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

# Replace with your model repository ID
model_repo_id = "ubiodee/Plutuslearn-Llama-3.2-3B-Instruct"

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_repo_id)

# Load the base model and apply the PEFT adapter
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-3B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, model_repo_id)

# Define the prediction function
def predict(text):
    inputs = tokenizer(text, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_length=100)  # Adjust parameters as needed
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(label="Input Text"),
    outputs=gr.Textbox(label="Model Output"),
    title="My Model Demo",
    description="Test the fine-tuned model hosted on Hugging Face."
)

# Launch the app
demo.launch()