Create an LLM-powered Chatbot using OpenVINO

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS.

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 INT8 precision using NNCF

• Create a chat inference pipeline

• Run chat pipeline

Table of contents:

• Prerequisites

• Select model for inference

• 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 install -q --extra-index-url https://download.pytorch.org/whl/cpu\
"git+https://github.com/huggingface/optimum-intel.git"\
"nncf>=2.6.0"\
"gradio"\
"onnx" "onnxruntime" "einops" "transformers>=4.31.0"\
"openvino==2023.2.0.dev20230922"

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:

• 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.

from config import SUPPORTED_MODELS
import ipywidgets as widgets

model_ids = list(SUPPORTED_MODELS)

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

model_id

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

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

Selected model red-pajama-3b-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 model currently is not covered by Optimum Intel, we will convert it manually and create wrapper compatible with Optimum Intel.

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).

Weights Compression using Optimum Intel

To enable weights compression via NNCF for models supported by Optimum Intel OVQuantizer class should be used instantiated by PyTorch model using from_pretrained method. OVQuantizer.quantize(save_directory=save_dir, weights_only=True) enables weights compression and model conversion to OpenVINO Intermediate Representation format. We will consider how to do it on RedPajama and LLAMA examples.

Note: This tutorial involves conversion model for both FP16 and INT8 weights compression scenarios. It maybe memory and time-consuming in first run. You can manually disable FP16 conversion using CONVERT_FP16 variable below, CONVERT_INT8 variable can be used for disabling conversion model with weights compression respectively.

CONVERT_FP16 = True
CONVERT_INT8 = True

from pathlib import Path
from optimum.intel import OVQuantizer
from transformers import AutoModelForCausalLM
from optimum.intel.openvino import OVModelForCausalLM
import logging
import nncf
import gc

nncf.set_log_level(logging.ERROR)

compressed_model_dir = Path(model_id.value) / "INT8_compressed_weights"
model_dir = Path(model_id.value) / "FP16"
pt_model_id = model_configuration["model_id"]

if "mpt" not in model_id.value:
    if CONVERT_INT8 and not compressed_model_dir.exists():
        pt_model = AutoModelForCausalLM.from_pretrained(pt_model_id)
        quantizer = OVQuantizer.from_pretrained(pt_model)
        quantizer.quantize(save_directory=compressed_model_dir, weights_only=True)
        del quantizer
        del pt_model
        gc.collect()

    if CONVERT_FP16 and not model_dir.exists():
        ov_model = OVModelForCausalLM.from_pretrained(pt_model_id, export=True, compile=False)
        ov_model.half()
        ov_model.save_pretrained(model_dir)
        del ov_model
gc.collect();

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

No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-09-19 19:06:00.934297: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2023-09-19 19:06:00.971948: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-09-19 19:06:01.591238: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
  warnings.warn(

Weights Compression using NNCF

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

To begin compression, we should define model conversion first.

from functools import wraps
import torch
from transformers import AutoModelForCausalLM
from nncf import compress_weights
import openvino as ov
from typing import Optional, Union, Dict, Tuple, List

def flattenize_inputs(inputs):
    """
    Helper function for making nested inputs flattens
    """
    flatten_inputs = []
    for input_data in inputs:
        if input_data is None:
            continue
        if isinstance(input_data, (list, tuple)):
            flatten_inputs.extend(flattenize_inputs(input_data))
        else:
            flatten_inputs.append(input_data)
    return flatten_inputs

def cleanup_torchscript_cache():
    """
    Helper for removing cached model representation
    """
    torch._C._jit_clear_class_registry()
    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
    torch.jit._state._clear_class_state()

def convert_mpt(pt_model:torch.nn.Module, model_path:Path):
    """
    MPT model conversion function

    Params:
      pt_model: PyTorch model
      model_path: path for saving model
    Returns:
      None
    """
    ov_out_path = Path(model_path) / "openvino_model.xml"
    pt_model.config.save_pretrained(ov_out_path.parent)
    pt_model.config.use_cache = True
    outs = pt_model(input_ids=torch.ones((1, 10), dtype=torch.long), attention_mask=torch.ones((1, 10), dtype=torch.long))
    inputs = ["input_ids"]
    outputs = ["logits"]

    dynamic_shapes = {"input_ids": {1: "seq_len"}, "attention_mask": {1: "seq_len"}}
    for idx in range(len(outs.past_key_values)):
        inputs.extend([f"past_key_values.{idx}.key", f"past_key_values.{idx}.value"])
        dynamic_shapes[inputs[-1]] = {2: "past_sequence + sequence"}
        dynamic_shapes[inputs[-2]] = {3: "past_sequence + sequence"}
        outputs.extend([f"present.{idx}.key", f"present.{idx}.value"])

