app.py

from pydantic import BaseModel
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
import re
import os
from dotenv import load_dotenv
import spaces
import requests
import random
from faker import Faker
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from threading import Thread
from time import sleep
from fastapi.staticfiles import StaticFiles
import gradio as gr
from typing import Dict, Any, Tuple
from urllib.parse import urlparse

load_dotenv()

HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

global_data = {
    'models': {},
}

model_configs = [
    {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-8B Instruct"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf", "name": "Gemma 2-9B IT"},
    {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf", "name": "Gemma 2-27B"},
    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf", "name": "Phi-3 Mini 128K Instruct"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf", "name": "Meta Llama 3.1-8B"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf", "name": "Qwen2 7B Instruct"},
    {"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf", "name": "Starcoder2 3B"},
    {"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf", "name": "Qwen2 1.5B Instruct"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-q2_k.gguf", "name": "Meta Llama 3.1-70B"},
    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"},
    {"repo_id": "Ffftdtd5dtft/Hermes-3-Llama-3.1-8B-IQ1_S-GGUF", "filename": "hermes-3-llama-3.1-8b-iq1_s-imat.gguf", "name": "Hermes 3 Llama 3.1-8B"},
    {"repo_id": "Ffftdtd5dtft/Phi-3.5-mini-instruct-Q2_K-GGUF", "filename": "phi-3.5-mini-instruct-q2_k.gguf", "name": "Phi 3.5 Mini Instruct"},
    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-70B Instruct"},
    {"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"},
    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-IQ2_XXS-GGUF", "filename": "phi-3-mini-128k-instruct-iq2_xxs-imat.gguf", "name": "Phi 3 Mini 128K Instruct XXS"},
    {"repo_id": "Ffftdtd5dtft/TinyLlama-1.1B-Chat-v1.0-IQ1_S-GGUF", "filename": "tinyllama-1.1b-chat-v1.0-iq1_s-imat.gguf", "name": "TinyLlama 1.1B Chat"},
    {"repo_id": "Ffftdtd5dtft/Mistral-NeMo-Minitron-8B-Base-IQ1_S-GGUF", "filename": "mistral-nemo-minitron-8b-base-iq1_s-imat.gguf", "name": "Mistral NeMo Minitron 8B Base"},
    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"}
]

class ModelManager:
    def __init__(self):
        self.models = {}

    def load_model(self, model_config):
        if model_config['name'] not in self.models:
            try:
                print(f"Loading model {model_config['name']}...")
                self.models[model_config['name']] = Llama.from_pretrained(
                    repo_id=model_config['repo_id'], 
                    filename=model_config['filename'], 
                    use_auth_token=HUGGINGFACE_TOKEN
                )
                print(f"Model {model_config['name']} loaded successfully.")
            except Exception as e:
                print(f"Error loading model {model_config['name']}: {e}")

    def load_all_models(self):
        with ThreadPoolExecutor() as executor:
            for config in model_configs:
                executor.submit(self.load_model, config)
        return self.models

model_manager = ModelManager()
global_data['models'] = model_manager.load_all_models()

class ChatRequest(BaseModel):
    message: str

def normalize_input(input_text):
    return input_text.strip()

def remove_duplicates(text):
    text = re.sub(r'(Hello there, how are you\? \[/INST\]){2,}', 'Hello there, how are you? [/INST]', text)
    text = re.sub(r'(How are you\? \[/INST\]){2,}', 'How are you? [/INST]', text)
    text = text.replace('[/INST]', '')
    lines = text.split('\n')
    unique_lines = []
    seen_lines = set()
    for line in lines:
        if line not in seen_lines:
            unique_lines.append(line)
            seen_lines.add(line)
    return '\n'.join(unique_lines)

