app.py

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = f"alimrb/eff24"
config = PeftConfig.from_pretrained(peft_model_id) 
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

def make_inference(question, answer):
    batch = tokenizer(
        f"### Question:\n{question}\n\n### Answer:",
        return_tensors="pt",
    )

    # Move batch to the same device as the model
    batch = {k: v.to(model.device) for k, v in batch.items()}

    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, max_new_tokens=50)

    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)

if __name__ == "__main__":
    # Create a Gradio interface
    import gradio as gr
    
    gr.Interface(
        make_inference,
        [
            gr.Textbox(lines=2, label="Question"),
        ],
        gr.Textbox(label="Answer"),
        title="EFF24",
        description="EFF24 is a generative model that generates Answers for Questions."
    ).launch()