import spaces
import json
import subprocess
import os
import sys

def run_command(command):
    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
    output, error = process.communicate()
    if process.returncode != 0:
        print(f"Error executing command: {command}")
        print(error.decode('utf-8'))
        exit(1)
    return output.decode('utf-8')

# Download CUDA installer
download_command = "wget https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
result = run_command(download_command)
if result is None:
    print("Failed to download CUDA installer.")
    exit(1)

# Run CUDA installer in silent mode
install_command = "sh cuda_12.2.0_535.54.03_linux.run --silent --toolkit --samples --override"
result = run_command(install_command)
if result is None:
    print("Failed to run CUDA installer.")
    exit(1)

print("CUDA installation process completed.")

def install_packages():
    
    # Clone the repository with submodules
    run_command("git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git")
    
    # Change to the cloned directory
    os.chdir("llama-cpp-python")
    
    # Checkout the specific commit in the llama.cpp submodule
    os.chdir("vendor/llama.cpp")
    run_command("git checkout 50e0535")
    os.chdir("../..")
    
    # Upgrade pip
    run_command("pip install --upgrade pip")



    # Install all optional dependencies with CUDA support
    run_command('CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DCUDA_PATH=/usr/local/cuda-12.2 -DCUDAToolkit_ROOT=/usr/local/cuda-12.2 -DCUDAToolkit_INCLUDE_DIR=/usr/local/cuda-12/include -DCUDAToolkit_LIBRARY_DIR=/usr/local/cuda-12.2/lib64" FORCE_CMAKE=1 pip install -e .')
    
    run_command("make clean && GGML_OPENBLAS=1 make -j")
        
    # Reinstall the package with CUDA support
    run_command('CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DCUDA_PATH=/usr/local/cuda-12.2 -DCUDAToolkit_ROOT=/usr/local/cuda-12.2 -DCUDAToolkit_INCLUDE_DIR=/usr/local/cuda-12/include -DCUDAToolkit_LIBRARY_DIR=/usr/local/cuda-12.2/lib64" FORCE_CMAKE=1 pip install -e .')

    # Install llama-cpp-agent
    run_command("pip install llama-cpp-agent")
    
    run_command("export PYTHONPATH=$PYTHONPATH:$(pwd)")
    
    print("Installation complete!")

try:
    install_packages()
    
    # Add a delay to allow for package registration
    import time
    time.sleep(5)
    
    # Force Python to reload the site packages
    import site
    import importlib
    importlib.reload(site)
    
    # Now try to import the libraries
    from llama_cpp import Llama
    from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
    from llama_cpp_agent.providers import LlamaCppPythonProvider
    from llama_cpp_agent.chat_history import BasicChatHistory
    from llama_cpp_agent.chat_history.messages import Roles
    
    print("Libraries imported successfully!")
except Exception as e:
    print(f"Installation failed or libraries couldn't be imported: {str(e)}")
    sys.exit(1)
    
import gradio as gr
from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="MaziyarPanahi/Mistral-Nemo-Instruct-2407-GGUF",
    filename="Mistral-Nemo-Instruct-2407.Q5_K_M.gguf",
    local_dir="./models"
)

# Initialize LLM outside the respond function
llm = Llama(
    model_path="models/Mistral-Nemo-Instruct-2407.Q5_K_M.gguf",
    flash_attn=True,
    n_gpu_layers=81,
    n_batch=1024,
    n_ctx=32768,
)

provider = LlamaCppPythonProvider(llm)

@spaces.GPU(duration=120)
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    chat_template = MessagesFormatterType.MISTRAL

    agent = LlamaCppAgent(
        provider,
        system_prompt=f"{system_message}",
        predefined_messages_formatter_type=chat_template,
        debug_output=True
    )
    
    settings = provider.get_provider_default_settings()
    settings.temperature = temperature
    settings.top_k = top_k
    settings.top_p = top_p
    settings.max_tokens = max_tokens
    settings.repeat_penalty = repeat_penalty
    settings.stream = True

    messages = BasicChatHistory()

    for msn in history:
        user = {
            'role': Roles.user,
            'content': msn[0]
        }
        assistant = {
            'role': Roles.assistant,
            'content': msn[1]
        }
        messages.add_message(user)
        messages.add_message(assistant)
    
    stream = agent.get_chat_response(
        message,
        llm_sampling_settings=settings,
        chat_history=messages,
        returns_streaming_generator=True,
        print_output=False
    )
    
    outputs = ""
    for output in stream:
        outputs += output
        yield outputs

description = """<p><center>
<a href="https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407" target="_blank">[Instruct Model]</a>
<a href="https://huggingface.co/mistralai/Mistral-Nemo-Base-2407" target="_blank">[Base Model]</a>
<a href="https://huggingface.co/second-state/Mistral-Nemo-Instruct-2407-GGUF" target="_blank">[GGUF Version]</a>
</center></p>
"""

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max 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",
        ),
        gr.Slider(
            minimum=0,
            maximum=100,
            value=40,
            step=1,
            label="Top-k",
        ),
        gr.Slider(
            minimum=0.0,
            maximum=2.0,
            value=1.1,
            step=0.1,
            label="Repetition penalty",
        ),
    ],
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    title="Chat with Mistral-NeMo using llama.cpp", 
    description=description,
    chatbot=gr.Chatbot(
        scale=1, 
        likeable=False,
        show_copy_button=True
    )
)

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