Create an LLM-powered Chatbot using OpenVINO¶

This Jupyter notebook can be launched after a local installation only.

In the rapidly evolving world of artificial intelligence (AI), chatbots have emerged as powerful tools for businesses to enhance customer interactions and streamline operations. Large Language Models (LLMs) are artificial intelligence systems that can understand and generate human language. They use deep learning algorithms and massive amounts of data to learn the nuances of language and produce coherent and relevant responses. While a decent intent-based chatbot can answer basic, one-touch inquiries like order management, FAQs, and policy questions, LLM chatbots can tackle more complex, multi-touch questions. LLM enables chatbots to provide support in a conversational manner, similar to how humans do, through contextual memory. Leveraging the capabilities of Language Models, chatbots are becoming increasingly intelligent, capable of understanding and responding to human language with remarkable accuracy.

Previously, we already discussed how to build an instruction-following pipeline using OpenVINO and Optimum Intel, please check out Dolly example for reference. In this tutorial, we consider how to use the power of OpenVINO for running Large Language Models for chat. We will use a pre-trained model from the Hugging Face Transformers library. To simplify the user experience, the Hugging Face Optimum Intel library is used to convert the models to OpenVINO™ IR format.

The tutorial consists of the following steps:

Install prerequisites
Download and convert the model from a public source using the OpenVINO integration with Hugging Face Optimum.
Compress model weights to 4-bit or 8-bit data types using NNCF
Create a chat inference pipeline
Run chat pipeline

Table of contents:¶

Prerequisites
Select model for inference
login to huggingfacehub to get access to pretrained model
Instantiate Model using Optimum Intel
Compress model weights
- Weights Compression using Optimum Intel
- Weights Compression using NNCF
Select device for inference and model variant
Run Chatbot

Prerequisites¶

Install required dependencies

%pip uninstall -q -y openvino-dev openvino openvino-nightly optimum optimum-intel
%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
"git+https://github.com/huggingface/optimum-intel.git"\
"nncf>=2.8.0"\
"datasets" \
"accelerate"\
"openvino-nightly"\
"gradio"\
"onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.36.0"

Select model for inference¶

The tutorial supports different models, you can select one from the provided options to compare the quality of open source LLM solutions.

NOTE: conversion of some models can require additional actions from user side and at least 64GB RAM for conversion.

The available options are:

tiny-llama-1b-chat - This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens with the adoption of the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. More details about model can be found in model card
red-pajama-3b-chat - A 2.8B parameter pre-trained language model based on GPT-NEOX architecture. It was developed by Together Computer and leaders from the open-source AI community. The model is fine-tuned on OASST1 and Dolly2 datasets to enhance chatting ability. More details about model can be found in HuggingFace model card.
llama-2-7b-chat - LLama 2 is the second generation of LLama models developed by Meta. Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. llama-2-7b-chat is 7 billions parameters version of LLama 2 finetuned and optimized for dialogue use case. More details about model can be found in the paper, repository and HuggingFace model card

NOTE: run model with demo, you will need to accept license agreement. You must be a registered user in Hugging Face Hub. Please visit HuggingFace model card, carefully read terms of usage and click accept button. You will need to use an access token for the code below to run. For more information on access tokens, refer to this section of the documentation. You can login on Hugging Face Hub in notebook environment, using following code:

## login to huggingfacehub to get access to pretrained model

from huggingface_hub import notebook_login, whoami

try:
    whoami()
    print('Authorization token already provided')
except OSError:
    notebook_login()

