import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
import torch
import spaces

import os
from threading import Thread
from typing import Iterator

# Define quantization configuration
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,  # Specify 4-bit quantization
    bnb_4bit_use_double_quant=True,  # Use double quantization for better efficiency
    bnb_4bit_quant_type="nf4",  # Set the quantization type to NF4
    bnb_4bit_compute_dtype=torch.float16  # Use float16 for computations
)

# Load the tokenizer and quantized model from Hugging Face
model_name = "llSourcell/medllama2_7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load model with quantization
model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             quantization_config=quantization_config, 
                                             device_map="auto")
model.eval()
max_token_length = 4096

@spaces.GPU(duration=15)
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []
    for user, assistant in chat_history:
        conversation.extend(
            [
                {"role": "user", "content": user},
                {"role": "assistant", "content": assistant},
            ]
        )
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    if input_ids.shape[1] > max_token_length:
        input_ids = input_ids[:, -max_token_length:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {max_token_length} tokens.")
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)

# Define the Gradio ChatInterface
chatbot = gr.ChatInterface(
                fn=generate,
                chatbot=gr.Chatbot(
                        height="64vh"
                    ),
                additional_inputs=[
                    gr.Textbox(
                        "Behave as if you are a medical doctor providing answers for patients' clinical questions.",
                        label="System Prompt"
                    )
                ],
                title="Medical QA Chat",
                description="Feel free to ask any question to Medllama2 Chatbot.",
                theme="soft",
                submit_btn="Send",
                retry_btn="Regenerate Response",
                undo_btn="Delete Previous",
                clear_btn="Clear Chat"
)

# Following line is important to queue the messages
chatbot.queue()

# Enable share = True if you want to create a public link for people to use your application
chatbot.launch()