analyse this spacemining.html :<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta name="description" content="Discover space mining and become part of the next space frontier. Learn, innovate, and explore with us.">
    <meta name="keywords" content="space mining, education, asteroid mining, space resources, learning modules">
    <meta name="author" content="Space Mining Education Team">
    <title>Space Mining Education Platform</title>
    <link rel="stylesheet" href="styles.css">
    <link rel="icon" href="favicon.ico" type="image/x-icon">
    <link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;700&display=swap" rel="stylesheet">
    <style>
        body {
            background: #0a0a0a;
            color: #f0f0f0;
            font-family: 'Roboto', sans-serif;
            margin: 0;
            padding: 0;
        }
        #app-container {
            max-width: 1400px;
            margin: auto;
            padding: 20px;
        }
        .branding {
            text-align: center;
            padding: 40px;
        }
        .branding img {
            width: 150px;
            height: auto;
        }
        .branding h1 {
            font-size: 3.5rem;
            font-weight: 700;
            color: #f0f0f0;
        }
        .nav-list {
            display: flex;
            justify-content: space-around;
            padding: 0;
            margin: 30px 0;
            list-style: none;
        }
        .nav-link {
            color: #00d1b2;
            text-decoration: none;
            font-size: 1.3rem;
            padding: 10px 20px;
            transition: color 0.3s;
        }
        .nav-link:hover {
            color: #ff5722;
        }
        #main-content {
            padding: 50px;
            background: #121212;
            border-radius: 20px;
            margin-bottom: 50px;
        }
        .content-section {
            margin-bottom: 50px;
        }
        .content-section h2 {
            font-size: 2.7rem;
            color: #00d1b2;
        }
        .content-section p {
            font-size: 1.3rem;
            color: #cccccc;
            line-height: 1.6;
        }
        .cta-button {
            background-color: #00d1b2;
            color: #ffffff;
            border: none;
            padding: 15px 30px;
            font-size: 1.5rem;
            cursor: pointer;
            transition: background-color 0.3s, transform 0.2s;
            border-radius: 30px;
            margin-top: 30px;
        }
        .cta-button:hover {
            background-color: #ff5722;
            transform: translateY(-3px);
        }
        #interactive-section {
            background: #1a1a1a;
            padding: 40px;
            border-radius: 15px;
            color: #ffffff;
            box-shadow: 0px 6px 15px rgba(0, 0, 0, 0.8);
        }
        #interactive-section h2 {
            font-size: 2.5rem;
            color: #ff9800;
        }
        #interactive-output {
            background-color: #000;
            color: #00d1b2;
            padding: 20px;
            margin-top: 20px;
            border-radius: 10px;
            text-align: left;
            height: 200px;
            overflow-y: auto;
        }
        #interactive-input {
            margin-top: 20px;
        }
        #interactive-input input {
            width: calc(100% - 100px);
            padding: 10px;
            font-size: 1.2rem;
            border: 2px solid #00d1b2;
            border-radius: 5px;
            background: #000;
            color: #ffffff;
        }
        #interactive-input button {
            width: 80px;
            padding: 10px;
            font-size: 1.2rem;
            background-color: #00d1b2;
            color: #ffffff;
            border: none;
            cursor: pointer;
            border-radius: 5px;
        }
    </style>
</head>
<body>
    <div id="app-container">
        <!-- Header and Navigation -->
        <header id="app-header" role="banner" class="branding">
            <img src="static/logo.png" alt="Space Mining Education Logo">
            <h1>Space Mining Education Platform</h1>
        </header>
        <nav role="navigation" aria-label="Main Navigation">
            <ul class="nav-list">
                <li><a href="#home" aria-label="Navigate to Home" class="nav-link">Home</a></li>
                <li><a href="#vision" aria-label="Navigate to Vision" class="nav-link">Vision</a></li>
                <li><a href="#modules" aria-label="Navigate to Learning Modules" class="nav-link">Modules</a></li>
                <li><a href="#interactive" aria-label="Navigate to Speco Game" class="nav-link">Speco Game</a></li>
                <li><a href="#contact" aria-label="Navigate to Contact" class="nav-link">Contact Us</a></li>
            </ul>
        </nav>