PROXY_URL = "https://uhhy-fsfsfs.hf.space/valid"

def get_random_proxy():
    try:
        response = requests.get(PROXY_URL)
        proxies = response.text.splitlines()
        return random.choice(proxies)
    except Exception as e:
        print(f"Error fetching proxy: {e}")
        return None

fake = Faker()

def generate_fake_ip():
    return fake.ipv4()

def get_random_user_agent():
    user_agents = [
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
        "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7; rv:89.0) Gecko/20100101 Firefox/89.0",
        "Mozilla/5.0 (X11; Linux x86_64; rv:89.0) Gecko/20100101 Firefox/89.0",
        "Mozilla/5.0 (iPhone; CPU iPhone OS 14_6 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0.3 Mobile/15E148 Safari/604.1",
        "Mozilla/5.0 (iPad; CPU OS 14_6 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0.3 Mobile/15E148 Safari/604.1",
        "Mozilla/5.0 (Android 11; Mobile; rv:89.0) Gecko/89.0 Firefox/89.0"
    ]
    return random.choice(user_agents)

def get_model_name_from_url(url: str) -> str:
    """Extracts the model name from a Hugging Face model URL."""
    parsed_url = urlparse(url)
    path_parts = parsed_url.path.split('/')
    if len(path_parts) >= 2:
        return path_parts[-2]
    else:
        return "Unknown Model"

def get_model_config_by_name(model_name: str) -> Dict[str, Any]:
    """Finds the model configuration based on the model name."""
    for config in model_configs:
        if config['name'] == model_name:
            return config
    return {}  # Return an empty dictionary if not found

def load_model_from_url(url: str) -> Llama:
    """Loads a Llama model from a Hugging Face model URL."""
    model_name = get_model_name_from_url(url)
    model_config = get_model_config_by_name(model_name)
    if model_config:
        try:
            print(f"Loading model {model_name}...")
            model = Llama.from_pretrained(
                repo_id=model_config['repo_id'],
                filename=model_config['filename'],
                use_auth_token=HUGGINGFACE_TOKEN
            )
            print(f"Model {model_name} loaded successfully.")
            return model
        except Exception as e:
            print(f"Error loading model {model_name}: {e}")
    else:
        print(f"Model configuration not found for {model_name}")
    return None

async def generate_model_response(model: Llama, inputs: str) -> str:
    """Generates a response from the model."""
    try:
        print(f"Generating response for model: {model}")
        response = model(inputs)
        print(f"Response from {model}: {response}")
        return remove_duplicates(response['choices'][0]['text'])
    except Exception as e:
        print(f"Error with model: {e}")
        return "Error generating response. Please try again later."

def remove_repetitive_responses(responses: Dict[str, str]) -> Dict[str, str]:
    """Removes duplicate responses from a dictionary of model responses."""
    unique_responses = {}
    for model, response in responses.items():
        if response not in unique_responses:
            unique_responses[model] = response
    return unique_responses

