import gradio as gr
import boto3
import sagemaker
import json
import io
import os
from transformers import AutoTokenizer
from huggingface_hub import login


region = os.getenv("region")
sm_endpoint_name = os.getenv("sm_endpoint_name")
access_key = os.getenv("access_key")
secret_key = os.getenv("secret_key")
hf_token = os.getenv("hf_read_access")
HF_TOKEN = os.getenv('HF_TOKEN')
print("hf_token",hf_token)
print("HF_TOKEN",HF_TOKEN)

session = boto3.Session(
    aws_access_key_id=access_key,
    aws_secret_access_key=secret_key,
    region_name=region
)
sess = sagemaker.Session(boto_session=session)

smr = session.client("sagemaker-runtime")

DEFAULT_SYSTEM_PROMPT = (
    "You are an helpful, concise and direct Assistant."
)

# load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2",token=hf_token)


MAX_INPUT_TOKEN_LENGTH = 256

# hyperparameters for llm
parameters = {
    "do_sample": True,
    "top_p": 0.6,
    "temperature": 0.9,
    "max_new_tokens": 768,
    "repetition_penalty": 1.2,
    "return_full_text": False,
    "stop": ["</s>"],
}


# Helper for reading lines from a stream
class LineIterator:
    def __init__(self, stream):
        self.byte_iterator = iter(stream)
        self.buffer = io.BytesIO()
        self.read_pos = 0

    def __iter__(self):
        return self

    def __next__(self):
        while True:
            self.buffer.seek(self.read_pos)
            line = self.buffer.readline()
            if line and line[-1] == ord("\n"):
                self.read_pos += len(line)
                return line[:-1]
            try:
                chunk = next(self.byte_iterator)
            except StopIteration:
                if self.read_pos < self.buffer.getbuffer().nbytes:
                    continue
                raise
            if "PayloadPart" not in chunk:
                print("Unknown event type:" + chunk)
                continue
            self.buffer.seek(0, io.SEEK_END)
            self.buffer.write(chunk["PayloadPart"]["Bytes"])


def format_prompt(message, history):
    '''
    messages = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}]
    for interaction in history:
        messages.append({"role": "user", "content": interaction[0]})
        messages.append({"role": "assistant", "content": interaction[1]})
    messages.append({"role": "user", "content": message})
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    '''

    messages = [
    {"role": "user", "content": message},]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 
    return prompt


def generate(
    prompt,
    history,
):
    formatted_prompt = format_prompt(prompt, history)
    check_input_token_length(formatted_prompt)

    request = {"inputs": formatted_prompt, "parameters": parameters, "stream": True}


    resp = smr.invoke_endpoint_with_response_stream(
        EndpointName=sm_endpoint_name,
        Body=json.dumps(request),
        ContentType="application/json",
    )
    output = ""
    for c in LineIterator(resp["Body"]):
        c = c.decode("utf-8")
        if c.startswith("data:"):
            chunk = json.loads(c.lstrip("data:").rstrip("/n"))
            if chunk["token"]["special"]:
                continue
            if chunk["token"]["text"] in request["parameters"]["stop"]:
                break
            output += chunk["token"]["text"]
            for stop_str in request["parameters"]["stop"]:
                if output.endswith(stop_str):
                    output = output[: -len(stop_str)]
                    output = output.rstrip()
                    yield output

            yield output
    return output


def check_input_token_length(prompt: str) -> None:
    input_token_length = len(tokenizer(prompt)["input_ids"])
    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
        raise gr.Error(
            f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again."
        )


theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[
        gr.themes.GoogleFont("Open Sans"),
        "ui-sans-serif",
        "system-ui",
        "sans-serif",
    ],
)

DESCRIPTION = """
<div style="text-align: center; max-width: 650px; margin: 0 auto; display:grid; gap:25px;">
    <img class="logo" src="https://huggingface.co/datasets/philschmid/assets/resolve/main/aws-neuron_hf.png" alt="Hugging Face Neuron Logo"
        style="margin: auto; max-width: 14rem;">        
    <h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px">
        Mistral-7B-Instruct-v0.2 Chat on AWS INF2 ⚡
    </h1> 
</div>
"""

demo = gr.ChatInterface(
    generate,
    description=DESCRIPTION,
    chatbot=gr.Chatbot(layout="panel"),
    theme=theme,
)

demo.queue().launch(share=False)