mpt-7b-chat - MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases (ALiBi). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT-7B-chat is a chatbot-like model for dialogue generation. It was built by finetuning MPT-7B on the ShareGPT-Vicuna, HC3, Alpaca, HH-RLHF, and Evol-Instruct datasets. More details about the model can be found in blog post, repository and HuggingFace model card.
qwen-7b-chat - Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. For more details about Qwen, please refer to the GitHub code repository.
chatglm3-6b - ChatGLM3-6B is the latest open-source model in the ChatGLM series. While retaining many excellent features such as smooth dialogue and low deployment threshold from the previous two generations, ChatGLM3-6B employs a more diverse training dataset, more sufficient training steps, and a more reasonable training strategy. ChatGLM3-6B adopts a newly designed Prompt format, in addition to the normal multi-turn dialogue. You can find more details about model in the model card
mistral-7b - The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. You can find more details about model in the model card, paper and release blog post.
zephyr-7b-beta - Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-beta is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). You can find more details about model in technical report and HuggingFace model card.
neural-chat-7b-v3-1 - Mistral-7b model fine-tuned using Intel Gaudi. The model fine-tuned on the open source dataset Open-Orca/SlimOrca and aligned with Direct Preference Optimization (DPO) algorithm. More details can be found in model card and blog post.
notus-7b-v1 - Notus is a collection of fine-tuned models using Direct Preference Optimization (DPO). and related RLHF techniques. This model is the first version, fine-tuned with DPO over zephyr-7b-sft. Following a data-first approach, the only difference between Notus-7B-v1 and Zephyr-7B-beta is the preference dataset used for dDPO. Proposed approach for dataset creation helps to effectively fine-tune Notus-7b that surpasses Zephyr-7B-beta and Claude 2 on AlpacaEval. More details about model can be found in model card.
youri-7b-chat - Youri-7b-chat is a Llama2 based model. Rinna Co., Ltd. conducted further pre-training for the Llama2 model with a mixture of English and Japanese datasets to improve Japanese task capability. The model is publicly released on Hugging Face hub. You can find detailed information at the rinna/youri-7b-chat project page.

from config import SUPPORTED_LLM_MODELS
import ipywidgets as widgets

model_ids = list(SUPPORTED_LLM_MODELS)

model_id = widgets.Dropdown(
    options=model_ids,
    value=model_ids[0],
    description="Model:",
    disabled=False,
)

model_id

Dropdown(description='Model:', options=('tiny-llama-1b-chat', 'red-pajama-3b-chat', 'llama-2-chat-7b', 'mpt-7b…

model_configuration = SUPPORTED_LLM_MODELS[model_id.value]
print(f"Selected model {model_id.value}")

Selected model tiny-llama-1b-chat

Instantiate Model using Optimum Intel¶

Optimum Intel can be used to load optimized models from the Hugging Face Hub and create pipelines to run an inference with OpenVINO Runtime using Hugging Face APIs. The Optimum Inference models are API compatible with Hugging Face Transformers models. This means we just need to replace AutoModelForXxx class with the corresponding OVModelForXxx class.

Below is an example of the RedPajama model

-from transformers import AutoModelForCausalLM
+from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer, pipeline

model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-model = AutoModelForCausalLM.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id, export=True)

Model class initialization starts with calling from_pretrained method. When downloading and converting Transformers model, the parameter export=True should be added. We can save the converted model for the next usage with the save_pretrained method. Tokenizer class and pipelines API are compatible with Optimum models.

To optimize the generation process and use memory more efficiently, the use_cache=True option is enabled. Since the output side is auto-regressive, an output token hidden state remains the same once computed for every further generation step. Therefore, recomputing it every time you want to generate a new token seems wasteful. With the cache, the model saves the hidden state once it has been computed. The model only computes the one for the most recently generated output token at each time step, re-using the saved ones for hidden tokens. This reduces the generation complexity from \(O(n^3)\) to \(O(n^2)\) for a transformer model. More details about how it works can be found in this article. With this option, the model gets the previous step’s hidden states (cached attention keys and values) as input and additionally provides hidden states for the current step as output. It means for all next iterations, it is enough to provide only a new token obtained from the previous step and cached key values to get the next token prediction.

In our case, MPT, Qwen and ChatGLM model currently is not covered by Optimum Intel, we will convert it manually and create wrapper compatible with Optimum Intel.

from transformers import AutoModelForCausalLM, AutoConfig
from optimum.intel import OVQuantizer
from optimum.intel.openvino import OVModelForCausalLM
import openvino as ov
from pathlib import Path
import shutil
import torch
import logging
import nncf
import gc
from converter import converters

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino

/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/torch/cuda/__init__.py:138: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11080). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() > 0
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'

Compress model weights¶

The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). Compared to INT8 compression, INT4 compression improves performance even more, but introduces a minor drop in prediction quality.

