Create app.py

app.py

@@ -0,0 +1,349 @@
+import os
+import sys
+import uvicorn
+from fastapi import FastAPI, Query
+from fastapi.responses import HTMLResponse
+from starlette.middleware.cors import CORSMiddleware
+from datasets import load_dataset, list_datasets
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from loguru import logger
+import concurrent.futures
+import psutil
+import asyncio
+import torch
+from tenacity import retry, stop_after_attempt, wait_fixed
+from huggingface_hub import HfApi, RepositoryNotFoundError
+from dotenv import load_dotenv
+
+load_dotenv()
+
+HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+if not HUGGINGFACE_TOKEN:
+    logger.error("Hugging Face token not found. Please set the HUGGINGFACE_TOKEN environment variable.")
+    sys.exit(1)
+
+datasets_dict = {}
+example_usage_list = []
+
+CACHE_DIR = os.path.expanduser("~/.cache/huggingface")
+os.makedirs(CACHE_DIR, exist_ok=True)
+os.environ["HF_HOME"] = CACHE_DIR
+os.environ["HF_TOKEN"] = HUGGINGFACE_TOKEN
+
+def initialize_model():
+    try:
+        logger.info("Initializing the base model and tokenizer.")
+        base_model_repo = "Yhhxhfh/test"
+        model = AutoModelForCausalLM.from_pretrained(base_model_repo, cache_dir=CACHE_DIR)
+        tokenizer = AutoTokenizer.from_pretrained(base_model_repo, cache_dir=CACHE_DIR)
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        pipeline_instance = pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            device=0 if torch.cuda.is_available() else -1
+        )
+        logger.info("Model and tokenizer initialized successfully.")
+        return pipeline_instance
+    except Exception as e:
+        logger.error(f"Error initializing model and tokenizer: {e}", exc_info=True)
+        sys.exit(1)
+
+pipeline_instance = initialize_model()
+
+@retry(stop=stop_after_attempt(3), wait=wait_fixed(5))
+def download_dataset(dataset_name):
+    try:
+        logger.info(f"Starting download for dataset: {dataset_name}")
+        datasets_dict[dataset_name] = load_dataset(dataset_name, trust_remote_code=True, cache_dir=CACHE_DIR)
+        create_example_usage(dataset_name)
+    except Exception as e:
+        logger.error(f"Error loading dataset {dataset_name}: {e}", exc_info=True)
+        raise
+
+def upload_model_to_hub():
+    try:
+        api = HfApi()
+        model_repo = "Yhhxhfh/test"
+        try:
+            api.repo_info(repo_id=model_repo)
+            logger.info(f"Model repository {model_repo} already exists.")
+        except RepositoryNotFoundError:
+            api.create_repo(repo_id=model_repo, private=False, token=HUGGINGFACE_TOKEN)
+            logger.info(f"Created model repository {model_repo}.")
+        logger.info(f"Pushing the model and tokenizer to {model_repo}.")
+        pipeline_instance.model.push_to_hub(model_repo, use_auth_token=HUGGINGFACE_TOKEN)
+        pipeline_instance.tokenizer.push_to_hub(model_repo, use_auth_token=HUGGINGFACE_TOKEN)
+        logger.info(f"Successfully pushed the model and tokenizer to {model_repo}.")
+    except Exception as e:
+        logger.error(f"Error uploading model to Hugging Face Hub: {e}", exc_info=True)
+
+def create_example_usage(dataset_name):
+    try:
+        logger.info(f"Creating example usage for dataset {dataset_name}")
+        example_prompts = [
+            "Translate the following catering menu from English to French:",
+            "Generate a catering menu for a wedding with vegetarian options:",
+            "Convert the following catering menu to a gluten-free version:",
+            "Provide a detailed catering menu for a corporate event including desserts:",
+            "Generate a children's birthday party catering menu with allergen-free items:"
+        ]
+        examples = []
+        for prompt in example_prompts:
+            generated_text = pipeline_instance(prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
+            examples.append({"prompt": prompt, "response": generated_text})
+        example_usage_list.append({"dataset_name": dataset_name, "examples": examples})
+        logger.info(f"Example usage created for dataset {dataset_name}")
+    except Exception as e:
+        logger.error(f"Error creating example usage for dataset {dataset_name}: {e}", exc_info=True)
+
+def unify_datasets():
+    try:
+        logger.info("Starting to unify datasets")
+        unified_dataset = None
+        for dataset in datasets_dict.values():
+            if unified_dataset is None:
+                unified_dataset = dataset
+            else:
+                unified_dataset = unified_dataset.concatenate(dataset)
+        datasets_dict['unified'] = unified_dataset
+        logger.info("Datasets successfully unified.")
+    except Exception as e:
+        logger.error(f"Error unifying datasets: {e}", exc_info=True)
+
+cpu_count = psutil.cpu_count(logical=False) or 1
+memory_available_mb = psutil.virtual_memory().available / (1024 * 1024)
+memory_per_download_mb = 100
+memory_available = int(memory_available_mb / memory_per_download_mb)
+gpu_count = torch.cuda.device_count()
+max_concurrent_downloads = min(cpu_count, memory_available, gpu_count * 2 if gpu_count else cpu_count)
+max_concurrent_downloads = max(1, max_concurrent_downloads)
+max_concurrent_downloads = min(10, max_concurrent_downloads)
+
+logger.info(f"Using up to {max_concurrent_downloads} concurrent workers for downloading datasets.")
+
+executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_concurrent_downloads)
+
+async def download_and_process_datasets():
+    dataset_names = list_datasets()
+    logger.info(f"Found {len(dataset_names)} datasets to download.")
