio_file

def text_to_speech(text, speed=1.3):
    tts = gTTS(text=text, lang='es')
    audio_fp = io.BytesIO()
    tts.write_to_fp(audio_fp)
    audio_fp.seek(0)
    return audio_fp

def detect_vocal_activity(audio_data):
    y, sr = librosa.load(audio_data, sr=None)
    umbral_actividad_vocal = 0.01
    amplitud_media = librosa.feature.rms(y=y)
    actividad_vocal = amplitud_media > umbral_actividad_vocal

    return actividad_vocal


def main():
    if "history" not in st.session_state:
        st.session_state.history = []

    if not audio_data.empty():
        st.audio(audio_data.export().read(), format="audio/wav")
        audio_data.export("audio.wav", format="wav")
        audio_text = recognize_speech("audio.wav")

        if not st.session_state.history:
            pre_prompt = "Te Llamarás Chaman 4.0 y tus respuestas serán sumamente breves."
            output, _ = generate(pre_prompt, history=st.session_state.history)
            st.session_state.history.append((pre_prompt, output))

        if audio_text:
            actividad_vocal = detect_vocal_activity("audio.wav")

            if actividad_vocal.any():
                output, audio_file = generate(audio_text, history=st.session_state.history)

                if audio_text:
                    st.session_state.history.append((audio_text, output))

                if audio_file is not None:
                    st.markdown(
                        f"""
                        <audio autoplay="autoplay" controls="controls" src="data:audio/mp3;base64,{base64.b64encode(audio_file.read()).decode()}" type="audio/mp3" id="audio_player"></audio>
                        """,
                        unsafe_allow_html=True
                    )
            else:
                st.warning("No se detectó actividad vocal.")