import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# 1. Configuration
MODEL_ID = "ConceptModels/Concept-7b-V1-Full"

# 2. Load Model and Tokenizer (Done once at startup)
print(f"Loading {MODEL_ID}... this may take a while.")

try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    
    # Attempt to use GPU if available, otherwise CPU
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Running on device: {device}")

    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32,
        device_map="auto" if device == "cuda" else None,
        # Uncomment the line below to use 4-bit quantization (requires pip install bitsandbytes)
        # load_in_4bit=True 
    )
    # If using CPU, move model explicitly
    if device == "cpu":
        model.to("cpu")
        
    print("Model loaded successfully.")

except Exception as e:
    print(f"Error loading model: {e}")
    raise e

def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token=None, # Not strictly needed for local if logged in via CLI, but kept for signature compatibility
):
    # 3. Format the conversation
    # We construct the list of messages including system, history, and current input
    messages = [{"role": "system", "content": system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": message})

    # Apply the model's specific chat template
    input_ids = tokenizer.apply_chat_template(
        messages, 
        return_tensors="pt", 
        add_generation_prompt=True
    ).to(model.device)

    # 4. Setup Streaming
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
    )

    # 5. Run generation in a separate thread so we can yield tokens
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    # 6. Yield output as it generates
    partial_message = ""
    for new_token in streamer:
        partial_message += new_token
        yield partial_message

# 7. Gradio Interface
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="You are an AI called Concept. You are made for programming in any type of code.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

with gr.Blocks() as demo:
    # Removed LoginButton because local execution usually relies on environment login
    # or public models.
    chatbot.render()

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