it seems that you are some serious chatbot with rethinkings, right?

Yes, I strive to provide thorough and thoughtful responses by considering various aspects of a question. I'm here to assist you with any information or guidance you need. How can I help you today?

but can we finally fix this chat to properly format its outputs from inference api returned? do you know anything to their hf inference/transformers docs or cannot help with anything?:

"""

IT OBVIOUSLY STARTED LOOKING MORE COHERENT NOW, BUT IT JUST WRAPPING EITHER HTML/CSS/JAVASCRIPT IN TRIPLE BACKTICKS AND DUAL ASTERIXES. WE CAN ACTUALLY USE THIS TO DISTINGUISH WHERE DO WE NEED TO WRAP THAT PART IN INDEPENDENT BLOCK FOR CODEMIRROR TO FORMAT ALL PROPERLY AND HIGHLIGHT.

THE LOGICS IN FORMATTINGS THERE SUPPOSE:

IF TRIPLE BACKTICKS FOLLOWING WITH "html" OR "css" OR "javascript" lowercase ("html, or css, or ```javascript), then we should distinguish it as an independent either html or css or javascript block, and look for closing triple backticks as:

OPENING TRIPLE BCKTICKS: ```html
some garbage here and AND TOTAL mess IN OTHER HYPOTHETICAL TAGS



then we will tell to codemirror to format everything accordingly and highlight in syntaxes and all esle.




we now need somehow to singularize a bot message in one whole thing but for codemirror to be able at the same time to distinguish in formattings independent html/css/javascript structure we excluded.


need to do a bot response as one text block which idently explicitly show to codemirror where html/css/javascript for it to syntax highlight it in one text message, not independent blocks or textareas.



<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Chatbot</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }

        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }

        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }

        .message {
            margin-bottom: 15px;
        }

        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }

        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }

        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }

        #user-input-container {
            display: flex;
            margin-top: 10px;
        }

        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }

        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }

        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
<div id="chat-container">
    <div id="messages"></div>
    <div id="user-input-container">
        <input id="user-input" type="text" placeholder="Type your message here...">
        <button id="send-button">Send</button>
    </div>
</div>

<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/htmlmixed/htmlmixed.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/css/css.min.js"></script>

<script>
    const messagesDiv = document.getElementById('messages');
    const userInput = document.getElementById('user-input');
    const sendButton = document.getElementById('send-button');
    const apiKey = "<TRUFFLEHOG_REDACTED_HUGGINGFACE>";

    // Maintain a rolling history of the chat
    const chatHistory = [];

    sendButton.addEventListener('click', async () => {
        const userMessage = userInput.value.trim();
        if (!userMessage) return;

        // Save user's message to chat history
        chatHistory.push({ role: "user", content: userMessage });

        // Display user message in the UI
        displayMessage(userMessage, 'user-message');
        userInput.value = '';

        try {
            // Send the entire chat history to Hugging Face inference API
            const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                method: 'POST',
                headers: {
                    'Authorization': `Bearer ${apiKey}`,
                    'Content-Type': 'application/json',
                    'x-use-cache': 'false',
                },
                body: JSON.stringify({
                    model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                    messages: chatHistory, // Send rolling history of the conversation
                    max_tokens: 2048,      // Allow large responses
                    temperature: 0.9       // Control randomness in bot responses
                })
            });

            const data = await response.json();
            
            // Get bot response and save to history
            const botMessage = data.choices[0]?.message?.content || "No response from the model.";
            chatHistory.push({ role: "assistant", content: botMessage });

            displayMessage(botMessage, 'bot-message');
        } catch (err) {
            console.error("API Error:", err);
            displayMessage("Error communicating with the model.", 'bot-message');
        }
    });

    function displayMessage(message, className) {
        const container = document.createElement('div');
        container.className = `message ${className}`;

        if (className === 'bot-message') {
            const plainText = extractPlainText(message);
            const codeBlocks = extractCodeBlocks(message);

            // Add plain text explanations
            if (plainText) {
                const textElement = document.createElement('p');
                textElement.innerHTML = parseSingleBackticks(plainText); // Convert inline `code`
                container.appendChild(textElement);
            }

            // Render code blocks in CodeMirror
            if (codeBlocks) {
                const codeContainer = document.createElement('div');
                codeContainer.className = 'code-block-container';
                CodeMirror(codeContainer, {
                    value: codeBlocks,
                    mode: 'htmlmixed', // Supports HTML, CSS, and JS
                    lineNumbers: true,
                    readOnly: true,
                    theme: 'dracula',
                });
                container.appendChild(codeContainer);
            }
        } else {
            container.textContent = message; // For user messages
        }

        messagesDiv.appendChild(container);
        messagesDiv.scrollTop = messagesDiv.scrollHeight; // Auto-scroll to bottom
    }

    function extractPlainText(message) {
        // Remove triple-backtick code blocks, preserving plain text
        const codeBlockRegex = /```(html|css|javascript)?\n[\s\S]*?```/g;
        return message.replace(codeBlockRegex, '').trim();
    }

    function extractCodeBlocks(message) {
        // Extract and combine all triple-backtick sections
        const codeBlockRegex = /```(html|css|javascript)?\n([\s\S]*?)```/g;
        let combinedCode = '';
        let match;

        while ((match = codeBlockRegex.exec(message)) !== null) {
            combinedCode += match[2].trim() + '\n';
        }

        return combinedCode.trim(); // Final cleanup
    }

    function parseSingleBackticks(text) {
        // Replace `inline backticks` with <code> for inline formatting
        return text.replace(/`(.*?)`/g, '<code>$1</code>');
    }
</script>
</body>
</html>


""".


try fix and just show a fixed full solid html code, without your explanatory texts all around.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Chatbot</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }

        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }

        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }

        .message {
            margin-bottom: 15px;
        }

        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }

        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }

        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }

        #user-input-container {
            display: flex;
            margin-top: 10px;
        }

        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }

        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }

        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/htmlmixed/htmlmixed.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/css/css.min.js"></script>

    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "<TRUFFLEHOG_REDACTED_HUGGINGFACE>";

        // Maintain a rolling history of the chat
        const chatHistory = [];

        sendButton.addEventListener('click', async () => {
            const userMessage = userInput.value.trim();
            if (!userMessage) return;

            // Save user's message to chat history
            chatHistory.push({ role: "user", content: userMessage });

            // Display user message in the UI
            displayMessage(userMessage, 'user-message');
            userInput.value = '';

            try {
                // Send the entire chat history to Hugging Face inference API
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: chatHistory, // Send rolling history of the conversation
                        max_tokens: 2048,      // Allow large responses
                        temperature: 0.9       // Control randomness in bot responses
                    })
                });

                const data = await response.json();
                
                // Get bot response and save to history
                const botMessage = data.choices[0]?.message?.content || "No response from the model.";
                chatHistory.push({ role: "assistant", content: botMessage });

                displayMessage(botMessage, 'bot-message');
            } catch (err) {
                console.error("API Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        });

        function displayMessage(message, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                const plainText = extractPlainText(message);
                const codeBlocks = extractCodeBlocks(message);

                // Add plain text explanations
                if (plainText) {
                    const textElement = document.createElement('p');
                    textElement.innerHTML = parseSingleBackticks(plainText); // Convert inline `code`
                    container.appendChild(textElement);
                }

                // Render code blocks in CodeMirror
                if (codeBlocks) {
                    const codeContainer = document.createElement('div');
                    codeContainer.className = 'code-block-container';
                    CodeMirror(codeContainer, {
                        value: codeBlocks,
                        mode: 'htmlmixed', // Supports HTML, CSS, and JS
                        lineNumbers: true,
                        readOnly: true,
                        theme: 'dracula',
                    });
                    container.appendChild(codeContainer);
                }
            } else {
                container.textContent = message; // For user messages
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight; // Auto-scroll to bottom
        }

        function extractPlainText(message) {
            // Remove triple-backtick code blocks, preserving plain text
            const codeBlockRegex = /```(html|css|javascript)?\n[\s\S]*?```/g;
            return message.replace(codeBlockRegex, '').trim();
        }

        function extractCodeBlocks(message) {
            // Extract and combine all triple-backtick sections
            const codeBlockRegex = /```(html|css|javascript)?\n([\s\S]*?)```/g;
            let combinedCode = '';
            let match;

            while ((match = codeBlockRegex.exec(message)) !== null) {
                combinedCode += match[2].trim() + '\n';
            }

            return combinedCode.trim(); // Final cleanup
        }

        function parseSingleBackticks(text) {
            // Replace `inline backticks` with <code> for inline formatting
            return text.replace(/`(.*?)`/g, '<code>$1</code>');
        }
    </script>
</body>
</html>

yeah, sadly you do not know anything about hf.space and their docs:

request:
"""

{
"max_tokens": 2048,
"messages": [
{
"content": "show some code",
"role": "user"
}
],
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"temperature": 0.9
}

""".

response:
"""

{
"object": "chat.completion",
"id": "",
"created": 1735776367,
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"system_fingerprint": "2.4.1-sha-d2ed52f",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Here are some examples of code in different programming languages:\n\nPython\npython\n# Hello World program\nprint(\"Hello, World!\")\n\n# Simple calculator\ndef calculate(num1, num2, operator):\n if operator == \"+\":\n return num1 + num2\n elif operator == \"-\":\n return num1 - num2\n elif operator == \"*\":\n return num1 * num2\n elif operator == \"/\":\n return num1 / num2\n else:\n return \"Invalid operator\"\n\n# Get user input\nnum1 = float(input(\"Enter the first number: \"))\nnum2 = float(input(\"Enter the second number: \"))\noperator = input(\"Enter the operator (+, -, *, /): \")\n\n# Calculate and print the result\nresult = calculate(num1, num2, operator)\nprint(f\"{num1} {operator} {num2} = {result}\")\n\n\nJava\njava\n// Hello World program\npublic class HelloWorld {\n public static void main(String[] args) {\n System.out.println(\"Hello, World!\");\n }\n}\n\n// Simple calculator\nimport java.util.Scanner;\n\npublic class Calculator {\n public static void main(String[] args) {\n Scanner scanner = new Scanner(System.in);\n System.out.print(\"Enter the first number: \");\n double num1 = scanner.nextDouble();\n System.out.print(\"Enter the second number: \");\n double num2 = scanner.nextDouble();\n System.out.print(\"Enter the operator (+, -, *, /): \");\n String operator = scanner.next();\n\n double result;\n switch (operator) {\n case \"+\":\n result = num1 + num2;\n break;\n case \"-\":\n result = num1 - num2;\n break;\n case \"*\":\n result = num1 * num2;\n break;\n case \"/\":\n result = num1 / num2;\n break;\n default:\n result = 0;\n System.out.println(\"Invalid operator\");\n return;\n }\n\n System.out.println(f\"{num1} {operator} {num2} = {result}\");\n }\n}\n\n\nC++\ncpp\n// Hello World program\n#include <iostream>\nint main() {\n std::cout << \"Hello, World!\";\n return 0;\n}\n\n// Simple calculator\n#include <iostream>\n\nint main() {\n double num1, num2;\n char operator;\n\n std::cout << \"Enter the first number: \";\n std::cin >> num1;\n std::cout << \"Enter the second number: \";\n std::cin >> num2;\n std::cout << \"Enter the operator (+, -, *, /): \";\n std::cin >> operator;\n\n double result;\n switch (operator) {\n case '+':\n result = num1 + num2;\n break;\n case '-':\n result = num1 - num2;\n break;\n case '*':\n result = num1 * num2;\n break;\n case '/':\n result = num1 / num2;\n break;\n default:\n std::cout << \"Invalid operator\";\n return 0;\n }\n\n std::cout << f\"{num1} {operator} {num2} = {result}\";\n}\n\n\nJavaScript\njavascript\n// Hello World program\nconsole.log(\"Hello, World!\");\n\n// Simple calculator\nlet num1 = parseInt(prompt(\"Enter the first number:\"));\nlet num2 = parseInt(prompt(\"Enter the second number:\"));\nlet operator = prompt(\"Enter the operator (+, -, *, /):\");\n\nlet result;\nswitch (operator) {\n case \"+\":\n result = num1 + num2;\n break;\n case \"-\":\n result = num1 - num2;\n break;\n case \"*\":\n result = num1 * num2;\n break;\n case \"/\":\n result = num1 / num2;\n break;\n default:\n console.log(\"Invalid operator\");\n return;\n}\n\nconsole.log(`${num1} ${operator} ${num2} = ${result}`);\n"
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 39,
"completion_tokens": 845,
"total_tokens": 884
}
}