        <!-- Main Content -->
        <main id="main-content" role="main">
            <!-- Vision Section -->
            <section id="vision" class="content-section">
                <h2>Our Vision</h2>
                <p>We are building a future where humanity taps into the boundless resources of space to create a sustainable and prosperous civilization. We invite you to learn, innovate, and push the boundaries of space exploration with us.</p>
                <button class="cta-button" onclick="location.href='#modules'">Explore Learning Modules</button>
            </section>

            <!-- Speco Game Section -->
            <section id="interactive-section">
                <h2>Speco Game</h2>
                <p>Interact with our AI and explore space mining scenarios. From scanning asteroids to mining resources, Speco allows you to experience the challenges and rewards of space exploration.</p>
                <div id="interactive-output">Welcome to the Space Mining Game! Type 'start' to begin your journey.</div>
                <div id="interactive-input">
                    <input type="text" id="user-input" placeholder="Enter your command...">
                    <button onclick="handleUserInput()">Send</button>
                </div>
            </section>
        </main>

        <!-- Footer -->
        <footer id="app-footer" role="contentinfo">
            <div class="footer-content">
                <p>&copy; 2024 Space Mining Education Platform. All rights reserved.</p>
                <p><a href="#app-header" aria-label="Back to top" class="back-to-top">Back to top</a></p>
            </div>
        </footer>
    </div>

    <!-- JavaScript for Speco Game -->
    <script>
        const interactiveOutput = document.getElementById('interactive-output');
        const userInput = document.getElementById('user-input');

        async function handleUserInput() {
            const input = userInput.value.trim();
            userInput.value = '';

            if (!input) return;

            interactiveOutput.innerHTML += `<br><strong>You:</strong> ${input}`;

            try {
                const response = await fetch("http://127.0.0.1:5000/speco", {
                    method: "POST",
                    headers: {
                        "Content-Type": "application/json"
                    },
                    body: JSON.stringify({
                        prompt: input
                    })
                });

                if (!response.ok) {
                    throw new Error(`HTTP error! Status: ${response.status}`);
                }

                const data = await response.json();
                const responseText = data.response;

                interactiveOutput.innerHTML += `<br><strong>Speco:</strong> ${responseText}`;
                interactiveOutput.scrollTop = interactiveOutput.scrollHeight;
            } catch (error) {
                console.error("Error communicating with the server:", error);
                interactiveOutput.innerHTML += `<br><strong>Speco:</strong> An error occurred while generating the response. Retrying...`;
                setTimeout(handleUserInput, 3000);  // Retry after 3 seconds
            }
        }

        userInput.addEventListener('keydown', function(event) {
            if (event.key === 'Enter') {
                handleUserInput();
            }
        });

        // Connectivity check to ensure server connection
        async function checkServerConnection() {
            try {
                const response = await fetch("http://127.0.0.1:5000/ping", {
                    method: "GET",
                });
                if (response.ok) {
                    console.log("Connected to Flask server.");
                } else {
                    throw new Error("Server not reachable");
                }
            } catch (error) {
                console.error("Unable to connect to the server, retrying in 5 seconds...");
                setTimeout(checkServerConnection, 5000);  // Retry after 5 seconds
            }
        }

        // Check server connection initially
        checkServerConnection();
    </script>
</body>
</html>
 analyses this model :use_model.py : from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load the pre-trained GPT-2 tokenizer and model
print("Loading GPT-2 model and tokenizer...")
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Set the model to evaluation mode and move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Function to generate text
def generate_text(prompt, max_length=200):
    """
    Generates text based on the input prompt using GPT-2.

    Parameters:
        prompt (str): The initial text prompt to generate text from.
        max_length (int): Maximum length of the generated text.

