app.py

from flask import Flask, request, Response
import logging
from llama_cpp import Llama
import threading
from huggingface_hub import snapshot_download

SYSTEM_PROMPT = "Ты — русскоязычный автоматический ассистент. Ты максимально точно и отвечаешь на запросы пользователя, используя русский язык."
SYSTEM_TOKEN = 1788
USER_TOKEN = 1404
BOT_TOKEN = 9225
LINEBREAK_TOKEN = 13

ROLE_TOKENS = {
    "user": USER_TOKEN,
    "bot": BOT_TOKEN,
    "system": SYSTEM_TOKEN
}

# Create a lock object
lock = threading.Lock()

app = Flask(__name__)
# Configure Flask logging
app.logger.setLevel(logging.DEBUG)  # Set the desired logging level

# Initialize the model when the application starts
#model_path = "../models/model-q4_K.gguf"  # Replace with the actual model path
#model_name = "model/ggml-model-q4_K.gguf"

#repo_name = "IlyaGusev/saiga2_13b_gguf"
#model_name = "model-q4_K.gguf"

repo_name = "IlyaGusev/saiga2_70b_gguf"
model_name = "ggml-model-q4_1.gguf"



snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)




def get_message_tokens(model, role, content):
    message_tokens = model.tokenize(content.encode("utf-8"))
    message_tokens.insert(1, ROLE_TOKENS[role])
    message_tokens.insert(2, LINEBREAK_TOKEN)
    message_tokens.append(model.token_eos())
    return message_tokens

def get_system_tokens(model):
    system_message = {
        "role": "system",
        "content": SYSTEM_PROMPT
    }
    return get_message_tokens(model, **system_message)

def get_system_tokens_for_preprompt(model, preprompt):
    system_message = {
        "role": "system",
        "content": preprompt
    }
    return get_message_tokens(model, **system_message)

app.logger.info('Evaluating system tokens start')
#system_tokens = get_system_tokens(model)
#model.eval(system_tokens)
app.logger.info('Evaluating system tokens end')

stop_generation = False

def generate_tokens(model, generator):
    global stop_generation
    app.logger.info('generate_tokens started')
    #with lock:
    try:
        for token in generator:            
            if token == model.token_eos() or stop_generation:
                stop_generation = False
                app.logger.info('Abort generating')
                yield b''  # End of chunk
                break
                
            token_str = model.detokenize([token])#.decode("utf-8", errors="ignore")
            yield token_str 
    except Exception as e:
        app.logger.info('generator exception')
        yield b''  # End of chunk

@app.route('/stop_generation', methods=['GET'])
def handler_stop_generation():
    global stop_generation
    stop_generation = True
    return Response('Stopped', content_type='text/plain')        
                
@app.route('/', methods=['GET', 'PUT', 'DELETE', 'PATCH'])
def generate_unknown_response():
    app.logger.info('unknown method: '+request.method)
    try:
        request_payload = request.get_json()
        app.logger.info('payload: '+request.get_json())
    except Exception as e:
        app.logger.info('payload empty')

    return Response('What do you want?', content_type='text/plain')
    
@app.route('/search_request', methods=['POST'])
def generate_search_request():
    global stop_generation
    stop_generation = False

    
    
    data = request.get_json()
    app.logger.info(data)
    user_query = data.get("query", "")
    preprompt = data.get("preprompt", "Ты — русскоязычный автоматический ассистент для написании запросов для поисковых систем на русском языке. Отвечай на сообщения пользователя только текстом поискового запроса, релевантным запросу пользователя. Если запрос пользователя уже хорош, используй его в качестве результата.")
    parameters = data.get("parameters", {})
    
    # Extract parameters from the request
    temperature = 0.01
    truncate = parameters.get("truncate", 1000)
    max_new_tokens = parameters.get("max_new_tokens", 1024)
    top_p = 0.8
    repetition_penalty = parameters.get("repetition_penalty", 1.2)
    top_k = 20
    return_full_text = parameters.get("return_full_text", False)

    model = Llama(
        model_path=model_name,
        n_ctx=2000,
        n_parts=1,
        #n_batch=100,
        logits_all=True,
        #n_threads=12,
        verbose=True,
        n_gpu_layers=40,
        n_gqa=8       #must be set for 70b models
    )
    
    tokens = get_system_tokens_for_preprompt(model, preprompt)
    tokens.append(LINEBREAK_TOKEN)        
    
    tokens = get_message_tokens(model=model, role="user", content=user_query[:200]) + [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]

    generator = model.generate(
        tokens,
        top_k=top_k,
        top_p=top_p,
        temp=temperature,
        repeat_penalty=repetition_penalty
    )

    # Use Response to stream tokens
    return Response(generate_tokens(model, generator), content_type='text/plain', status=200, direct_passthrough=True)
    
@app.route('/', methods=['POST'])
def generate_response():
    global stop_generation
    stop_generation = False

    
    data = request.get_json()
    app.logger.info(data)
    messages = data.get("messages", [])
    preprompt = data.get("preprompt", "")
    parameters = data.get("parameters", {})
    
    # Extract parameters from the request
    temperature = 0.02#parameters.get("temperature", 0.01)
    truncate = parameters.get("truncate", 1000)
    max_new_tokens = parameters.get("max_new_tokens", 1024)
    top_p = 80#parameters.get("top_p", 0.85)
    repetition_penalty = parameters.get("repetition_penalty", 1.2)
    top_k = 25#parameters.get("top_k", 30)
    return_full_text = parameters.get("return_full_text", False)

    
    model = Llama(
        model_path=model_name,
        n_ctx=2000,
        n_parts=1,
        #n_batch=100,
        logits_all=True,
        #n_threads=12,
        verbose=True,
        n_gpu_layers=40,
        n_gqa=8       #must be set for 70b models
    )
    
    # Generate the response
    #system_tokens = get_system_tokens(model)
    #tokens = system_tokens

    #if preprompt != "":
    #    tokens = get_system_tokens_for_preprompt(model, preprompt)
    #else:
    tokens = get_system_tokens(model)
    tokens.append(LINEBREAK_TOKEN)
    #model.eval(tokens)
        
    
    tokens = []
    
    for message in messages:#[:-1]:
        if message.get("from") == "assistant":
            message_tokens = get_message_tokens(model=model, role="bot", content=message.get("content", ""))
        else:
            message_tokens = get_message_tokens(model=model, role="user", content=message.get("content", ""))
    
        tokens.extend(message_tokens)
        #LINEBREAK_TOKEN)
        
    #app.logger.info('model.eval start')
    #model.eval(tokens)
    #app.logger.info('model.eval end')
    
    #last_message = messages[-1]
    #if last_message.get("from") == "assistant":
    #    last_message_tokens = get_message_tokens(model=model, role="bot", content=last_message.get("content", ""))
    #else:
    #    last_message_tokens = get_message_tokens(model=model, role="user", content=last_message.get("content", ""))
            
    tokens.extend([model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN])

    app.logger.info('Prompt:')
    app.logger.info(model.detokenize(tokens).decode("utf-8", errors="ignore"))

    app.logger.info('Generate started')
    generator = model.generate(
        tokens,
        top_k=top_k,
        top_p=top_p,
        temp=temperature,
        repeat_penalty=repetition_penalty
    )
    app.logger.info('Generator created')

    # Use Response to stream tokens
    return Response(generate_tokens(model, generator), content_type='text/plain', status=200, direct_passthrough=True)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=False, threaded=True)