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

# # Model initialization
# model_name = "gpt2"  # or your specific model
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# model = AutoModelForCausalLM.from_pretrained(model_name)

# # Add special tokens for languages
# special_tokens = {"additional_special_tokens": ["[YORUBA]", "[IGBO]", "[HAUSA]"]}
# tokenizer.add_special_tokens(special_tokens)
# model.resize_token_embeddings(len(tokenizer))

# # Move model to GPU if available
# device = "cuda" if torch.cuda.is_available() else "cpu"
# model.to(device)

# def generate_text(prompt, language):
#     # Add language tag to prompt
#     tagged_prompt = f"[{language.upper()}] {prompt}"
    
#     # Tokenize
#     inputs = tokenizer(tagged_prompt, return_tensors="pt").to(device)
    
#     # Generate
#     outputs = model.generate(
#         **inputs,
#         max_length=100,
#         num_return_sequences=1,
#         temperature=0.7,
#         pad_token_id=tokenizer.eos_token_id
#     )
    
#     # Decode and return
#     return tokenizer.decode(outputs[0], skip_special_tokens=True)

# # Create Gradio interface
# iface = gr.Interface(
#     fn=generate_text,
#     inputs=[
#         gr.Textbox(label="Enter your prompt"),
#         gr.Dropdown(choices=["YORUBA", "IGBO", "HAUSA"], label="Select Language")
#     ],
#     outputs=gr.Textbox(label="Generated Text"),
#     title="Nigerian Language Generator",
#     description="Generate text in Yoruba, Igbo, or Hausa using a fine-tuned GPT model."
# )

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


import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from model.config import ModelConfig
from model.model import NigerianLanguageModel
from interface.gradio_app import create_interface

def main():
    config = ModelConfig()
    model = NigerianLanguageModel(config)
    interface = create_interface(model)
    interface.launch()

if __name__ == "__main__":
    main()