    Returns:
        str: The generated text response.
    """
    # Encode the input prompt
    inputs = tokenizer.encode(prompt, return_tensors='pt').to(device)

    # Generate text with the model
    with torch.no_grad():
        outputs = model.generate(
            inputs,
            max_length=max_length + inputs.shape[1],
            do_sample=True,
            temperature=0.7,
            top_k=50,
            top_p=0.95,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            repetition_penalty=1.2,
            eos_token_id=tokenizer.eos_token_id
        )

    # Decode the generated text
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

# Example usage
if __name__ == "__main__":
    # Prompt input from the user
    prompt = input("Enter a prompt: ")

    # Generate text
    output = generate_text(prompt, max_length=200)
    print("\nGenerated Text:\n", output)
 analyse this flask :import os
import sys
import subprocess
import time
import torch
from flask import Flask, request, jsonify, render_template, send_from_directory
import logging
import requests
from importlib import metadata
import threading

os.environ['HF_HUB_DISABLE_SYMLINKS_WARNING'] = '1'

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger(__name__)

app = Flask(__name__, static_folder='static', template_folder='templates')

def install_missing_packages():
    try:
        required = {'flask', 'torch', 'transformers', 'requests'}
        installed = {pkg.metadata['Name'].lower() for pkg in metadata.distributions()}
        missing = required - installed

        if missing:
            logger.info(f"Installing missing packages: {missing}")
            subprocess.check_call([sys.executable, "-m", "pip", "install", *missing])
            logger.info("Missing packages installed successfully.")
        else:
            logger.info("All required packages are already installed.")
    except Exception as e:
        logger.error(f"Error during package installation: {e}")
        sys.exit(1)

def test_imports():
    try:
        global AutoModelForCausalLM, AutoTokenizer
        from transformers import AutoModelForCausalLM, AutoTokenizer
        logger.info("All package imports successful.")
    except ImportError as e:
        logger.error(f"ImportError during package testing: {e}")
        sys.exit(1)

def load_model():
    global model, tokenizer
    model_directory = "cerebras/Cerebras-GPT-590M"
    logger.info("Loading model and tokenizer...")
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_directory)
        model = AutoModelForCausalLM.from_pretrained(model_directory)
    except Exception as e:
        logger.error(f"Failed to load model from directory '{model_directory}': {e}")
        sys.exit(1)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    logger.info(f"Model loaded on device: {device}")

def test_model_inference():
    test_prompt = "Once upon a time"
    logger.info("Testing model inference...")
    try:
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        inputs = tokenizer(
            test_prompt,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=512
        )
        inputs = {key: value.to(model.device) for key, value in inputs.items()}

        with torch.no_grad():
            outputs = model.generate(
                inputs["input_ids"],
                attention_mask=inputs.get("attention_mask"),
                max_length=50,
                num_return_sequences=1,
                temperature=0.7,
                top_k=50,
                top_p=0.9,
                pad_token_id=tokenizer.eos_token_id,
                do_sample=True
            )

        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        logger.info(f"Model inference successful. Sample output: {response_text}")
    except Exception as e:
        logger.error(f"Model inference test failed: {e}")
        sys.exit(1)

def test_speco_endpoint():
    test_prompt = "Describe the process of photosynthesis."
    logger.info("Testing /speco endpoint...")
    try:
        api_url = "https://yuntian-deng-o1.hf.space/api/predict/"
        headers = {"Content-Type": "application/json"}
        payload = {"inputs": [test_prompt]}  # Adjusted to match API requirements

        response = requests.post(api_url, headers=headers, json=payload, timeout=30)

        if response.status_code == 200:
            response_data = response.json()
            logger.info(f"Speco endpoint test successful. Response: {response_data}")
            return True
        else:
            logger.error(f"Speco endpoint test failed with status code: {response.status_code}, Response: {response.text}")
            return False
    except requests.exceptions.RequestException as e:
        logger.error(f"RequestException during Speco endpoint test: {e}", exc_info=True)
        return False

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/generate', methods=['POST'])
def generate_response():
    try:
        data = request.get_json()
        prompt = data.get("prompt", "").strip()

        if not prompt:
            logger.warning("Invalid prompt received.")
            return jsonify({"response": "Invalid prompt."}), 400

