app.py

import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download

# 1. Download Model Koding Terbaik (Qwen2.5-Coder 7B)
# Kita pakai versi GGUF Q4_K_M agar muat di RAM gratisan dan cepat
print("Sedang mendownload model... Mohon tunggu.")
model_path = hf_hub_download(
    repo_id="Qwen/Qwen2.5-Coder-7B-Instruct-GGUF",
    filename="qwen2.5-coder-7b-instruct-q4_k_m.gguf"
)

# 2. Load Model ke Memory (CPU)
llm = Llama(
    model_path=model_path,
    n_ctx=8192,      # Context window besar untuk kode panjang
    n_threads=2,     # Menggunakan 2 core CPU
    verbose=False
)

# 3. Fungsi Chat
def chat_response(message, history):
    # Format prompt agar sesuai standar Qwen
    system_prompt = "You are an expert coding assistant. Write clean, professional code."
    prompt_str = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
    
    for user_input, ai_output in history:
        prompt_str += f"<|im_start|>user\n{user_input}<|im_end|>\n<|im_start|>assistant\n{ai_output}<|im_end|>\n"
    
    prompt_str += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
    
    # Generate jawaban stream (agar teks muncul satu-satu tidak nge-lag)
    output_stream = llm(
        prompt_str,
        max_tokens=2048,
        stop=["<|im_end|>"],
        stream=True
    )
    
    partial_message = ""
    for chunk in output_stream:
        delta = chunk['choices'][0]['text']
        partial_message += delta
        yield partial_message

# 4. Tampilan Web (Mobile Friendly)
custom_css = """
footer {visibility: hidden} 
"""

demo = gr.ChatInterface(
    fn=chat_response, 
    title="🤖 Qwen Android Coder",
    description="Asisten Koding Profesional untuk Android. Copy kode & jalankan di Termux/Acode.",
    theme=gr.themes.Soft(),
    css=custom_css
)

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