app.py

from ctransformers import AutoModelForCausalLM
import gradio as gr
import os
from huggingface_hub import login
import re

login(token=os.getenv("HF_TOKEN"))


if not os.path.exists("model.gguf"):
    os.system("wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf -O model.gguf")
    print("model.gguf downloaded.")

# Load GGUF model with ctransformers
llm = AutoModelForCausalLM.from_pretrained(
    "./",
    model_file="model.gguf",
    model_type="llama",
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9
)

# Clean and generate response with multi-language code formatting

def clean_response(raw):
    raw = re.sub(r"[{}]+", "", raw)
    raw = raw.replace("\\n", "\n")  # properly format newlines
    raw = raw.strip()

    if raw.startswith("````"):
        return raw

    language_blocks = [
        ("python", ["def ", "print(", "class ", "import ", "return "]),
        ("javascript", ["console.log(", "function ", "let ", "const "]),
        ("bash", ["#!/bin/bash", "sudo ", "echo ", "apt-get"]),
        ("sql", ["SELECT ", "FROM ", "WHERE "]),
        ("java", ["public static void main", "System.out.println"])
    ]

    for lang, keywords in language_blocks:
        if any(kw in raw for kw in keywords):
            return f"```{lang}\n{raw}\n```"

    return raw

def chat(prompt):
    if not prompt.strip():
        return ""
    wrapped_prompt = (
        "You are Fermata-LightCoder, a helpful, fast, and concise AI coding assistant created by Rangga Fermata. "
        "You specialize in writing and explaining programming code, shell scripts, algorithms, and small dev utilities. "
        "When the user asks in another language like Indonesian, respond completely in that language.\n\n"
        f"User: {prompt}\nAssistant:"
    )
    try:
        response = llm(wrapped_prompt)
        return clean_response(str(response))
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Gradio UI for Fermata-LightCoder

demo = gr.Interface(
    fn=chat,
    inputs=gr.Textbox(lines=5, label="Ask Fermata-LightCoder"),
    outputs=gr.Markdown(label="Fermata-LightCoder Says:"),
    title="🧠 Fermata-LightCoder (TinyLlama 1.1B via ctransformers)",
    description="A compact, CPU-optimized AI assistant that specializes in generating code and scripts. Powered by TinyLlama GGUF + ctransformers.",
    allow_flagging="never"
)


# Launch App
if __name__ == "__main__":
    demo.launch(mcp_server=True)