    inputs.append("attention_mask")
    dummy_inputs = {"input_ids": torch.ones((1,2), dtype=torch.long), "past_key_values": outs.past_key_values, "attention_mask": torch.ones((1,12), dtype=torch.long)}
    pt_model.config.torchscript = True
    orig_forward = pt_model.forward
    @wraps(orig_forward)
    def ts_patched_forward(input_ids: torch.Tensor, past_key_values: Tuple[Tuple[torch.Tensor]], attention_mask: torch.Tensor):
        pkv_list = list(past_key_values)
        outs = orig_forward(input_ids=input_ids, past_key_values=pkv_list, attention_mask=attention_mask)
        return (outs.logits, tuple(outs.past_key_values))
    pt_model.forward = ts_patched_forward
    ov_model = ov.convert_model(pt_model, example_input=dummy_inputs)
    pt_model.forward = orig_forward
    for inp_name, m_input, input_data in zip(inputs, ov_model.inputs, flattenize_inputs(dummy_inputs.values())):
        input_node = m_input.get_node()
        if input_node.element_type == ov.Type.dynamic:
            m_input.get_node().set_element_type(ov.Type.f32)
        shape = list(input_data.shape)
        if inp_name in dynamic_shapes:
            for k in dynamic_shapes[inp_name]:
                shape[k] = -1
        input_node.set_partial_shape(ov.PartialShape(shape))
        m_input.get_tensor().set_names({inp_name})

    for out, out_name in zip(ov_model.outputs, outputs):
        out.get_tensor().set_names({out_name})

    ov_model.validate_nodes_and_infer_types()
    ov.save_model(ov_model, ov_out_path)
    del ov_model
    cleanup_torchscript_cache()
    del pt_model

Now, we know how to convert model to OpenVINO format, we can save floating point and compressed model variants

compressed_model_dir = Path(model_id.value) / "INT8_compressed_weights"
model_dir = Path(model_id.value) / "FP16"

if "mpt" in model_id.value and (not compressed_model_dir.exists() or not model_dir.exists()):
    model = AutoModelForCausalLM.from_pretrained(model_configuration["model_id"], torch_dtype=torch.float32, trust_remote_code=True)
    if CONVERT_FP16 and not model_dir.exists():
        convert_mpt(model, model_dir)
    if CONVERT_INT8 and not compressed_model_dir.exists():
        compressed_model = compress_weights(model)
        convert_mpt(compressed_model, compressed_model_dir)

gc.collect();

fp16_weights = model_dir / "openvino_model.bin"
int8_weights = compressed_model_dir / "openvino_model.bin"

if fp16_weights.exists():
    print(f'Size of FP16 model in MB is {fp16_weights.stat().st_size / 1024 / 1024}')
if int8_weights.exists():
    print(f'Size of model with INT8 compressed weights in MB is {int8_weights.stat().st_size / 1024 / 1024}')
if int8_weights.exists() and fp16_weights.exists():
    print(f"Model compression rate: {fp16_weights.stat().st_size / int8_weights.stat().st_size:.3f}")

Size of FP16 model in MB is 5299.166286468506
Size of model with INT8 compressed weights in MB is 2659.578887939453
Model compression rate: 1.992

Select device for inference and model variant

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

VBox(children=(Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU'), Checkbox(value=Tr…

int8_compressed_weights = widgets.Checkbox(
    value=True,
    description='Use compressed weights',
    disabled=False
)

widgets.VBox([device, int8_compressed_weights])

The cell below create OVMPTModel model wrapper based on OVModelForCausalLM model.

from transformers import AutoConfig
import torch

from optimum.intel.openvino import OVModelForCausalLM
from optimum.utils import NormalizedTextConfig, NormalizedConfigManager
from transformers.modeling_outputs import CausalLMOutputWithPast
import numpy as np
from pathlib import Path


class OVMPTModel(OVModelForCausalLM):
    """
    Optimum intel compatible model wrapper for MPT
    """
    def __init__(
        self,
        model: "Model",
        config: "PretrainedConfig" = None,
        device: str = "CPU",
        dynamic_shapes: bool = True,
        ov_config: Optional[Dict[str, str]] = None,
        model_save_dir: Optional[Union[str, Path]] = None,
        **kwargs,
    ):
        NormalizedConfigManager._conf["mpt"] = NormalizedTextConfig.with_args(num_layers="n_layers", num_attention_heads="n_heads")
        super().__init__(model, config, device, dynamic_shapes, ov_config, model_save_dir, **kwargs)