""".

this is how messy it looks in actual chat:

"""

show some code

Here are some examples of code in different programming languages: Python python # Hello World program print("Hello, World!") # Simple calculator def calculate(num1, num2, operator): if operator == "+": return num1 + num2 elif operator == "-": return num1 - num2 elif operator == "*": return num1 * num2 elif operator == "/": return num1 / num2 else: return "Invalid operator" # Get user input num1 = float(input("Enter the first number: ")) num2 = float(input("Enter the second number: ")) operator = input("Enter the operator (+, -, *, /): ") # Calculate and print the result result = calculate(num1, num2, operator) print(f"{num1} {operator} {num2} = {result}") java // Hello World program public class HelloWorld { public static void main(String[] args) { System.out.println("Hello, World!"); } } // Simple calculator import java.util.Scanner; public class Calculator { public static void main(String[] args) { Scanner scanner = new Scanner(System.in); System.out.print("Enter the first number: "); double num1 = scanner.nextDouble(); System.out.print("Enter the second number: "); double num2 = scanner.nextDouble(); System.out.print("Enter the operator (+, -, *, /): "); String operator = scanner.next(); double result; switch (operator) { case "+": result = num1 + num2; break; case "-": result = num1 - num2; break; case "*": result = num1 * num2; break; case "/": result = num1 / num2; break; default: result = 0; System.out.println("Invalid operator"); return; } System.out.println(f"{num1} {operator} {num2} = {result}"); } } cpp // Hello World program #include int main() { std::cout << "Hello, World!"; return 0; } // Simple calculator #include int main() { double num1, num2; char operator; std::cout << "Enter the first number: "; std::cin >> num1; std::cout << "Enter the second number: "; std::cin >> num2; std::cout << "Enter the operator (+, -, *, /): "; std::cin >> operator; double result; switch (operator) { case '+': result = num1 + num2; break; case '-': result = num1 - num2; break; case '*': result = num1 * num2; break; case '/': result = num1 / num2; break; default: std::cout << "Invalid operator"; return 0; } std::cout << f"{num1} {operator} {num2} = {result}"; } javascript // Hello World program console.log("Hello, World!"); // Simple calculator let num1 = parseInt(prompt("Enter the first number:")); let num2 = parseInt(prompt("Enter the second number:")); let operator = prompt("Enter the operator (+, -, *, /):"); let result; switch (operator) { case "+": result = num1 + num2; break; case "-": result = num1 - num2; break; case "*": result = num1 * num2; break; case "/": result = num1 / num2; break; default: console.log("Invalid operator"); return; } console.log(${num1} ${operator} ${num2} = ${result});
1

Java

2

C++

3

JavaScript

""".

it still improperly all formatted in return from their backend to chat's ui. you can redo this code from scratch if you wanting to, but it seems you cannot perform this task properly.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Proper Code Formatting</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/htmlmixed/htmlmixed.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/css/css.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/clike/clike.min.js"></script>

    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "<TRUFFLEHOG_REDACTED_HUGGINGFACE>";

        // Maintain a rolling history of the chat
        const chatHistory = [];

        sendButton.addEventListener('click', async () => {
            const userMessage = userInput.value.trim();
            if (!userMessage) return;

            // Save user's message to chat history
            chatHistory.push({ role: "user", content: userMessage });

            // Display user message in the UI
            displayMessage(userMessage, 'user-message');
            userInput.value = '';

            try {
                // Send the entire chat history to Hugging Face inference API
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: chatHistory, // Send rolling history of the conversation
                        max_tokens: 2048,      // Allow large responses
                        temperature: 0.9       // Control randomness in bot responses
                    })
                });

                const data = await response.json();
                
                // Get bot response and save to history
                const botMessage = data.choices[0]?.message?.content || "No response from the model.";
                chatHistory.push({ role: "assistant", content: botMessage });

                displayMessage(botMessage, 'bot-message');
            } catch (err) {
                console.error("API Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        });

        function displayMessage(message, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Split message into plain text and code blocks
                const messageParts = parseMessage(message);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content); // Convert inline `code`
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';
                        CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });
                        container.appendChild(codeContainer);
                    }
                });
            } else {
                container.textContent = message; // For user messages
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight; // Auto-scroll to bottom
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|(`[^`]*`)|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block with optional language
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code with single backticks
                    parts.push({
                        type: 'text',
                        content: result[3]
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            // Replace `inline backticks` with <code> for inline formatting
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            switch (language ? language.toLowerCase() : '') {
                case 'html':
                    return 'htmlmixed';
                case 'css':
                    return 'css';
                case 'javascript':
                case 'js':
                    return 'javascript';
                case 'python':
                    return 'python';
                case 'java':
                    return 'text/x-java';
                case 'cpp':
                case 'c++':
                    return 'text/x-c++src';
                case 'c':
                    return 'text/x-csrc';
                case 'csharp':
                case 'c#':
                    return 'text/x-csharp';
                default:
                    return 'plaintext';
            }
        }
    </script>
</body>
</html>

now it looks normally as need, but:

"""

xxxxxxxxxx
1
# Import necessary libraries
2
import numpy as np
3
from tensorflow import keras
4
from tensorflow.keras import layers
5
import matplotlib.pyplot as plt
6
​
7
# Load MNIST dataset
8
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
9
​
10
# Scale pixel values between 0 and 1
11
X_train = X_train.astype('float32') / 255.0
12
X_test = X_test.astype('float32') / 255.0
13
​
14
# Add a new dimension for convolutional layer
15
X_train = X_train[..., np.newaxis]
16
X_test = X_test[..., np.newaxis]
17
​
18
# Define the model architecture
19
def build_model(input_shape, num_classes):
20
    inputs = layers.Input(shape=input_shape)
21
    x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
22
    x = layers.MaxPooling2D((2, 2))(x)
23
    x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)
24
    x = layers.MaxPooling2D((2, 2))(x)
25
    x = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)
26
    x = layers.Flatten()(x)
27
    x = layers.Dense(128, activation='relu')(x)
28
    x = layers.Dense(num_classes, activation='softmax')(x)
29
    return keras.Model(inputs=inputs, outputs=x)
30
​
31
# Build and compile the model
32
model = build_model((28, 28, 1), 2)

""".

initially looks obscured. only after viewport dimension update or direct click at this code block frame inside bot response in messge, it getting rendered and showing the code. then we would need some "requestanimationframe" chat UI updating mechanism which will auto-update chat UI on message returned from backend, right? you know it better how it should be there.

You're absolutely right. The issue arises because the CodeMirror instances are not properly rendered when they are first added to the DOM. This often happens because the rendering engine hasn't fully updated the layout before the CodeMirror initializes, leading to display issues. To fix this, we need to ensure that the CodeMirror instances refresh after they've been added to the DOM.

We can achieve this by using requestAnimationFrame to schedule a refresh call on the CodeMirror instance after the browser has had a chance to render the new elements. Here's the updated code with the necessary adjustments:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Proper Code Formatting</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/htmlmixed/htmlmixed.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/css/css.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/clike/clike.min.js"></script>

    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";

        // Maintain a rolling history of the chat
        const chatHistory = [];

        sendButton.addEventListener('click', async () => {
            const userMessage = userInput.value.trim();
            if (!userMessage) return;

            // Save user's message to chat history
            chatHistory.push({ role: "user", content: userMessage });

            // Display user message in the UI
            displayMessage(userMessage, 'user-message');
            userInput.value = '';

            try {
                // Send the entire chat history to Hugging Face inference API
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: chatHistory, // Send rolling history of the conversation
                        max_tokens: 2048,      // Allow large responses
                        temperature: 0.9       // Control randomness in bot responses
                    })
                });

                const data = await response.json();
                
                // Get bot response and save to history
                const botMessage = data.choices[0]?.message?.content || "No response from the model.";
                chatHistory.push({ role: "assistant", content: botMessage });

                displayMessage(botMessage, 'bot-message');
            } catch (err) {
                console.error("API Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        });

        function displayMessage(message, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Split message into plain text and code blocks
                const messageParts = parseMessage(message);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content); // Convert inline `code`
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Ensure the CodeMirror instance refreshes after being added to the DOM
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });
            } else {
                container.textContent = message; // For user messages
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight; // Auto-scroll to bottom
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block with optional language
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code with single backticks
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            // Replace `inline backticks` with <code> for inline formatting
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            switch (language ? language.toLowerCase() : '') {
                case 'html':
                    return 'htmlmixed';
                case 'css':
                    return 'css';
                case 'javascript':
                case 'js':
                    return 'javascript';
                case 'python':
                    return 'python';
                case 'java':
                    return 'text/x-java';
                case 'cpp':
                case 'c++':
                    return 'text/x-c++src';
                case 'c':
                    return 'text/x-csrc';
                case 'csharp':
                case 'c#':
                    return 'text/x-csharp';
                default:
                    return 'plaintext';
            }
        }
    </script>
</body>
</html>


Explanation of the Fix:

Issue: The CodeMirror instances are not fully rendering when added to the DOM, causing them to appear blank or require user interaction to display properly.

Solution: After initializing a CodeMirror instance, we use requestAnimationFrame to schedule a call to codeMirrorInstance.refresh(). This ensures that the CodeMirror editor refreshes after the browser has rendered the DOM changes, allowing it to display correctly.

Modified Section in displayMessage Function:

if (part.type === 'code') {
    const codeContainer = document.createElement('div');
    codeContainer.className = 'code-block-container';

    // Initialize CodeMirror instance
    const codeMirrorInstance = CodeMirror(codeContainer, {
        value: part.content,
        mode: getCodeMirrorMode(part.language),
        lineNumbers: true,
        readOnly: true,
        theme: 'dracula',
    });

    // Ensure the CodeMirror instance refreshes after being added to the DOM
    requestAnimationFrame(() => {
        codeMirrorInstance.refresh();
    });

    container.appendChild(codeContainer);
}


Why This Works:

requestAnimationFrame: This method tells the browser that you wish to perform an animation and requests that the browser call a specified function before the next repaint. By placing codeMirrorInstance.refresh() inside, we ensure it runs after the browser has had a chance to lay out the DOM elements.

codeMirrorInstance.refresh(): This method tells CodeMirror to recalculate its size and redraw the content, which fixes rendering issues that occur when the editor is initialized in an off-screen or otherwise hidden state.

