app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_map = {
    "Tamil": "Harisanth/mbart-chatbot-tamil",
    "Sinhala": "Harisanth/mbart-chatbot-sinhala",
    "English": "Harisanth/mbart-chatbot-english",
    "Tanglish": "Harisanth/mbart-chatbot-tanglish"
}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def chat_fn(text, lang):
    repo = model_map[lang]
    tok = AutoTokenizer.from_pretrained(repo)
    mod = AutoModelForCausalLM.from_pretrained(repo).to(device)
    tok.src_lang = {'Tamil':'ta_IN','Sinhala':'si_LK','English':'en_XX','Tanglish':'en_XX'}[lang]
    inp = tok(text, return_tensors="pt").to(device)
    out = mod.generate(**inp, max_length=100, forced_bos_token_id=tok.lang_code_to_id[tok.src_lang])
    return tok.decode(out[0], skip_special_tokens=True)

iface = gr.Interface(
    fn=chat_fn,
    inputs=["text", gr.Radio(["Tamil","Sinhala","English","Tanglish"], label="Language")],
    outputs="text",
    title="Multilingual Chatbot",
    description="A fine-tuned mBART chatbot by Harisanth"
)

if __name__ == "__main__":
    iface.launch()