import os
from transformers import AutoTokenizer, AutoConfig
from optimum.intel.openvino import OVModelForCausalLM
from generation_utils import run_generation, estimate_latency, reset_textbox,get_special_token_id
from config import SUPPORTED_LLM_MODELS
import gradio as gr
from threading import Thread
from time import perf_counter
from typing import List
from transformers import AutoTokenizer, TextIteratorStreamer
import numpy as np
import os
from flask import Flask, render_template, redirect, url_for, request, flash
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
from werkzeug.security import generate_password_hash, check_password_hash

app = Flask(__name__)
app.config['SECRET_KEY'] = 'your_secret_key'
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///users.db'
db = SQLAlchemy(app)
login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = 'login'

class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False)
    email = db.Column(db.String(120), unique=True, nullable=False)

    def __repr__(self):
        return '<User %r>' % self.username

# Create the database tables
with app.app_context():
    db.create_all()

@login_manager.user_loader
def load_user(user_id):
    return User.query.get(int(user_id))

@app.route('/signup', methods=['GET', 'POST'])
def signup():
    if request.method == 'POST':
        username = request.form['username']
        password = request.form['password']
        hashed_password = generate_password_hash(password, method='sha256')

        new_user = User(username=username, password=hashed_password)
        db.session.add(new_user)
        db.session.commit()
        flash('Signup successful!', 'success')
        return redirect(url_for('login'))

    return render_template('signup.html')

@app.route('/login', methods=['GET', 'POST'])
def login():
    if request.method == 'POST':
        username = request.form['username']
        password = request.form['password']
        user = User.query.filter_by(username=username).first()
        if user and check_password_hash(user.password, password):
            login_user(user)
            return redirect(url_for('dashboard'))
        flash('Invalid username or password', 'danger')

    return render_template('login.html')

@app.route('/dashboard')
@login_required
def dashboard():
    return render_template('dashboard.html', name=current_user.username)

@app.route('/logout')
@login_required
def logout():
    logout_user()
    return redirect(url_for('login'))

if __name__ == '__main__':
    app.run(debug=True)
model_dir = "C:/Users/KIIT/OneDrive/Desktop/INTEL/phi-2/INT8_compressed_weights"
print(f"Checking model directory: {model_dir}")
print(f"Contents: {os.listdir(model_dir)}")  # Check contents of the directory

print(f"Loading model from {model_dir}")


model_name = "susnato/phi-2"
model_configuration = SUPPORTED_LLM_MODELS["phi-2"]
ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}

tok = AutoTokenizer.from_pretrained(model_name)

ov_model = OVModelForCausalLM.from_pretrained(
    model_dir,
    device="CPU",
    ov_config=ov_config,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer_kwargs = model_configuration.get("toeknizer_kwargs", {})
# Continue with your tokenizer usage
response_key = model_configuration.get("response_key")
tokenizer_response_key = None

def get_special_token_id(tokenizer: AutoTokenizer, key: str) -> int:
    """
    Gets the token ID for a given string that has been added to the tokenizer as a special token.

    Args:
        tokenizer (PreTrainedTokenizer): the tokenizer
        key (str): the key to convert to a single token

    Raises:
        ValueError: if more than one ID was generated

    Returns:
        int: the token ID for the given key
    """
    token_ids = tokenizer.encode(key)
    if len(token_ids) > 1:
        raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
    return token_ids[0]
if response_key is not None:
    tokenizer_response_key = next(
        (token for token in tokenizer.additional_special_tokens if token.startswith(response_key)),
        None,
    )

end_key_token_id = None
if tokenizer_response_key:
    try:
        end_key = model_configuration.get("end_key")
        if end_key:
            end_key_token_id =get_special_token_id(tokenizer, end_key)
        # Ensure generation stops once it generates "### End"
    except ValueError:
        pass

prompt_template = model_configuration.get("prompt_template", "{instruction}")
end_key_token_id = end_key_token_id or tokenizer.eos_token_id
pad_token_id = end_key_token_id or tokenizer.pad_token_id

def estimate_latency(
    current_time: float,
    current_perf_text: str,
    new_gen_text: str,
    per_token_time: List[float],
    num_tokens: int,
):
    """
    Helper function for performance estimation

    Parameters:
      current_time (float): This step time in seconds.
      current_perf_text (str): Current content of performance UI field.
      new_gen_text (str): New generated text.
      per_token_time (List[float]): history of performance from previous steps.
      num_tokens (int): Total number of generated tokens.

