# -*- coding: utf-8 -*-
"""Medllama use.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1pZiJn21DK8U77WfKyxw94zNVYnxR40LP
"""

#!pip install transformers accelerate peft bitsandbytes gradio

from huggingface_hub import notebook_login
import torch
notebook_login()

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

config = PeftConfig.from_pretrained("tmberooney/medllama")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf",load_in_4bit=True, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, "tmberooney/medllama")
tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = model.to('cuda:0')





"""### Using Gradio App"""

from transformers import pipeline

llama_pipeline = pipeline(
    "text-generation",  # LLM task
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
    tokenizer=tokenizer
)

SYSTEM_PROMPT = """<s>[INST] <<SYS>>
You are a helpful medical bot. Your answers are clear and concise with medical information.
<</SYS>>

"""

# Formatting function for message and history
def format_message(message: str, history: list, memory_limit: int = 3) -> str:
    """
    Formats the message and history for the Llama model.

    Parameters:
        message (str): Current message to send.
        history (list): Past conversation history.
        memory_limit (int): Limit on how many past interactions to consider.

    Returns:
        str: Formatted message string
    """
    # always keep len(history) <= memory_limit
    if len(history) > memory_limit:
        history = history[-memory_limit:]

    if len(history) == 0:
        return SYSTEM_PROMPT + f"{message} [/INST]"

    formatted_message = SYSTEM_PROMPT + f"{history[0][0]} [/INST] {history[0][1]} </s>"

    # Handle conversation history
    for user_msg, model_answer in history[1:]:
        formatted_message += f"<s>[INST] {user_msg} [/INST] {model_answer} </s>"

    # Handle the current message
    formatted_message += f"<s>[INST] {message} [/INST]"

    return formatted_message

from transformers import TextIteratorStreamer
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)

# Generate a response from the Llama model
def get_model_response(message: str, history: list) -> str:
    """
    Generates a conversational response from the Llama model.

    Parameters:
        message (str): User's input message.
        history (list): Past conversation history.

    Returns:
        str: Generated response from the Llama model.
    """
    query = format_message(message, history)
    response = ""

    sequences = llama_pipeline(
        query,
        generation_config = model.generation_config,
        do_sample=True,
        top_k=10,
        streamer=streamer,
        top_p=0.7,
        temperature=0.7,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        max_length=1024,
    )

    generated_text = sequences[0]['generated_text']
    response = generated_text[len(query):]  # Remove the prompt from the output

    partial_message  = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message

import gradio as gr

gr.ChatInterface(fn=get_model_response,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
                 title="Medllama : The Medically Fine-tuned LLaMA-2").queue().launch()

!gradio deploy