app.py

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

# Load tokenizer dan model DeepSeek
model_name = "deepseek-ai/deepseek-coder-1.3b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

def generate_code(prompt, max_tokens):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=max_tokens, temperature=0.7)
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_code[len(prompt):]  # potong prompt awal

with gr.Blocks() as demo:
    gr.Markdown("# 🚀 DeepSeek Code Generator")
    with gr.Row():
        prompt_input = gr.Textbox(label="Masukkan prompt (instruksi)", placeholder="Contoh: Buatkan fungsi Python untuk menghitung faktorial")
        token_slider = gr.Slider(32, 1024, value=256, label="Jumlah token maksimum")
    with gr.Row():
        generate_button = gr.Button("🔍 Generate Code")
        output = gr.Code(label="Output Kode")

    generate_button.click(fn=generate_code, inputs=[prompt_input, token_slider], outputs=output)

demo.launch()