Additional Notes:

Ensure Correct API Key: Replace "your_hf_api_key_here" with your actual Hugging Face API key.

CodeMirror Modes: I've added additional modes for Python and C-like languages to support syntax highlighting for various programming languages.

Robust Message Parsing: The parseMessage function now correctly handles inline code and plain text, ensuring that all parts of the bot's response are appropriately rendered.

With these changes, the code blocks should render correctly immediately after they are added to the chat, without requiring user interaction or resizing the viewport.

okay, now it all renders properly.

now about the hardest thing in continuous chattings with bot in that chat ui.

if you set "max_tokens" to any higher value than 2048, then backend returns something as:

"""

{
"error": "Input validation error: inputs tokens + max_new_tokens must be <= 4096. Given: 42 inputs tokens and 4096 max_new_tokens",
"error_type": "validation"
}

""".

can you comprehend how it all should be there for a properous continuous chat interaction with that bot, so it could also see and consider other user or bot messages in chat context-length? what about context-window in that case, can we do an endless chat context rolling when bot sees some window through context but user's frontend ui keeps all the previous context?

the other question is how we attach a summarizer window for bot to keep and consider it altogether with its context-window to previous chat context length in some range to actually expand bot's overall awareness in overall chat context-length?

also, can we change a theme to something else and darky as well?

You're absolutely correct; handling the context window limitations is crucial for creating a smooth and continuous chat experience with the model. I'll address your questions step by step, explaining how to manage the context length, implement summarization, and change the theme to another dark variant.

1. Managing the Context Length Limitations

Understanding the Issue:

Language models like the one you're using have a maximum context length. In your case, the total number of tokens (input tokens plus max_new_tokens) must not exceed 4096 tokens. If you attempt to set max_new_tokens higher than this limit or if your input (conversation history) grows too large, you'll encounter errors like:

{
    "error": "Input validation error: `inputs` tokens + `max_new_tokens` must be <= 4096. Given: 42 `inputs` tokens and 4096 `max_new_tokens`",
    "error_type": "validation"
}


Solution:

To maintain a continuous conversation within the token limit, you can:

Implement a sliding window of recent messages.
Summarize earlier messages to reduce token count.
Implementing a Sliding Window of Recent Messages

Explanation:

Only include the most recent messages in the conversation history that fit within the context window. This allows the model to consider the latest interactions without exceeding the token limit.

Implementation:

Here's how you can adjust your code:

// Set your desired max_tokens and calculate allowable input tokens
const MAX_TOKENS = 4096;
const MAX_NEW_TOKENS = 1024; // Adjust as needed
const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

// Function to estimate the number of tokens
function estimateTokens(text) {
    // Simple estimation: 1 token ≈ 4 characters (rough estimate)
    return Math.ceil(text.length / 4);
}

sendButton.addEventListener('click', async () => {
    const userMessage = userInput.value.trim();
    if (!userMessage) return;

    // Add user's message
    chatHistory.push({ role: "user", content: userMessage });

    // Estimate token usage
    let totalTokens = 0;
    const messagesToSend = [];
    // Start from the end and include messages until token limit is reached
    for (let i = chatHistory.length - 1; i >= 0; i--) {
        const tokens = estimateTokens(chatHistory[i].content);
        if (totalTokens + tokens > MAX_INPUT_TOKENS) break;
        totalTokens += tokens;
        messagesToSend.unshift(chatHistory[i]); // Build messages in reverse
    }

    // Display user message
    displayMessage(userMessage, 'user-message');
    userInput.value = '';

    try {
        const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
            method: 'POST',
            headers: {
                'Authorization': `Bearer ${apiKey}`,
                'Content-Type': 'application/json',
                'x-use-cache': 'false',
            },
            body: JSON.stringify({
                model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                messages: messagesToSend,
                max_tokens: MAX_NEW_TOKENS,
                temperature: 0.9
            })
        });

        const data = await response.json();

        const botMessage = data.choices[0]?.message?.content || "No response from the model.";
        chatHistory.push({ role: "assistant", content: botMessage });

        displayMessage(botMessage, 'bot-message');
    } catch (err) {
        console.error("API Error:", err);
        displayMessage("Error communicating with the model.", 'bot-message');
    }
});


Notes:

Token Estimation: The estimation is rough; if you need precise control, consider using a tokenizer library compatible with your model.
Discarding Old Messages: This approach prioritizes recent messages, which often are more relevant in a conversation.
Implementing Conversation Summarization

Explanation:

To preserve the essence of the conversation without exceeding the token limit, you can summarize older messages and include the summary in the context.

Implementation:

Create a Function to Summarize Messages:
async function summarizeMessages(messages) {
    const summaryPrompt = messages.map(msg => `${msg.role}: ${msg.content}`).join('\n');
    const response = await fetch('https://api-inference.huggingface.co/v1/complete', {
        method: 'POST',
        headers: {
            'Authorization': `Bearer ${apiKey}`,
            'Content-Type': 'application/json',
            'x-use-cache': 'false',
        },
        body: JSON.stringify({
            model: "openai/gpt-3.5-turbo",
            prompt: `Summarize the following conversation briefly:\n${summaryPrompt}\nSummary:`,
            max_tokens: 150,
            temperature: 0.5,
        })
    });

    const data = await response.json();
    return data.choices[0]?.text.trim();
}

Integrate Summarization into the Chat Flow:
sendButton.addEventListener('click', async () => {
    const userMessage = userInput.value.trim();
    if (!userMessage) return;

    // Add user's message
    chatHistory.push({ role: "user", content: userMessage });

    // Estimate token usage
    let totalTokens = estimateTokens(chatHistory.map(m => m.content).join(' '));

    if (totalTokens > MAX_INPUT_TOKENS) {
        // Summarize conversation
        const messagesToSummarize = chatHistory.slice(0, -5); // Summarize all except last 5 messages
        const summary = await summarizeMessages(messagesToSummarize);

        // Replace old messages with summary
        chatHistory.splice(0, chatHistory.length - 5);
        chatHistory.unshift({ role: "system", content: `Summary of previous conversation: ${summary}` });
    }

    // Display user message
    displayMessage(userMessage, 'user-message');
    userInput.value = '';

    // Proceed to send messages similar to previous code
    // ...

