app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
model_name = "unsloth/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the generation function
def generate_response(prompt):
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs,
        max_length=512,
        num_return_sequences=1,
        do_sample=True,
        temperature=0.7,
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Create the Gradio interface
interface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here..."),
    outputs=gr.Textbox(label="Generated Response"),
    title="Llama-3.2-1B-Instruct Model",
    description="A simple interface to interact with the Llama-3.2-1B-Instruct model.",
)

# Launch the app
if __name__ == "__main__":
    interface.launch()