app.py

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

TOKEN = os.environ['TOKEN']


device = "cuda:0" if torch.cuda.is_available() else "cpu"
print(f'[INFO] Using device: {device}')

# Load the pretrained model and tokenizer
MODEL_NAME = "BounharAbdelaziz/Al-Atlas-LLM-0.5B"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=TOKEN)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, token=TOKEN).to(device)

# Fix tokenizer padding
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token  # Set pad token

# Predefined examples
examples = [
    ["الذكاء الاصطناعي هو فرع من علوم الكمبيوتر اللي كيركز"
     , 256, 0.7, 0.9, 150, 8, 1.5],
    ["المستقبل ديال الذكاء الصناعي فالمغرب"
     , 256, 0.7, 0.9, 150, 8, 1.5],
    [" المطبخ المغربي"
     , 256, 0.7, 0.9, 150, 8, 1.5],
    ["الماكلة المغربية كتعتبر من أحسن الماكلات فالعالم"
     , 256, 0.7, 0.9, 150, 8, 1.5],
]

@spaces.GPU
def generate_text(prompt, max_length=256, temperature=0.7, top_p=0.9, top_k=150, num_beams=8, repetition_penalty=1.5):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(
        **inputs, 
        max_length=max_length, 
        temperature=temperature, 
        top_p=top_p, 
        do_sample=True,
        repetition_penalty=repetition_penalty,
        num_beams=num_beams,
        top_k= top_k,
        early_stopping = True,
        pad_token_id=tokenizer.pad_token_id,  # Explicit pad token
        eos_token_id=tokenizer.eos_token_id,  # Explicit eos token
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

if __name__ == "__main__":
    # Create the Gradio interface
    with gr.Blocks() as app:
        gr.Interface(
            fn=generate_text,
            inputs=[
                gr.Textbox(label="Prompt: دخل النص بالدارجة"),
                gr.Slider(8, 4096, value=256, label="Max Length"),
                gr.Slider(0.0, 2, value=0.7, label="Temperature"),
                gr.Slider(0.0, 1.0, value=0.9, label="Top-p"),
                gr.Slider(1, 10000, value=150, label="Top-k"),
                gr.Slider(1, 20, value=8, label="Number of Beams"),
                gr.Slider(0.0, 100.0, value=1.5, label="Repetition Penalty"),
            ],
            outputs=gr.Textbox(label="Generated Text in Moroccan Darija"),
            title="Moroccan Darija LLM",
            description="Enter a prompt and get AI-generated text using our pretrained LLM on Moroccan Darija.",
            examples=examples,
        )
    
    app.launch()