    def _reshape(
        self,
        model: "Model",
        *args,
        **kwargs
    ):
        shapes = {}
        for inputs in model.inputs:
            shapes[inputs] = inputs.get_partial_shape()
            if shapes[inputs].rank.get_length() in [2, 3]:
                shapes[inputs][1] = -1
            else:
                if ".key" in inputs.get_any_name():
                    shapes[inputs][3] = -1
                else:
                    shapes[inputs][2] = -1

        model.reshape(shapes)
        return model

    def forward(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        self.compile()

        if self.use_cache and past_key_values is not None:
            input_ids = input_ids[:, -1:]

        inputs = {}
        if past_key_values is not None:
            # Flatten the past_key_values
            past_key_values = tuple(
                past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer
            )
            # Add the past_key_values to the decoder inputs
            inputs = dict(zip(self.key_value_input_names, past_key_values))

        # Create empty past_key_values for decoder_with_past first generation step
        elif self.use_cache:
            shape_input_ids = input_ids.shape
            num_attention_heads = (
                self.normalized_config.num_attention_heads if self.config.model_type == "bloom" else 1
            )
            for input_name in self.key_value_input_names:
                model_inputs = self.model.input(input_name)
                shape = model_inputs.get_partial_shape()
                shape[0] = shape_input_ids[0] * num_attention_heads
                if shape[2].is_dynamic:
                    shape[2] = 0
                if shape[1].is_dynamic:
                    shape[1] = 0
                if shape.rank.get_length() == 4 and shape[3].is_dynamic:
                    shape[3] = 0
                inputs[input_name] = ov.Tensor(model_inputs.get_element_type(), shape.get_shape())

        inputs["input_ids"] = np.array(input_ids)

        # Add the attention_mask inputs when needed
        if "attention_mask" in self.input_names and attention_mask is not None:
            inputs["attention_mask"] = np.array(attention_mask)

        # Run inference
        self.request.start_async(inputs, shared_memory=True)
        self.request.wait()

        logits = torch.from_numpy(self.request.get_tensor("logits").data).to(self.device)

        if self.use_cache:
            # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
            past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
            # Tuple of tuple of length `n_layers`, with each tuple of length equal to 2 (k/v of self-attention)
            past_key_values = tuple(
                past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv)
            )
        else:
            past_key_values = None

        return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)

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

from pathlib import Path
from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer

model_dir = Path(model_id.value) / ("FP16" if not int8_compressed_weights.value else "INT8_compressed_weights")
model_name = model_configuration["model_id"]

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

tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model_class = OVModelForCausalLM if "mpt" not in model_id.value else OVMPTModel
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)

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

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)[0])

Setting pad_token_id to eos_token_id:0 for open-end generation.
/home/ea/work/ov_venv/lib/python3.8/site-packages/optimum/intel/openvino/modeling_decoder.py:364: FutureWarning: shared_memory is deprecated and will be removed in 2024.0. Value of shared_memory is going to override share_inputs value. Please use only share_inputs explicitly.
  self.request.start_async(inputs, shared_memory=True)

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

import gradio as gr
import torch
from transformers import (
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)


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



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 de

    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_text(history:List[Tuple[str, str]]):
    """
    function for conversion history stored as list pairs of user and assistant messages to string according to model expected conversation template
    Params:
      history: dialogue history
    Returns:
      history in text format
    """
    text = start_message + "".join(
        [
            "".join(
                [
                    history_template.format(user=item[0], assistant=item[1])
                ]
            )
            for item in history[:-1]
        ]
    )
    text += "".join(
        [
            "".join(
                [
                    current_message_template.format(user=history[-1][0], assistant=history[-1][1])
                ]
            )
        ]
    )
    return text



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
    messages = convert_history_to_text(history)

    # Tokenize the messages string
    input_ids = tok(messages, return_tensors="pt", **tokenizer_kwargs).input_ids
    if input_ids.shape[1] > 2000:
        history = [history[-1]]
        messages = convert_history_to_text(history)
        input_ids = tok(messages, return_tensors="pt", **tokenizer_kwargs).input_ids
    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([
        ["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“"]
    ],
        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()

Running on local URL:  http://127.0.0.1:7860

To create a public link, set share=True in launch().

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

Closing server running on port: 7860