Weights Compression using Optimum Intel¶

To enable weights compression via NNCF for models supported by Optimum Intel OVQuantizer class should be used for OVModelForCausalLM model. OVQuantizer.quantize(save_directory=save_dir, weights_only=True) enables weights compression. We will consider how to do it on RedPajama, LLAMA and Zephyr examples.

NOTE: Weights Compression using Optimum Intel currently supports only INT8 compression. We will apply INT4 compression for these model using NNCF API described below.

NOTE: There may be no speedup for INT4/INT8 compressed models on dGPU.

Weights Compression using NNCF¶

You also can perform weights compression for OpenVINO models using NNCF directly. nncf.compress_weights function accepts OpenVINO model instance and compresses its weights for Linear and Embedding layers. We will consider this variant based on MPT model.

NOTE: This tutorial involves conversion model for FP16 and INT4/INT8 weights compression scenarios. It may be memory and time-consuming in the first run. You can manually control the compression precision below.

from IPython.display import display

prepare_int4_model = widgets.Checkbox(
    value=True,
    description="Prepare INT4 model",
    disabled=False,
)
prepare_int8_model = widgets.Checkbox(
    value=False,
    description="Prepare INT8 model",
    disabled=False,
)
prepare_fp16_model = widgets.Checkbox(
    value=False,
    description="Prepare FP16 model",
    disabled=False,
)

display(prepare_int4_model)
display(prepare_int8_model)
display(prepare_fp16_model)

Checkbox(value=True, description='Prepare INT4 model')

Checkbox(value=False, description='Prepare INT8 model')

Checkbox(value=False, description='Prepare FP16 model')

We can now save floating point and compressed model variants

nncf.set_log_level(logging.ERROR)

pt_model_id = model_configuration["model_id"]
pt_model_name = model_id.value.split("-")[0]
model_type = AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True).model_type
fp16_model_dir = Path(model_id.value) / "FP16"
int8_model_dir = Path(model_id.value) / "INT8_compressed_weights"
int4_model_dir = Path(model_id.value) / "INT4_compressed_weights"


def convert_to_fp16():
    if (fp16_model_dir / "openvino_model.xml").exists():
        return
    if not model_configuration["remote"]:
        ov_model = OVModelForCausalLM.from_pretrained(
            pt_model_id, export=True, compile=False, load_in_8bit=False
        )
        ov_model.half()
        ov_model.save_pretrained(fp16_model_dir)
        del ov_model
    else:
        model_kwargs = {}
        if "revision" in model_configuration:
            model_kwargs["revision"] = model_configuration["revision"]
        model = AutoModelForCausalLM.from_pretrained(
            model_configuration["model_id"],
            torch_dtype=torch.float32,
            trust_remote_code=True,
            **model_kwargs
        )
        converters[pt_model_name](model, fp16_model_dir)
        del model
    gc.collect()


def convert_to_int8():
    if (int8_model_dir / "openvino_model.xml").exists():
        return
    int8_model_dir.mkdir(parents=True, exist_ok=True)
    if not model_configuration["remote"]:
        if fp16_model_dir.exists():
            ov_model = OVModelForCausalLM.from_pretrained(fp16_model_dir, compile=False)
        else:
            ov_model = OVModelForCausalLM.from_pretrained(
                pt_model_id, export=True, compile=False, load_in_8bit=False
            )
            ov_model.half()
        quantizer = OVQuantizer.from_pretrained(ov_model)
        quantizer.quantize(save_directory=int8_model_dir, weights_only=True)
        del quantizer
        del ov_model
    else:
        convert_to_fp16()
        ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
        shutil.copy(fp16_model_dir / "config.json", int8_model_dir / "config.json")
        configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
        if configuration_file.exists():
            shutil.copy(
                configuration_file, int8_model_dir / f"configuration_{model_type}.py"
            )
        compressed_model = nncf.compress_weights(ov_model)
        ov.save_model(compressed_model, int8_model_dir / "openvino_model.xml")
        del ov_model
        del compressed_model
    gc.collect()


