import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import time
import numpy as np
from torch.nn import functional as F
import os
from threading import Thread
token = os.environ["HF_TOKEN"]

model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,token=token)
tok = AutoTokenizer.from_pretrained("google/gemma-2b-it",token=token)
# using CUDA for an optimal experience
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)


start_message = ""

def user(message, history):
    # Append the user's message to the conversation history
    return "", history + [[message, ""]]


def chat(message, history):
    chat = []
    for item in history:
        chat.append({"role": "user", "content": item[0]})
        if item[1] is not None:
            chat.append({"role": "assistant", "content": item[1]})
    chat.append({"role": "user", "content": message})
    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
    # Tokenize the messages string
    model_inputs = tok([messages], return_tensors="pt").to(device)
    streamer = TextIteratorStreamer(
        tok, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=0.75,
        num_beams=1,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        # print(new_text)
        partial_text += new_text
        # Yield an empty string to cleanup the message textbox and the updated conversation history
        yield partial_text



demo = gr.ChatInterface(fn=chat, examples=[["Write me a poem about Machine Learning."]], title="gemma 2b-it")
demo.launch()