models.py

import requests
import json
import torch
import os
from datetime import datetime, timedelta
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

class GigaChat:
    def __init__(self, auth_file='auth_token.json'):
        # url = "https://ngw.devices.sberbank.ru:9443/api/v2/oauth"
        self.auth_url = "https://api.mlrnd.ru/api/v2/oauth"

        # url = "https://gigachat.devices.sberbank.ru/api/v1/chat/completions"
        self.gen_url = "https://api.mlrnd.ru/api/v1/chat/completions"

        # payload='scope=GIGACHAT_API_CORP'
        self.payload='scope=API_v1'

        self.auth_file = auth_file

        if self.auth_file is None or not os.path.isfile(auth_file):
            self.gen_giga_token(auth_file)

    @classmethod
    def get_giga(cls, auth_file='auth_token.json'):
        print('got giga')
        return cls(auth_file)

    def gen_giga_token(self, auth_file):
        headers = {
            'Content-Type': 'application/x-www-form-urlencoded',
            'Accept': 'application/json',
            'RqUID': '1b519047-0ee9-4b63-8599-e5ffc9c77e72',
            'Authorization': os.getenv('GIGACHAT_API_TOKEN')
        }

        response = requests.request(
            "POST",
            self.auth_url,
            headers=headers,
            data=self.payload,
            verify=False
            )

        with open(auth_file, 'w') as f:
            json.dump(json.loads(response.text), f, ensure_ascii=False)


    def get_text(self, content, auth_token=None, params=None):
        if params is None:
            params = dict()

        payload = json.dumps(
            {
                 "model": "Test_model",
                 "messages": content,
                 "temperature": params.get("temperature") if params.get("temperature") else 1,
                 "top_p": params.get("top_p") if params.get("top_p") else 0.9,
                 "n": params.get("n") if params.get("n") else 1,
                 "stream": False,
                 "max_tokens": params.get("max_tokens") if params.get("max_tokens") else 512,
                 "repetition_penalty":  params.get("repetition_penalty") if params.get("repetition_penalty") else 1
            }
        )
        headers = {
            'Content-Type': 'application/json',
            'Accept': 'application/json',
            'Authorization': f'Bearer {auth_token}'
        }

        response = requests.request("POST", self.gen_url, headers=headers, data=payload, verify=False)

        return json.loads(response.text)


def get_tinyllama():
    print('got llama')
    tinyllama = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16, device_map="auto")
    return tinyllama

def get_qwen2ins1b():
    model = AutoModelForCausalLM.from_pretrained(
        "Qwen/Qwen2-1.5B-Instruct",
        torch_dtype="auto",
        device_map="auto"
        )
    
    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
    return {'model': model, 'tokenizer': tokenizer}

def response_tinyllama(
        model=None,
        messages=None,
        params=None
        ):
    
    if params is None:
        params = dict()

    messages_dict = [
        {
            "role": "system",
            "content": "You are a friendly and helpful chatbot",
        }
    ]
    for step in messages:
        messages_dict.append({'role': 'user', 'content': step[0]})
        if len(step) >= 2:
            messages_dict.append({'role': 'assistant', 'content': step[1]})

    prompt = model.tokenizer.apply_chat_template(messages_dict, tokenize=False, add_generation_prompt=True)
    outputs = model(
        prompt,
        max_new_tokens = params.get("max_tokens") if params.get("max_tokens") else 512,
        temperature = params.get("temperature") if params.get("temperature") else 1,
        top_p = params.get("top_p") if params.get("top_p") else 0.9,
        repetition_penalty = params.get("repetition_penalty") if params.get("repetition_penalty") else 1
        )

    return outputs[0]['generated_text'].split('<|assistant|>')[1].strip()

def response_qwen2ins1b(
        model=None,
        messages=None,
        params=None
        ):
    
    messages_dict = [
        {
            "role": "system",
            "content": "You are a friendly and helpful chatbot",
        }
    ]
    for step in messages:
        messages_dict.append({'role': 'user', 'content': step[0]})
        if len(step) >= 2:
            messages_dict.append({'role': 'assistant', 'content': step[1]})

    text = model['tokenizer'].apply_chat_template(
        messages_dict,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = model['tokenizer']([text], return_tensors="pt")

    generated_ids = model['model'].generate(
        model_inputs.input_ids,
        max_new_tokens = params.get("max_tokens") if params.get("max_tokens") else 512,
        temperature = params.get("temperature") if params.get("temperature") else 1,
        top_p = params.get("top_p") if params.get("top_p") else 0.9,
        repetition_penalty = params.get("repetition_penalty") if params.get("repetition_penalty") else 1
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = model['tokenizer'].batch_decode(generated_ids, skip_special_tokens=True)[0]

    return response # outputs[0]['generated_text'] #.split('<|assistant|>')[1].strip()

def response_gigachat(
        model=None,
        messages=None,
        model_params=None
        ): # content=None, auth_file=None

    with open(model.auth_file) as f:
        auth_token = json.load(f)
    
    if datetime.fromtimestamp(auth_token['expires_at']/1000) <= datetime.now() - timedelta(seconds=60):
        model.gen_giga_token(model.auth_file)
        with open(model.auth_file) as f:
            auth_token = json.load(f)

    content = []
    for step in messages:
        content.append({'role': 'user', 'content': step[0]})
        if len(step) >= 2:
            content.append({'role': 'assistant', 'content': step[1]})

    resp = model.get_text(content, auth_token['access_token'], model_params)
    
    return resp["choices"][0]["message"]["content"]