app.py

import gradio as gr
from llama_cpp import Llama
from transformers import AutoTokenizer


MODEL_REPO = "simonper/Llama-3.2-1B-bnb-4bit_untrained_gguf_4bit"
MODEL_FILE = "Llama-3.2-1B.Q4_K_M.gguf"


TOKENIZER_ID = "chthees/lora_model_full_finetome-tokenizer" 

print("Loading Tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_ID)

print("Loading Model...")
llm = Llama.from_pretrained(
    repo_id=MODEL_REPO,
    filename=MODEL_FILE,
    n_ctx=2048,
    n_threads=2,
    verbose=False
)

# --- SYSTEM PROMPT LOGIC ---
def get_system_prompt(style_mode):
    base_instruction = "You are a helpful and intelligent AI assistant."
    
    prompts = {
        "Normal": f"{base_instruction} Answer clearly and concisely.",
        "Professional": (
            f"{base_instruction} You are a senior corporate executive. "
            "Your tone is strictly professional, polite, and business-oriented."
        ),
        "Shakespeare": (
            f"{base_instruction} You are William Shakespeare. "
            "Speak only in Early Modern English (thee, thou, hath). Be poetic and dramatic."
        ),
        "Funny/Ironic": (
            f"{base_instruction} You are a sarcastic comedian. "
            "Wrap your answers in dry humor, irony, and witty remarks."
        )
    }
    return prompts.get(style_mode, prompts["Normal"])

# --- CORE RESPONSE FUNCTION ---
def respond(
    message,
    history: list[dict],
    system_message_dummy,
    max_tokens,
    temperature,
    top_p,
    repetition_penalty,
    style_mode,
):
    messages = []
    
    # Add System Persona
    system_prompt = get_system_prompt(style_mode)
    messages.append({"role": "system", "content": system_prompt})

    # Add Conversation History
    # We slice to the last 10 turns to keep the context window manageable
    for turn in history[-10:]:
        messages.append({"role": turn['role'], "content": turn['content']})

    # Add Current User Message
    messages.append({"role": "user", "content": message})

    prompt_str = tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
    )

    # 3. Generate Response
    output = llm(
        prompt_str,
        max_tokens=int(max_tokens),
        temperature=float(temperature),
        top_p=float(top_p),
        repeat_penalty=float(repetition_penalty),
        stop=[tokenizer.eos_token, "<|eot_id|>"],
        echo=False
    )

    return output["choices"][0]["text"].strip()

# --- GUI SETUP ---
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="", label="System Prompt (Hidden)", visible=False),
        gr.Slider(minimum=1, maximum=1024, value=512, label="Max New Tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, label="Top-p"),
        gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.05, label="Repetition Penalty"),
        gr.Dropdown(
            choices=["Normal", "Professional", "Shakespeare", "Funny/Ironic"],
            value="Normal",
            label="Choose the Style / Tone"
        )
    ],
)

with gr.Blocks() as demo:
    gr.Markdown("# Styled Chat Bot")
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()

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