def convert_to_int4():
    compression_configs = {
        "zephyr-7b-beta": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "mistral-7b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "notus-7b-v1": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "neural-chat-7b-v3-1": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "llama-2-chat-7b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 128,
            "ratio": 0.8,
        },
        "chatglm2-6b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 128,
            "ratio": 0.72,
        },
        "qwen-7b-chat": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 128,
            "ratio": 0.6
        },
        'red-pajama-3b-chat': {
            "mode": nncf.CompressWeightsMode.INT4_ASYM,
            "group_size": 128,
            "ratio": 0.5,
        },
        "default": {
            "mode": nncf.CompressWeightsMode.INT4_ASYM,
            "group_size": 128,
            "ratio": 0.8,
        },
    }

    model_compression_params = compression_configs.get(
        model_id.value, compression_configs["default"]
    )
    if (int4_model_dir / "openvino_model.xml").exists():
        return
    int4_model_dir.mkdir(parents=True, exist_ok=True)
    if not model_configuration["remote"]:
        if not fp16_model_dir.exists():
            model = OVModelForCausalLM.from_pretrained(
                pt_model_id, export=True, compile=False, load_in_8bit=False
            ).half()
            model.config.save_pretrained(int4_model_dir)
            ov_model = model._original_model
            del model
            gc.collect()
        else:
            ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
            shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")

    else:
        convert_to_fp16()
        ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
        shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")
        configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
        if configuration_file.exists():
            shutil.copy(
                configuration_file, int4_model_dir / f"configuration_{model_type}.py"
            )
    compressed_model = nncf.compress_weights(ov_model, **model_compression_params)
    ov.save_model(compressed_model, int4_model_dir / "openvino_model.xml")
    del ov_model
    del compressed_model
    gc.collect()


if prepare_fp16_model.value:
    convert_to_fp16()
if prepare_int8_model.value:
    convert_to_int8()
if prepare_int4_model.value:
    convert_to_int4()

Let’s compare model size for different compression types

fp16_weights = fp16_model_dir / "openvino_model.bin"
int8_weights = int8_model_dir / "openvino_model.bin"
int4_weights = int4_model_dir / "openvino_model.bin"

if fp16_weights.exists():
    print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
    if compressed_weights.exists():
        print(
            f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB"
        )
    if compressed_weights.exists() and fp16_weights.exists():
        print(
            f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}"
        )

Size of FP16 model is 2098.68 MB
Size of model with INT8 compressed weights is 1050.99 MB
Compression rate for INT8 model: 1.997
Size of model with INT4 compressed weights is 696.99 MB
Compression rate for INT4 model: 3.011

Select device for inference and model variant¶

NOTE: There may be no speedup for INT4/INT8 compressed models on dGPU.

core = ov.Core()
device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="CPU",
    description="Device:",
    disabled=False,
)

device

Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')

The cell below create OVMPTModel, OVQWENModel and OVCHATGLM2Model wrapper based on OVModelForCausalLM model.

from ov_llm_model import model_classes

The cell below demonstrates how to instantiate model based on selected variant of model weights and inference device

available_models = []
if int4_model_dir.exists():
    available_models.append("INT4")
if int8_model_dir.exists():
    available_models.append("INT8")
if fp16_model_dir.exists():
    available_models.append("FP16")

model_to_run = widgets.Dropdown(
    options=available_models,
    value=available_models[0],
    description="Model to run:",
    disabled=False,
)

model_to_run

Dropdown(description='Model to run:', options=('INT4', 'INT8', 'FP16'), value='INT4')

from transformers import AutoTokenizer

if model_to_run.value == "INT4":
    model_dir = int4_model_dir
elif model_to_run.value == "INT8":
    model_dir = int8_model_dir
else:
    model_dir = fp16_model_dir
print(f"Loading model from {model_dir}")

ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}

# On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
# issues caused by this, which we avoid by setting precision hint to "f32".
if model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and device.value in ["GPU", "AUTO"]:
    ov_config["INFERENCE_PRECISION_HINT"] = "f32"

model_name = model_configuration["model_id"]
class_key = model_id.value.split("-")[0]
tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model_class = (
    OVModelForCausalLM
    if not model_configuration["remote"]
    else model_classes[class_key]
)
ov_model = model_class.from_pretrained(
    model_dir,
    device=device.value,
    ov_config=ov_config,
    config=AutoConfig.from_pretrained(model_dir, trust_remote_code=True),
    trust_remote_code=True,
)

