app.py

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

# Load the Unsloth Phi-4 GGUF model and tokenizer
model_name = "unsloth/phi-4-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Function to generate code based on user input
def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=150)
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_code

# Gradio interface
interface = gr.Interface(
    fn=generate_code,
    inputs=gr.Textbox(lines=5, label="Prompt"),
    outputs=gr.Code(language="python", label="Generated Code"),
    title="Code Generator with Unsloth Phi-4 GGUF",
    description="Enter a prompt to generate Python code using the Unsloth Phi-4 GGUF model."
)

if __name__ == "__main__":
    interface.launch()