});


Notes:

Model for Summarization: If the current model isn't suitable for summarization, you may want to use a dedicated summarization model.
API Costs and Latency: Summarization adds an extra API call and may increase latency. It also consumes additional tokens and may incur additional costs if using a paid API.
2. Expanding the Bot's Overall Awareness

By combining both strategies—sliding window and summarization—you can effectively expand the bot's awareness:

Recent Messages: Include in full for immediate context.
Summarized Past Conversations: Provide condensed context of earlier interactions.

This ensures that the assistant has access to both the detailed recent exchanges and a broader understanding of prior discussions without exceeding the token limit.

3. Changing the Theme to Another Dark Variant

To change the theme of your chat UI to a different dark theme, you can modify the CSS and use a different CodeMirror theme.

Choosing a New CodeMirror Theme:

CodeMirror offers various themes. For instance, let's switch to the "Material Dark" theme.

Include the Theme's CSS:
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/material-darker.min.css">

Update the CodeMirror Initialization:

In your JavaScript code where you initialize CodeMirror, update the theme:

const codeMirrorInstance = CodeMirror(codeContainer, {
    // ... other options ...
    theme: 'material-darker',
});


Modify the UI CSS:

Adjust the CSS styles to match the new theme's aesthetic.

body {
    background-color: #212121;
    color: #e0e0e0;
    /* Other styles */
}

#messages {
    background-color: #303030;
    /* Other styles */
}

.user-message {
    background-color: #424242;
    /* Other styles */
}

.bot-message {
    background-color: #37474f;
    /* Other styles */
}

/* Update other components as needed */


Previewing Themes:

You can preview available CodeMirror themes here and select one that suits your preference.

Full Updated HTML Code

Here's the updated HTML code incorporating all the changes:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Context Management and Custom Theme</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/material-darker.min.css">
    <style>
        body {
            font-family: 'Fira Code', monospace;
            background-color: #212121;
            color: #e0e0e0;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #303030;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #424242;
            color: #fff;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #37474f;
            color: #fff;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #263238;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #263238;
            color: #fff;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #546e7a;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #455a64;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- CodeMirror Modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes as needed -->
    <!-- Include your token estimation library if you have one -->
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";

        // Maximum tokens
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Adjust as needed
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        // Maintain chat history
        let chatHistory = [];

        // Token estimation function
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        async function summarizeMessages(messages) {
            const summaryPrompt = messages.map(msg => `${msg.role}: ${msg.content}`).join('\n');
            const response = await fetch('https://api-inference.huggingface.co/v1/complete', {
                method: 'POST',
                headers: {
                    'Authorization': `Bearer ${apiKey}`,
                    'Content-Type': 'application/json',
                    'x-use-cache': 'false',
                },
                body: JSON.stringify({
                    model: "openai/gpt-3.5-turbo",
                    prompt: `Summarize the following conversation briefly:\n${summaryPrompt}\nSummary:`,
                    max_tokens: 150,
                    temperature: 0.5,
                })
            });

            const data = await response.json();
            return data.choices[0]?.text.trim();
        }

        sendButton.addEventListener('click', async () => {
            const userMessage = userInput.value.trim();
            if (!userMessage) return;

            // Add user's message
            chatHistory.push({ role: "user", content: userMessage });

            // Estimate token usage
            let totalTokens = estimateTokens(chatHistory.map(m => m.content).join(' '));

            if (totalTokens > MAX_INPUT_TOKENS) {
                // Summarize conversation
                const messagesToSummarize = chatHistory.slice(0, -5); // Summarize all except last 5 messages
                const summary = await summarizeMessages(messagesToSummarize);

                // Replace old messages with summary
                chatHistory = [
                    { role: "system", content: `Summary of previous conversation: ${summary}` },
                    ...chatHistory.slice(-5)
                ];
            }

            // Display user message
            displayMessage(userMessage, 'user-message');
            userInput.value = '';

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: chatHistory,
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.9
                    })
                });

                const data = await response.json();

                const botMessage = data.choices[0]?.message?.content || "No response from the model.";
                chatHistory.push({ role: "assistant", content: botMessage });

                displayMessage(botMessage, 'bot-message');
            } catch (err) {
                console.error("API Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        });

        function displayMessage(message, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Split message into plain text and code blocks
                const messageParts = parseMessage(message);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'material-darker',
                        });

                        // Refresh CodeMirror after rendering
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });
            } else {
                container.textContent = message;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function parseMessage(message) {
            // Your existing parseMessage code
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block with optional language
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code with single backticks
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            // Your existing getCodeMirrorMode code
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }
    </script>
</body>
</html>


Make sure to replace "your_hf_api_key_here" with your actual Hugging Face API key.

Final Notes

Accuracy of Token Estimation: The method used here for estimating tokens (text.length / 4) is an approximation. For more accurate token counts, consider using a tokenizer library specific to your model's tokenizer.

Performance Considerations: Summarization can add latency. You may want to implement caching mechanisms or optimize how often summarization occurs.

Model Selection for Summarization: If the model you're using isn't well-suited for summarization, you might need to use a different model or API endpoint that's specialized for summarization tasks.