Loading model from tiny-llama-1b-chat/INT4_compressed_weights

The argument trust_remote_code is to be used along with export=True. It will be ignored.
Compiling the model to CPU ...

tokenizer_kwargs = model_configuration.get("tokenizer_kwargs", {})
test_string = "2 + 2 ="
input_tokens = tok(test_string, return_tensors="pt", **tokenizer_kwargs)
answer = ov_model.generate(**input_tokens, max_new_tokens=2)
print(tok.batch_decode(answer, skip_special_tokens=True)[0])

Setting pad_token_id to eos_token_id:2 for open-end generation.

2 + 2 = 4

Run Chatbot¶

Now, when model created, we can setup Chatbot interface using Gradio. The diagram below illustrates how the chatbot pipeline works

generation pipeline¶

As can be seen, the pipeline very similar to instruction-following with only changes that previous conversation history additionally passed as input with next user question for getting wider input context. On the first iteration, the user provided instructions joined to conversation history (if exists) converted to token ids using a tokenizer, then prepared input provided to the model. The model generates probabilities for all tokens in logits format The way the next token will be selected over predicted probabilities is driven by the selected decoding methodology. You can find more information about the most popular decoding methods in this blog. The result generation updates conversation history for next conversation step. it makes stronger connection of next question with previously provided and allows user to make clarifications regarding previously provided answers.

There are several parameters that can control text generation quality: * Temperature is a parameter used to control the level of creativity in AI-generated text. By adjusting the temperature, you can influence the AI model’s probability distribution, making the text more focused or diverse.

Consider the following example: The AI model has to complete the sentence “The cat is ____.” with the following token probabilities:

playing: 0.5
sleeping: 0.25
eating: 0.15
driving: 0.05
flying: 0.05

- **Low temperature** (e.g., 0.2): The AI model becomes more focused and deterministic, choosing tokens with the highest probability, such as "playing."
- **Medium temperature** (e.g., 1.0): The AI model maintains a balance between creativity and focus, selecting tokens based on their probabilities without significant bias, such as "playing," "sleeping," or "eating."
- **High temperature** (e.g., 2.0): The AI model becomes more adventurous, increasing the chances of selecting less likely tokens, such as "driving" and "flying."

Top-p, also known as nucleus sampling, is a parameter used to control the range of tokens considered by the AI model based on their cumulative probability. By adjusting the top-p value, you can influence the AI model’s token selection, making it more focused or diverse. Using the same example with the cat, consider the following top_p settings:
- Low top_p (e.g., 0.5): The AI model considers only tokens with the highest cumulative probability, such as “playing.”
- Medium top_p (e.g., 0.8): The AI model considers tokens with a higher cumulative probability, such as “playing,” “sleeping,” and “eating.”
- High top_p (e.g., 1.0): The AI model considers all tokens, including those with lower probabilities, such as “driving” and “flying.”
Top-k is an another popular sampling strategy. In comparison with Top-P, which chooses from the smallest possible set of words whose cumulative probability exceeds the probability P, in Top-K sampling K most likely next words are filtered and the probability mass is redistributed among only those K next words. In our example with cat, if k=3, then only “playing”, “sleeping” and “eating” will be taken into account as possible next word.
Repetition Penalty This parameter can help penalize tokens based on how frequently they occur in the text, including the input prompt. A token that has already appeared five times is penalized more heavily than a token that has appeared only one time. A value of 1 means that there is no penalty and values larger than 1 discourage repeated tokens.