    Returns:
      update for performance text field
      update for a total number of tokens
    """
    num_current_toks = len(tokenizer.encode(new_gen_text))
    num_tokens += num_current_toks
    per_token_time.append(num_current_toks / current_time)
    if len(per_token_time) > 10 and len(per_token_time) % 4 == 0:
        current_bucket = per_token_time[:-10]
        return (
            f"Average generation speed: {np.mean(current_bucket):.2f} tokens/s. Total generated tokens: {num_tokens}",
            num_tokens,
        )
    return current_perf_text, num_tokens
def run_generation(
    user_text: str,
    top_p: float,
    temperature: float,
    top_k: int,
    max_new_tokens: int,
    perf_text: str,
):
    """
    Text generation function

    Parameters:
      user_text (str): User-provided instruction for a generation.
      top_p (float):  Nucleus sampling. If set to < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for a generation.
      temperature (float): The value used to module the logits distribution.
      top_k (int): The number of highest probability vocabulary tokens to keep for top-k-filtering.
      max_new_tokens (int): Maximum length of generated sequence.
      perf_text (str): Content of text field for printing performance results.
    Returns:
      model_output (str) - model-generated text
      perf_text (str) - updated perf text filed content
    """

    # Prepare input prompt according to model expected template
    prompt_text = prompt_template.format(instruction=user_text)

    # Tokenize the user text.
    model_inputs = tokenizer(prompt_text, return_tensors="pt", **tokenizer_kwargs)

    # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
    # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        temperature=float(temperature),
        top_k=top_k,
        eos_token_id=end_key_token_id,
        pad_token_id=pad_token_id,
    )
    t = Thread(target=ov_model.generate, kwargs=generate_kwargs)
    t.start()

    # Pull the generated text from the streamer, and update the model output.
    model_output = ""
    per_token_time = []
    num_tokens = 0
    start = perf_counter()
    for new_text in streamer:
        current_time = perf_counter() - start
        model_output += new_text
        perf_text, num_tokens = estimate_latency(current_time, perf_text, new_text, per_token_time, num_tokens)
        yield model_output, perf_text
        start = perf_counter()
    return model_output, perf_text
def reset_textbox(instruction: str, response: str, perf: str):
    """
    Helper function for resetting content of all text fields

    Parameters:
      instruction (str): Content of user instruction field.
      response (str): Content of model response field.
      perf (str): Content of performance info filed

    Returns:
      empty string for each placeholder
    """
    return "", "", ""



examples = [
    "Give me a recipe for pizza with pineapple",
    "Write me a tweet about the new OpenVINO release",
    "Explain the difference between CPU and GPU",
    "Give five ideas for a great weekend with family",
    "Do Androids dream of Electric sheep?",
    "Who is Dolly?",
    "Please give me advice on how to write resume?",
    "Name 3 advantages to being a cat",
    "Write instructions on how to become a good AI engineer",
    "Write a love letter to my best friend",
]

def main():
    with gr.Blocks() as demo:
        gr.Markdown(
            "# Question Answering with Model and OpenVINO.\n"
            "Provide instruction which describes a task below or select among predefined examples and model writes response that performs requested task."
        )

        with gr.Row():
            with gr.Column(scale=4):
                user_text = gr.Textbox(
                    placeholder="Write an email about an alpaca that likes flan",
                    label="User instruction",
                )
                model_output = gr.Textbox(label="Model response", interactive=False)
                performance = gr.Textbox(label="Performance", lines=1, interactive=False)
                with gr.Column(scale=1):
                    button_clear = gr.Button(value="Clear")
                    button_submit = gr.Button(value="Submit")
                gr.Examples(examples, user_text)
            with gr.Column(scale=1):
                max_new_tokens = gr.Slider(
                    minimum=1,
                    maximum=1000,
                    value=256,
                    step=1,
                    interactive=True,
                    label="Max New Tokens",
                )
                top_p = gr.Slider(
                    minimum=0.05,
                    maximum=1.0,
                    value=0.92,
                    step=0.05,
                    interactive=True,
                    label="Top-p (nucleus sampling)",
                )
                top_k = gr.Slider(
                    minimum=0,
                    maximum=50,
                    value=0,
                    step=1,
                    interactive=True,
                    label="Top-k",
                )
                temperature = gr.Slider(
                    minimum=0.1,
                    maximum=5.0,
                    value=0.8,
                    step=0.1,
                    interactive=True,
                    label="Temperature",
                )

        user_text.submit(
            run_generation,
            [user_text, top_p, temperature, top_k, max_new_tokens, performance],
            [model_output, performance],
        )
        button_submit.click(
            run_generation,
            [user_text, top_p, temperature, top_k, max_new_tokens, performance],
            [model_output, performance],
        )
        button_clear.click(
            reset_textbox,
            [user_text, model_output, performance],
            [user_text, model_output, performance],
        )

    if __name__ == "__main__":
        demo.queue()
        try:
            demo.launch(height=800)
        except Exception:
            demo.launch(share=True, height=800)

# Call main function to start Gradio interface
main()