import subprocess
import os
import warnings

warnings.filterwarnings("ignore", message="The installed version of bitsandbytes was compiled without GPU support")
warnings.filterwarnings("ignore", message="Setting share=True is not supported on Hugging Face Spaces")


# Install packages using subprocess
subprocess.run(["pip", "install", "transformers", "torch"], check=True)

import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import List, Optional, Tuple, Dict

# Set up default system prompt and history/message structures
default_system = 'You are Andrej Karpathy, the most helpful coding assistant and solves everything with working code.'
History = List[Tuple[str, str]]
Messages = List[Dict[str, str]]

# Clear the chat session
def clear_session() -> History:
    return '', []

# Modify the chat system's prompt
def modify_system_session(system: str) -> Tuple[str, str, History]:
    if not system:
        system = default_system
    return system, system, []

# Convert the history of messages into a format suitable for model input
def history_to_messages(history: History, system: str) -> Messages:
    messages = [{'role': 'system', 'content': system}]
    for user_msg, assistant_msg in history:
        messages.append({'role': 'user', 'content': user_msg})
        messages.append({'role': 'assistant', 'content': assistant_msg})
    return messages

# Convert messages back into a history format after model processing
def messages_to_history(messages: Messages) -> Tuple[str, History]:
    system = messages[0]['content']
    history = [(msg['content'], messages[i+1]['content']) for i, msg in enumerate(messages[1:-1:2])]
    return system, history

# Handle the chat interaction, including processing the input and generating a response
def model_chat(query: str, history: History, system: str) -> Tuple[str, History, str]:
    messages = history_to_messages(history, system)
    messages.append({'role': 'user', 'content': query})

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model_name = "codeqwen1.5-7b-chat"

    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

    inputs = tokenizer(query, return_tensors="pt").to(device)
    outputs = model.generate(inputs.input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    system, history = messages_to_history(messages + [{'role': 'assistant', 'content': response}])

    return '', history, system

# Setting up the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("""<center><font size=8>CodeQwen1.5-7B-Chat Bot👾</center>""")

    with gr.Row():
        with gr.Column(scale=3):
            system_input = gr.Textbox(value=default_system, lines=1, label='System')
        with gr.Column(scale=1):
            modify_system = gr.Button("🛠️ Set system prompt and clear history", scale=2)
        system_state = gr.State(value=default_system)
    chatbot = gr.Chatbot(label='codeqwen1.5-7b-chat')
    textbox = gr.Textbox(lines=2, label='Input')

    with gr.Row():
        clear_history = gr.Button("🧹 Clear History")
        submit = gr.Button("🚀 Send")

    submit.click(model_chat,
                 inputs=[textbox, chatbot, system_state],
                 outputs=[textbox, chatbot, system_state])
    clear_history.click(fn=clear_session,
                        inputs=[],
                        outputs=[textbox, chatbot])
    modify_system.click(fn=modify_system_session,
                        inputs=[system_input],
                        outputs=[system_state, system_input, chatbot])

demo.queue(api_open=False)
demo.launch(max_threads=30)