from threading import Event, Thread
from uuid import uuid4
from typing import List, Tuple
import gradio as gr
from transformers import (
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)


model_name = model_configuration["model_id"]
start_message = model_configuration["start_message"]
history_template = model_configuration.get("history_template")
current_message_template = model_configuration.get("current_message_template")
stop_tokens = model_configuration.get("stop_tokens")
roles = model_configuration.get("roles")
tokenizer_kwargs = model_configuration.get("tokenizer_kwargs", {})

chinese_examples = [
    ["你好!"],
    ["你是谁?"],
    ["请介绍一下上海"],
    ["请介绍一下英特尔公司"],
    ["晚上睡不着怎么办？"],
    ["给我讲一个年轻人奋斗创业最终取得成功的故事。"],
    ["给这个故事起一个标题。"],
]

english_examples = [
    ["Hello there! How are you doing?"],
    ["What is OpenVINO?"],
    ["Who are you?"],
    ["Can you explain to me briefly what is Python programming language?"],
    ["Explain the plot of Cinderella in a sentence."],
    ["What are some common mistakes to avoid when writing code?"],
    [
        "Write a 100-word blog post on “Benefits of Artificial Intelligence and OpenVINO“"
    ],
]

japanese_examples = [
    ["こんにちは！調子はどうですか?"],
    ["OpenVINOとは何ですか?"],
    ["あなたは誰ですか?"],
    ["Pythonプログラミング言語とは何か簡単に説明してもらえますか?"],
    ["シンデレラのあらすじを一文で説明してください。"],
    ["コードを書くときに避けるべきよくある間違いは何ですか?"],
    ["人工知能と「OpenVINOの利点」について100語程度のブログ記事を書いてください。"],
]

examples = (
    chinese_examples
    if ("qwen" in model_id.value or "chatglm" in model_id.value)
    else japanese_examples
    if ("youri" in model_id.value)
    else english_examples
)

max_new_tokens = 256


class StopOnTokens(StoppingCriteria):
    def __init__(self, token_ids):
        self.token_ids = token_ids

    def __call__(
        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
    ) -> bool:
        for stop_id in self.token_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False


if stop_tokens is not None:
    if isinstance(stop_tokens[0], str):
        stop_tokens = tok.convert_tokens_to_ids(stop_tokens)

    stop_tokens = [StopOnTokens(stop_tokens)]


def default_partial_text_processor(partial_text: str, new_text: str):
    """
    helper for updating partially generated answer, used by default

    Params:
      partial_text: text buffer for storing previosly generated text
      new_text: text update for the current step
    Returns:
      updated text string

    """
    partial_text += new_text
    return partial_text


text_processor = model_configuration.get(
    "partial_text_processor", default_partial_text_processor
)


def convert_history_to_token(history: List[Tuple[str, str]], roles=None):
    """
    function for conversion history stored as list pairs of user and assistant messages to tokens according to model expected conversation template
    Params:
      history: dialogue history
    Returns:
      history in token format
    """
    if roles is None:
        text = start_message + "".join(
            [
                "".join(
                    [
                        history_template.format(
                            num=round, user=item[0], assistant=item[1]
                        )
                    ]
                )
                for round, item in enumerate(history[:-1])
            ]
        )
        text += "".join(
            [
                "".join(
                    [
                        current_message_template.format(
                            num=len(history) + 1,
                            user=history[-1][0],
                            assistant=history[-1][1],
                        )
                    ]
                )
            ]
        )
        input_token = tok(text, return_tensors="pt", **tokenizer_kwargs).input_ids
    else:
        input_ids = []
        input_ids.extend(tok.build_single_message(roles[0], "", start_message))
        for old_query, response in history[:-1]:
            input_ids.extend(tok.build_single_message(roles[1], "", old_query))
            input_ids.extend(tok.build_single_message(roles[2], "", response))
        input_ids.extend(tok.build_single_message(roles[1], "", history[-1][0]))
        input_ids.extend([tok.get_command(f"<|{roles[2]}|>")])
        input_token = tok.batch_encode_plus(
            [input_ids], return_tensors="pt", is_split_into_words=True
        ).input_ids
    return input_token


def user(message, history):
    """
    callback function for updating user messages in interface on submit button click

    Params:
      message: current message
      history: conversation history
    Returns:
      None
    """
    # Append the user's message to the conversation history
    return "", history + [[message, ""]]


def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
    """
    callback function for running chatbot on submit button click

    Params:
      history: conversation history
      temperature:  parameter for control the level of creativity in AI-generated text.
                    By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
      top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
      top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
      repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
      conversation_id: unique conversation identifier.

