Update app.py

app.py

@@ -15,342 +15,6 @@ from threading import Thread
 from time import sleep
 from fastapi.staticfiles import StaticFiles
 import gradio as gr
-
-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)
-
-@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
-)
-def generate_model_response(model, inputs):
-    print(f"Generating response for model: {model}")
-    response = model(inputs)
-    print(f"Response from {model}: {response}")
-    return remove_duplicates(response['choices'][0]['text'])
-
-def remove_repetitive_responses(responses):
-    unique_responses = {}
-    for response in responses:
-        if response not in unique_responses:
-            unique_responses[response] = response
-    return unique_responses
-
-async def process_message(message):
-    inputs = normalize_input(message)
-    with ThreadPoolExecutor() as executor:
-        futures = [
-            executor.submit(generate_model_response, model, inputs)
-            for model in global_data['models'].values()
-        ]
-        responses = []
-        for future in as_completed(futures):
-            try:
-                response = future.result()
-                responses.append(response)
-            except Exception as e:
-                print(f"Error with model: {e}")
-                responses.append("Error generating response. Please try again later.")  
-
-    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):
-    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():
-    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) 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