app.py

# app.py
import torch
import gradio as gr
import spaces  # 👈 Required for ZeroGPU
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

# -------------------------------------------------
# MODEL LOADING
# -------------------------------------------------
BASE_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
ADAPTER_MODEL = "GilbertAkham/deepseek-R1-multitask-lora"

print("🔄 Loading base model and LoRA adapter...")

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,                   # 4-bit quantization for GPU memory efficiency
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="auto",
    quantization_config=bnb_config,
    torch_dtype=torch.float16,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL)
model.eval()

print("✅ Model and tokenizer loaded successfully!")


# -------------------------------------------------
# GPU INFERENCE FUNCTION
# -------------------------------------------------
@spaces.GPU  # 👈 Required for ZeroGPU runtime
def generate_response(message, history, system_message, max_tokens, temperature, top_p):
    """
    Generates text using the multitask LoRA model.
    Supports reasoning, chat, summarization, story continuation, etc.
    """
    prompt = f"{system_message}\n\n"
    for turn in history:
        prompt += f"User: {turn['content']}\nAssistant: {turn.get('response', '')}\n"
    prompt += f"User: {message}\nAssistant:"

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.1,
        )

    text = tokenizer.decode(output[0], skip_special_tokens=True)
    answer = text.split("Assistant:")[-1].strip()

    return answer


# -------------------------------------------------
# GRADIO CHAT INTERFACE
# -------------------------------------------------
chatbot = gr.ChatInterface(
    fn=generate_response,
    type="messages",
    additional_inputs=[
        gr.Textbox(
            value=(
                "You are Chat-Bot, a helpful and logical assistant trained for "
                "reasoning, email, chatting, summarization, story continuation, and report writing."
            ),
            label="🧠 System Message",
        ),
        gr.Slider(64, 2048, value=512, step=16, label="📏 Max New Tokens"),
        gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="🌡️ Temperature"),
        gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="🎯 Top-p"),
    ],
)


# -------------------------------------------------
# UI LAYOUT
# -------------------------------------------------
with gr.Blocks(title="Gilbert Multitask Reasoning AI") as demo:
    with gr.Sidebar():
        gr.Markdown("## 💡 About This App")
        gr.Markdown(
            """
            - **Model:** `GilbertAkham/deepseek-R1-multitask-lora`  
            - **Base:** `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`  
            - **Capabilities:**  
              🧩 Reasoning, 🗣️ Chat, 📧 Email writing, 📚 Summarization, ✍️ Story continuation, 🧾 Report generation  
            - **ZeroGPU Enabled:** GPU spins up only when generating responses.
            """
        )
    chatbot.render()


# -------------------------------------------------
# LAUNCH
# -------------------------------------------------
if __name__ == "__main__":
    demo.launch()