@spaces.GPU(
    queue=False, 
    allow_gpu_memory=True, 
    timeout=0, 
    duration=0, 
    gpu_type='Tesla V100', 
    gpu_count=2, 
    gpu_memory_limit='32GB', 
    cpu_limit=4, 
    memory_limit='64GB',
    retry=True, 
    retry_delay=30, 
    priority='high',
    disk_limit='100GB',
    scratch_space='/mnt/scratch',
    network_bandwidth_limit='200Mbps',
    internet_access=True,
    precision='float16',
    batch_size=128,
    num_threads=16,
    logging_level='DEBUG',
    log_to_file=True,
    alert_on_failure=True,
    data_encryption=True,
    env_variables={'CUDA_VISIBLE_DEVICES': '0'},
    environment_type='conda',
    enable_checkpointing=True,
    resource_limits={'gpu': 'Tesla V100', 'cpu': 8, 'memory': '128GB'},
    hyperparameter_tuning=True, 
    prefetch_data=True, 
    persistent_storage=True, 
    auto_scaling=True, 
    security_level='high', 
    task_priority='urgent', 
    retries_on_timeout=True, 
    file_system='nfs', 
    custom_metrics={'throughput': '300GB/s', 'latency': '10ms'}, 
    gpu_utilization_logging=True, 
    job_isolation='container', 
    failure_strategy='retry', 
    gpu_memory_overcommit=True, 
    cpu_overcommit=True, 
    memory_overcommit=True, 
    enable_optimizations=True, 
    multi_gpu_strategy='data_parallel', 
    model_parallelism=True, 
    quantization='dynamic', 
    pruning='structured', 
    tensor_parallelism=True, 
    mixed_precision_training=True, 
    layerwise_lr_decay=True, 
    warmup_steps=500, 
    learning_rate_scheduler='cosine_annealing', 
    dropout_rate=0.3, 
    weight_decay=0.01, 
    gradient_accumulation_steps=8, 
    mixed_precision_loss_scale=128, 
    tensorboard_logging=True, 
    hyperparameter_search_space={'learning_rate': [1e-5, 1e-3], 'batch_size': [64, 256]}, 
    early_stopping=True, 
    early_stopping_patience=10, 
    input_data_pipeline='tf.data', 
    batch_normalization=True, 
    activation_function='relu', 
    optimizer='adam', 
    gradient_clipping=1.0, 
    checkpoint_freq=10, 
    experiment_name='deep_model_training', 
    experiment_tags=['nlp', 'deep_learning'], 
    adaptive_lr=True, 
    learning_rate_max=0.01, 
    learning_rate_min=1e-6, 
    max_steps=100000, 
    tolerance=0.01, 
    logging_frequency=10, 
    profile_gpu=True, 
    profile_cpu=True, 
    debug_mode=True, 
    save_best_model=True, 
    evaluation_metric='accuracy', 
    job_preemption='enabled', 
    preemptible_resources=True, 
    grace_period=60, 
    resource_scheduling='fifo', 
    hyperparameter_optimization_algorithm='bayesian', 
    distributed_training=True, 
    multi_node_training=True, 
    max_retries=5, 
    log_level='INFO', 
    secure_socket_layer=True, 
    data_sharding=True, 
    distributed_optimizer='horovod', 
    mixed_precision_support=True, 
    fault_tolerance=True, 
    external_gpu_resources=True, 
    disk_cache=True, 
    backup_enabled=True, 
    backup_frequency='daily', 
    task_grouping='dynamic', 
    instance_type='high_memory', 
    instance_count=3, 
    task_runtime='hours', 
    adaptive_memory_allocation=True, 
    model_versioning=True, 
    multi_model_support=True, 
    batch_optimization=True, 
    memory_prefetch=True, 
    data_prefetch_threads=16, 
    network_optimization=True, 
    model_parallelism_strategy='pipeline', 
    verbose_logging=True, 
    lock_on_failure=True, 
    data_compression=True, 
    inference_mode='batch', 
    distributed_cache_enabled=True, 
    dynamic_batching=True, 
    model_deployment=True, 
    latency_optimization=True, 
    multi_region_deployment=True, 
    multi_user_support=True, 
    job_scheduling='auto', 
    max_job_count=100, 
    suspend_on_idle=True, 
    hyperparameter_search_algorithm='random', 
    job_priority_scaling=True, 
    quantum_computing_support=True, 
    dynamic_resource_scaling=True, 
    runtime_optimization=True, 
    checkpoint_interval='30min', 
    max_gpu_temperature=80, 
    scale_on_gpu_utilization=True, 
    worker_threads=8
)
async def process_message(message: str) -> Tuple[str, str]:
    """Processes a user message and generates responses from multiple LLMs."""
    inputs = normalize_input(message)

    # Retrieve models from global_data and process responses
    responses = {}
    for model_name, model in global_data['models'].items():
        responses[model_name] = await generate_model_response(model, inputs)

    unique_responses = remove_repetitive_responses(responses)
    formatted_response = ""
    for model, response in unique_responses.items():
        formatted_response += f"**{model}:**\n{response}\n\n"

    curl_command = f"""
    curl -X POST -H "Content-Type: application/json" \\
         -d '{{"message": "{message}"}}' \\
         http://localhost:7860/generate
    """
    return formatted_response, curl_command

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app.mount("/", StaticFiles(directory="public", html=True), name="static")

@app.post("/generate")
async def generate_response(request: Request):
    """Handles API requests to generate responses."""
    data = await request.json()
    message = data.get("message")
    if not message:
        return JSONResponse(status_code=400, content={"error": "Message is required."})

    response, _ = await process_message(message)
    return JSONResponse(content={"response": response})

iface = gr.Interface(
    fn=process_message,
    inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
    outputs=[gr.Markdown(), gr.Textbox(label="cURL command")],
    title="Multi-Model LLM API",
    description="Enter a message and get responses from multiple LLMs.",
)

def anonymize_ip():
    """Continuously updates IP addresses to anonymize requests."""
    while True:
        sleep(0) 
        os.environ['HTTP_X_FORWARDED_FOR'] = generate_fake_ip()
        os.environ['REMOTE_ADDR'] = generate_fake_ip()

Thread(target=anonymize_ip).start()

if __name__ == "__main__":
    iface.launch(share=True)