Theme Customization: Feel free to experiment with different themes and CSS styles to achieve the desired look and feel for your chat UI.

I hope this helps you implement continuous chat interaction with proper context management and customize the theme as per your preference. If you have any more questions or need further clarification on any part, feel free to ask!

formattings and highlightings messed. better return all from previous code related to formattings and highlightings.

do we need to properly ID messages?

also, maybe it is much easier and righter to just allow that nlp ai model itself to summarize whole chat context as it goes and moves further in visible context-window?

we need somehow to instruct our nlp ai model through user initial or each subsequent message being sent to that nlp ai to summarize everything previously seen in chat context-length and just place it all into its header in responses in some tagging system as:

[|||/ user initial instruct to that model it interacts with about summarization]
(actual user's initial message)

[|||/||||/||||||/|||||||/|||||||||/||||||||/ bot's exponentially formatted summarization in some token range]
(actual bot response in message)

we then can hide these headers placeholders in messages in chat ui based on their predefined format as need.

yes, it will waste some tokens overally but what is the difference if we will use the same third-party summarizer? I think model should summarize itself, not something out there.

Acknowledgment and Understanding:

You're absolutely right, and I appreciate your insights. Let's address your points step by step:

Formatting and Highlighting Issues:

I'll ensure that the code snippets are properly formatted and highlighted, reverting to the previous methods that worked correctly.

Message Identification:

Properly identifying messages can be beneficial, especially if we need to manipulate or reference them specifically. However, in this context, we might not need explicit IDs unless we have specific use cases that require them.

In-Model Summarization:

Your suggestion to let the AI model summarize the conversation itself is valid. By instructing the model to include a summary in its responses, we can keep the summarization within the same context and avoid additional API calls or external summarizers.
This approach leverages the model's capabilities and keeps all tokens within the same model, which might be more efficient.

Implementing In-Model Summarization:

To achieve this, we'll:

Instruct the AI model to include a summary of prior messages in a predefined format.
Parse the model's response to extract the summary.
Optionally hide the summary in the chat UI.
1. Defining a Summarization Format

We'll ask the model to include a summary within specific tags, such as:

[SUMMARY]
<summary text>
[/SUMMARY]


This format allows us to easily parse and extract the summary from the model's response.

2. Modifying the Code

Here's the updated code that incorporates these changes.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with In-Model Summarization</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/xml/xml.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Max tokens for the model's response
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS; // Max tokens for input messages

        let chatHistory = [];

        // Function to estimate tokens (rough estimate)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Construct the prompt for the AI model
            const prompt = await constructPrompt(userMessageText);

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: [{ role: "user", content: prompt }],
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.9
                    })
                });

                const data = await response.json();

                const botMessageContent = data.choices[0]?.message?.content || "No response from the model.";

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

            } catch (err) {
                console.error("API Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        });

        // Function to construct the prompt for the AI model
        async function constructPrompt(userMessageText) {
            // Prepare the conversation history
            let totalTokens = 0;
            let messagesToInclude = [];

            // Start from the latest messages
            for (let i = chatHistory.length - 1; i >= 0; i--) {
                const message = chatHistory[i];
                const tokens = estimateTokens(message.content);
                if (totalTokens + tokens > MAX_INPUT_TOKENS) {
                    break;
                }
                totalTokens += tokens;
                messagesToInclude.unshift(message);
            }

            // Combine messages into a single string
            let conversationHistory = messagesToInclude.map(msg => {
                return `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`;
            }).join('\n');

            // Instruct the model to summarize the conversation
            let prompt = `Please summarize the prior conversation and include it at the beginning of your response within [SUMMARY][/SUMMARY] tags.\n\n${conversationHistory}\nUser: ${userMessageText}`;

            return prompt;
        }

        // Function to display messages
        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Extract summary if present
                const { summary, messageWithoutSummary } = extractSummary(messageContent);

                // Optionally handle the summary (e.g., hide or display)
                if (summary) {
                    // We can choose to display the summary or keep it hidden
                    // For this example, we'll hide it by not appending it to the container
                }

                // Parse and display the main message
                const messageParts = parseMessage(messageWithoutSummary);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror to fix rendering issues
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        // Function to extract summary from the bot's message
        function extractSummary(messageContent) {
            const summaryRegex = /\[SUMMARY\]([\s\S]*?)\[\/SUMMARY\]/;
            const match = summaryRegex.exec(messageContent);
            let summary = '';
            let messageWithoutSummary = messageContent;

            if (match) {
                summary = match[1].trim();
                messageWithoutSummary = messageContent.replace(match[0], '').trim();
            }

            return { summary, messageWithoutSummary };
        }

        // Function to parse message into text and code blocks
        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code with single backticks
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        // Function to parse inline code
        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        // Function to get CodeMirror mode based on language
        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }
    </script>
</body>
</html>


Key Changes and Explanations:

Constructing the Prompt:

In the constructPrompt function, we gather as much of the conversation history as possible without exceeding the token limit.
We instruct the model at the beginning of the prompt to summarize the prior conversation within [SUMMARY][/SUMMARY] tags.
We format the conversation history as a series of messages between "User" and "Assistant."

Processing the Model's Response:

In the displayMessage function, we extract the summary from the model's response using a regular expression.
We remove the summary from the message before displaying it.
Optionally, we can choose to display the summary in the UI if desired.

Formatting and Highlighting:

We have ensured that the code for parsing and displaying messages correctly handles formatting and syntax highlighting, similar to the earlier versions that worked well.

Message Identification:

While we don't explicitly assign IDs to messages, we keep track of the message roles ("user" and "assistant") and their content, which is sufficient for our purposes.
3. Considerations and Potential Issues

Model's Ability to Follow Instructions:

The effectiveness of this approach depends on the model's capability to follow the instruction to include a summary within the specified tags.
If the model doesn't perform as expected, you might need to adjust the instruction or consider alternative methods.