+    loop = asyncio.get_event_loop()
+    tasks = []
+    for dataset_name in dataset_names:
+        task = loop.run_in_executor(executor, download_dataset, dataset_name)
+        tasks.append(task)
+    await asyncio.gather(*tasks)
+    unify_datasets()
+    upload_model_to_hub()
+
+async def main():
+    await download_and_process_datasets()
+
+asyncio.run(main())
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+message_history = []
+
+@app.get('/')
+async def index():
+    html_code = """
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <title>ChatGPT Chatbot</title>
+        <style>
+            body {
+                font-family: Arial, sans-serif;
+                margin: 0;
+                padding: 0;
+                background-color: #f4f4f4;
+            }
+            .container {
+                max-width: 800px;
+                margin: auto;
+                padding: 20px;
+            }
+            .chat-container {
+                background-color: #fff;
+                border-radius: 8px;
+                box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+                overflow: hidden;
+                margin-bottom: 20px;
+                animation: fadeInUp 0.5s ease forwards;
+                display: flex;
+                flex-direction: column;
+            }
+            .chat-box {
+                flex: 1;
+                overflow-y: auto;
+                padding: 10px;
+            }
+            .chat-input {
+                width: calc(100% - 20px);
+                border: none;
+                border-top: 1px solid #ddd;
+                padding: 10px;
+                font-size: 16px;
+                outline: none;
+            }
+            .chat-input:focus {
+                border-top: 1px solid #007bff;
+            }
+            .user-message {
+                margin-bottom: 10px;
+                padding: 8px 12px;
+                border-radius: 8px;
+                background-color: #007bff;
+                color: #fff;
+                max-width: 70%;
+                word-wrap: break-word;
+                align-self: flex-end;
+            }
+            .bot-message {
+                margin-bottom: 10px;
+                padding: 8px 12px;
+                border-radius: 8px;
+                background-color: #4CAF50;
+                color: #fff;
+                max-width: 70%;
+                word-wrap: break-word;
+            }
+            .toggle-history {
+                text-align: center;
+                cursor: pointer;
+                color: #007bff;
+                margin-bottom: 10px;
+            }
+            .history-container {
+                display: none;
+            }
+            .history-container.show {
+                display: block;
+            }
+            .history-container .history-content {
+                max-height: 200px;
+                overflow-y: auto;
+            }
+            @keyframes fadeInUp {
+                from {
+                    opacity: 0;
+                    transform: translateY(20px);
+                }
+                to {
+                    opacity: 1;
+                    transform: translateY(0);
+                }
+            }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1 style="text-align: center;">ChatGPT Chatbot</h1>
+            <div class="chat-container" id="chat-container">
+                <div class="chat-box" id="chat-box">
+                </div>
+                <input type="text" class="chat-input" id="user-input" placeholder="Type your message...">
+                <button onclick="retryLastMessage()">Retry Last Message</button>
+            </div>
+            <div class="toggle-history" onclick="toggleHistory()">Toggle History</div>
+            <div class="history-container" id="history-container">
+                <h2>Chat History</h2>
+                <div class="history-content" id="history-content"></div>
+            </div>
+        </div>
+        <script>
+            function toggleHistory() {
+                const historyContainer = document.getElementById('history-container');
+                historyContainer.classList.toggle('show');
+            }
+
+            function saveMessage(sender, message) {
+                const historyContent = document.getElementById('history-content');
+                const messageElement = document.createElement('div');
+                messageElement.className = `${sender}-message`;
+                messageElement.innerText = message;
+                historyContent.appendChild(messageElement);
+            }
+
+            function appendMessage(sender, message) {
+                const chatBox = document.getElementById('chat-box');
+                const messageElement = document.createElement('div');
+                messageElement.className = `${sender}-message`;
+                messageElement.innerText = message;
+                chatBox.appendChild(messageElement);
+                chatBox.scrollTop = chatBox.scrollHeight;
+            }
+
+            const chatContainer = document.getElementById('chat-container');
+            const chatBox = document.getElementById('chat-box');
+            const userInput = document.getElementById('user-input');
+
+            userInput.addEventListener('keyup', function(event) {
+                if (event.keyCode === 13) {
+                    event.preventDefault();
+                    sendMessage();
+                }
+            });
+
+            function sendMessage() {
+                const userMessage = userInput.value.trim();
+                if (userMessage === '') return;
+
+                saveMessage('user', userMessage);
+                appendMessage('user', userMessage);
+                userInput.value = '';
+
+                fetch(`/autocomplete?q=${encodeURIComponent(userMessage)}`)
+                .then(response => response.json())
+                .then(data => {
+                    const botMessages = data.result;
+                    botMessages.forEach(message => {
+                        saveMessage('bot', message);
+                        appendMessage('bot', message);
+                    });
+                })
+                .catch(error => {
+                    console.error('Error:', error);
+                });
+            }
+
+            function retryLastMessage() {
+                const lastUserMessage = document.querySelector('.user-message:last-of-type');
+                if (lastUserMessage) {
+                    userInput.value = lastUserMessage.innerText;
+                    sendMessage();
+                }
+            }
+        </script>
+    </body>
+    </html>
+    """
+    return HTMLResponse(content=html_code, status_code=200)
+
+@app.get('/autocomplete')
+async def autocomplete(q: str = Query(..., title='query')):
+    global message_history
+    message_history.append(('user', q))
+    try:
+        response = pipeline_instance(q, max_length=50, num_return_sequences=1)[0]['generated_text']
+        logger.debug(f"Successfully autocomplete, q:{q}, res:{response}")
+        return {"result": [response]}
+    except Exception as e:
+        logger.error(f"Ignored error in autocomplete: {e}", exc_info=True)
+        return {"result": []}
+
+if __name__ == '__main__':
+    port = int(os.getenv("PORT", 443))
+    uvicorn.run(app=app, host='0.0.0.0', port=port)