import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from gtts import gTTS
import tempfile
from langdetect import detect

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")

def respond(user_input):
    if not user_input:
        return "Please enter a question.", None

    # Detect language
    try:
        detected_lang = detect(user_input)
    except:
        detected_lang = "en"

    # Generate AI response
    prompt = f"[INST] {user_input} [/INST]"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=256, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Convert to speech
    try:
        tts = gTTS(text=response, lang='hi' if detected_lang == 'hi' else 'en')
        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
            tts.save(fp.name)
            audio_path = fp.name
    except Exception as e:
        audio_path = None

    return response, audio_path

# UI
iface = gr.Interface(
    fn=respond,
    inputs=gr.Textbox(lines=2, placeholder="Ask anything...", label="user_input"),
    outputs=[
        gr.Textbox(label="TeachMe Says"),
        gr.Audio(label="Voice", type="filepath", autoplay=True)
    ],
    title="TeachMe - Your Smart Tutor",
    description="Light AI bot with Hindi + English voice support."
)

iface.launch()