    """

    # Construct the input message string for the model by concatenating the current system message and conversation history
    # Tokenize the messages string
    input_ids = convert_history_to_token(history, roles)
    if input_ids.shape[1] > 2000:
        history = [history[-1]]
        input_ids = convert_history_to_token(history, roles)
    streamer = TextIteratorStreamer(
        tok, timeout=30.0, skip_prompt=True, skip_special_tokens=True
    )
    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        do_sample=temperature > 0.0,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        streamer=streamer,
    )
    if stop_tokens is not None:
        generate_kwargs["stopping_criteria"] = StoppingCriteriaList(stop_tokens)

    stream_complete = Event()

    def generate_and_signal_complete():
        """
        genration function for single thread
        """
        global start_time
        ov_model.generate(**generate_kwargs)
        stream_complete.set()

    t1 = Thread(target=generate_and_signal_complete)
    t1.start()

    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        partial_text = text_processor(partial_text, new_text)
        history[-1][1] = partial_text
        yield history


def get_uuid():
    """
    universal unique identifier for thread
    """
    return str(uuid4())


with gr.Blocks(
    theme=gr.themes.Soft(),
    css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
    conversation_id = gr.State(get_uuid)
    gr.Markdown(f"""<h1><center>OpenVINO {model_id.value} Chatbot</center></h1>""")
    chatbot = gr.Chatbot(height=500)
    with gr.Row():
        with gr.Column():
            msg = gr.Textbox(
                label="Chat Message Box",
                placeholder="Chat Message Box",
                show_label=False,
                container=False,
            )
        with gr.Column():
            with gr.Row():
                submit = gr.Button("Submit")
                stop = gr.Button("Stop")
                clear = gr.Button("Clear")
    with gr.Row():
        with gr.Accordion("Advanced Options:", open=False):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        temperature = gr.Slider(
                            label="Temperature",
                            value=0.1,
                            minimum=0.0,
                            maximum=1.0,
                            step=0.1,
                            interactive=True,
                            info="Higher values produce more diverse outputs",
                        )
                with gr.Column():
                    with gr.Row():
                        top_p = gr.Slider(
                            label="Top-p (nucleus sampling)",
                            value=1.0,
                            minimum=0.0,
                            maximum=1,
                            step=0.01,
                            interactive=True,
                            info=(
                                "Sample from the smallest possible set of tokens whose cumulative probability "
                                "exceeds top_p. Set to 1 to disable and sample from all tokens."
                            ),
                        )
                with gr.Column():
                    with gr.Row():
                        top_k = gr.Slider(
                            label="Top-k",
                            value=50,
                            minimum=0.0,
                            maximum=200,
                            step=1,
                            interactive=True,
                            info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
                        )
                with gr.Column():
                    with gr.Row():
                        repetition_penalty = gr.Slider(
                            label="Repetition Penalty",
                            value=1.1,
                            minimum=1.0,
                            maximum=2.0,
                            step=0.1,
                            interactive=True,
                            info="Penalize repetition — 1.0 to disable.",
                        )
    gr.Examples(
        examples, inputs=msg, label="Click on any example and press the 'Submit' button"
    )

    submit_event = msg.submit(
        fn=user,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=False,
    ).then(
        fn=bot,
        inputs=[
            chatbot,
            temperature,
            top_p,
            top_k,
            repetition_penalty,
            conversation_id,
        ],
        outputs=chatbot,
        queue=True,
    )
    submit_click_event = submit.click(
        fn=user,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=False,
    ).then(
        fn=bot,
        inputs=[
            chatbot,
            temperature,
            top_p,
            top_k,
            repetition_penalty,
            conversation_id,
        ],
        outputs=chatbot,
        queue=True,
    )
    stop.click(
        fn=None,
        inputs=None,
        outputs=None,
        cancels=[submit_event, submit_click_event],
        queue=False,
    )
    clear.click(lambda: None, None, chatbot, queue=False)

demo.queue(max_size=2)
# if you are launching remotely, specify server_name and server_port
#  demo.launch(server_name='your server name', server_port='server port in int')
# if you have any issue to launch on your platform, you can pass share=True to launch method:
# demo.launch(share=True)
# it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
demo.launch()

# please run this cell for stopping gradio interface
demo.close()