app.py

import gradio as gr
import torch
import tempfile
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
import whisper
from TTS.api import TTS
from langdetect import detect

# Modelos
chat_model_id = "google/flan-t5-small"
chat_tokenizer = AutoTokenizer.from_pretrained(chat_model_id)
chat_model = AutoModelForSeq2SeqLM.from_pretrained(chat_model_id)
chat_pipe = pipeline("text2text-generation", model=chat_model, tokenizer=chat_tokenizer)

tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
whisper_model = whisper.load_model("base")

corr_model_id = "prithivida/grammar_error_correcter_v1"
corr_tokenizer = AutoTokenizer.from_pretrained(corr_model_id)
corr_model = AutoModelForSeq2SeqLM.from_pretrained(corr_model_id)

def detectar_idioma(texto):
    try:
        return detect(texto)
    except:
        return "en"

def corregir_gramatica(texto, idioma):
    if idioma == "auto":
        idioma = detectar_idioma(texto)
    if idioma != "en":
        return texto, idioma
    input_ids = corr_tokenizer.encode(texto, return_tensors="pt")
    output_ids = corr_model.generate(input_ids, max_length=128, num_beams=5, early_stopping=True)
    corrected = corr_tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return corrected, idioma

# 👇 Función unificada
def chatbot_con_voz(audio_input, text_input,history, idioma_opcion, nivel):
    if audio_input:
        transcription = whisper_model.transcribe(audio_input)["text"]
    elif text_input:
        transcription = text_input
    else:
        return "❗ Debes ingresar texto o voz.", None, "<div id='history-output'></div>"

    texto_corregido, idioma_detectado = corregir_gramatica(transcription, idioma_opcion)

    prompt = f"Level: {nivel}\nInstruction: {texto_corregido}\nResponse:"
    resultado = chat_pipe(prompt, max_new_tokens=150)[0]["generated_text"]
    respuesta = resultado.replace(prompt, "").strip()

    output_path = tempfile.mktemp(suffix=".wav")
    tts.tts_to_file(text=respuesta, file_path=output_path)

    resumen = f"""🎤 **You said:** {transcription}
🌍 **Language detected:** `{idioma_detectado}`
✅ **Corrected:** {texto_corregido}

🧠 **Response:** {respuesta}"""

    return resumen, output_path, "<div id='history-output'></div>"

# Interfaz Gradio
iface = gr.Interface(
    fn=chatbot_con_voz,
    inputs=[
        gr.Audio(sources="microphone", type="filepath", label="🎙️ Speak now (optional)"),
        gr.Textbox(label="✍️ Or type your message"),
        gr.Dropdown(["auto", "en", "it"], label="Language", value="auto"),
        gr.Dropdown(["Greetings", "Colors", "Daily Activities", "Verbs"], label="Level", value="Greetings")
    ],
    outputs=[
        gr.Textbox(label="📝 Transcription & Correction"),
        gr.Audio(label="🔊 Bot Response"),
        gr.HTML(label="📚 Previous Corrections")
    ],
    title="🗣️📝 Language Teacher (Voice & Text)",
    description="Speak or write in English/Italian. The bot corrects, replies with voice, and tracks your learning.",
    theme="soft",
    allow_flagging="never",
    js="static/script.js"

)

iface.launch()