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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() |