        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        try:
            inputs = tokenizer(
                prompt,
                return_tensors="pt",
                padding=True,
                truncation=True,
                max_length=512
            )
            inputs = {key: value.to(model.device) for key, value in inputs.items()}
        except Exception as tokenization_error:
            error_message = f"Error during tokenization: {str(tokenization_error)}"
            logger.error(error_message)
            return jsonify({"response": error_message}), 500

        try:
            with torch.no_grad():
                outputs = model.generate(
                    inputs["input_ids"],
                    attention_mask=inputs.get("attention_mask"),
                    max_length=100,
                    num_return_sequences=1,
                    temperature=0.7,
                    top_k=50,
                    top_p=0.9,
                    pad_token_id=tokenizer.eos_token_id,
                    do_sample=True
                )
        except Exception as generation_error:
            error_message = f"Error during text generation: {str(generation_error)}"
            logger.error(error_message)
            return jsonify({"response": error_message}), 500

        try:
            response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
            return jsonify({"response": response_text})
        except Exception as decoding_error:
            error_message = f"Error during decoding: {str(decoding_error)}"
            logger.error(error_message)
            return jsonify({"response": error_message}), 500

    except Exception as e:
        error_message = f"Unexpected error: {str(e)}"
        logger.error(error_message, exc_info=True)
        return jsonify({"response": error_message}), 500

speco_available = False

@app.route('/speco', methods=['POST'])
def speco_response():
    global speco_available
    if not speco_available:
        logger.warning("Speco endpoint is not available.")
        return jsonify({"response": "Speco endpoint is not available."}), 503

    try:
        data = request.get_json()
        prompt = data.get("prompt", "").strip()

        if not prompt:
            logger.warning("Invalid prompt received for Speco.")
            return jsonify({"response": "Invalid prompt."}), 400

        api_url = "https://yuntian-deng-o1.hf.space/api/predict/"
        headers = {"Content-Type": "application/json"}
        payload = {"inputs": [prompt]}  # Adjusted to match API requirements

        response = requests.post(api_url, headers=headers, json=payload, timeout=30)

        if response.status_code != 200:
            error_msg = f"Failed to get response from Speco: Status code {response.status_code}, Response: {response.text}"
            logger.error(error_msg)
            return jsonify({"response": error_msg}), 500

        try:
            response_data = response.json()
            speco_response_text = response_data.get("data", "No response from Speco.")
            return jsonify({"response": speco_response_text})
        except ValueError as e:
            logger.error(f"Failed to parse JSON from Speco response: {str(e)}")
            return jsonify({"response": "Failed to parse JSON response from Speco."}), 500

    except Exception as e:
        error_message = f"Unexpected error in Speco endpoint: {str(e)}"
        logger.error(error_message, exc_info=True)
        return jsonify({"response": error_message}), 500

def continuous_speco_testing():
    while True:
        logger.info("Starting Speco endpoint test and fix process...")
        success = test_speco_endpoint()

        if success:
            logger.info("Speco endpoint is working as expected.")
        else:
            logger.warning("Speco endpoint encountered issues. Retrying in 60 seconds...")

        time.sleep(60)

def main():
    global speco_available
    threading.Thread(target=continuous_speco_testing, daemon=True).start()
    while True:
        try:
            logger.info("Starting initialization process...")
            install_missing_packages()
            test_imports()
            load_model()
            test_model_inference()
            speco_available = test_speco_endpoint()

            if speco_available:
                logger.info("Speco endpoint is available.")
            else:
                logger.warning("Speco endpoint is not available.")

            logger.info("All tests passed. Starting Flask server...")
            app.run(host="127.0.0.1", port=5000, debug=True)
            break
        except Exception as e:
            logger.error(f"An error occurred: {e}")
            logger.info("Retrying initialization in 5 seconds...")
            time.sleep(5)

if __name__ == "__main__":
    main()
analyze all the three and fix the problem why my llm is not working with this in a better way