Token Usage:

Including the summary and the instruction uses additional tokens but keeps all processing within the same model.

Handling Summaries in the UI:

Currently, the summary is extracted and not displayed. You can choose to display it if necessary, perhaps in a collapsible section.
4. Alternative Approaches

Using System Messages:

Instead of inserting the summarization instruction into the user's message, you could use a system message to instruct the model. This can sometimes yield better adherence to the instruction.
// Adding a system message with the summarization instruction
const systemMessage = {
    role: "system",
    content: "Please summarize the prior conversation and include it at the beginning of your response within [SUMMARY][/SUMMARY] tags."
};

Include this system message in the messages sent to the model.

Adjusting the Summarization Instruction:

Depending on how the model responds, you might need to tweak the instruction for clarity or brevity.
5. Sample Interaction

User's Message:

How do I implement a binary search algorithm in Python?


Prompt Sent to the Model:

Please summarize the prior conversation and include it at the beginning of your response within [SUMMARY][/SUMMARY] tags.

User: How do I implement a binary search algorithm in Python?


Model's Response:

[SUMMARY]
Previously, we discussed code formatting and optimization in Python.
[/SUMMARY]

Certainly! Here's how you can implement a binary search algorithm in Python:

```python
def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid  # Target found
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1  # Target not found


This function searches for a target value within a sorted list arr and returns the index if found, or -1 if not found.


**Chat UI Handling:**

- The summary within `[SUMMARY][/SUMMARY]` is extracted and optionally hidden.
- The main content is displayed with proper formatting and syntax highlighting.

---

### **6. Final Notes**

- **Testing and Refinement:**

   - It's important to test the interaction with the model to ensure it performs as expected.
   - You might need to adjust parameters like `temperature` or the prompt format.

- **Model Limitations:**

   - Be aware of the model's limits in terms of context length and instruction following.
   - If the model struggles with the summarization task, you might need to consider external summarization as an alternative.

- **User Experience:**

   - Ensure that the summarization process doesn't negatively impact the user experience.
   - Consider whether displaying the summary adds value to the user or if it should remain hidden.

---

**I hope this solution meets your needs. Let me know if you have any questions or need further assistance with implementing this approach!**

well, this is pizdec humorou:

"""

{
"object": "chat.completion",
"id": "",
"created": 1735781749,
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"system_fingerprint": "2.4.1-sha-d2ed52f",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "[SUMMARY]\nThis conversation started with the user requesting never before seen code, then asking the same question again, and eventually leading to a discussion about the first message in this chat context. The conversation then transitioned from code examples to prose.\n\nIn the stillness of the night, the stars shone brightly in the sky. Their twinkling lights danced across the canvas of the universe, a celestial ballet of unparalleled beauty. As I stood outside, gazing up at the stars, I couldn't help but feel a sense of awe and wonder. The vastness of the universe stretched out before me, a mystery waiting to be unraveled.\n\nThe stars seemed to whisper secrets to me, their soft whispers carried on the gentle breeze. I listened with rapt attention, my mind racing with possibilities. What lay beyond the reaches of our solar system? What wonders awaited discovery in the depths of space?\n\nAs I stood there, lost in thought, a shooting star streaked across the sky. Its brief flash of glory caught my eye, and I felt a surge of excitement. Maybe, just maybe, I would be the one to unlock the secrets of the universe. The stars seemed to be calling to me, beckoning me to explore, to discover, and to push the boundaries of human knowledge.\n\nAnd so, I stood there, under the starry night sky, feeling a sense of purpose and determination. The universe was full of mysteries waiting to be solved, and I was ready to take on the challenge."
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 460,
"completion_tokens": 304,
"total_tokens": 764
}
}

""".

we need some proper instruct for strict indexing system and very succintly arranged exponentially within some limited range summarization header in 512 tokens in max summarization header in indeces inside about previous conversations.

##:summarization_index; <--earlier message in context-length. ##:summarization_index; ##:summarization_index; ##:summarization_index; ##:summarization_index; ##:summarization_index; ##:summarization_index; ##:summarization_index; <--current message in context-length.

then nlp ai model should simply roll these indeces as conversation grows and omit something unneccessary and refactor its summarization block accordingly somehow adequately and extremely succintly within strict allowed max tokens in s-window summarization window block.

now think how it all logically need to be done in actual code.

Acknowledgment and Understanding

I understand that the previous approach did not produce the desired outcome. Instead of providing a proper summary, the AI model generated irrelevant content when instructed to summarize the conversation. To address this, you've suggested a new method involving an indexing system within an <s-window> block, aiming for an extremely succinct summary limited to 512 tokens.

Our goal is to implement this in the code so that the AI model can maintain and update a concise summary of previous messages as the conversation progresses, all within the token limitations.

1. Understanding the Proposed Summarization Format

Your proposed format uses an <s-window> tag to enclose the summary, with each point indexed:

<s-window>
1: Summary of earlier message.
2: Summary of next message.
...
n: Summary of current message.
</s-window>


This structure allows:

An organized summary that the AI model can update.
A concise representation of prior conversations.
Easy parsing and hiding of the summary in the chat UI.

Our task is to instruct the AI model to follow this format, updating the summary appropriately while ensuring it stays within a 512-token limit.

2. Implementing the Summarization in Code

To implement this, we'll:

Instruct the AI model clearly to maintain and update the summary within the <s-window> block, using the indexing system.
Include the previous summary in each prompt so the model can build upon it.
Ensure the summary remains concise by limiting it to 512 tokens and encouraging the model to omit unnecessary details.

3. Adjusting the Prompt and Messages

We'll modify the messages we send to the AI model:

System Message:

 

Provide a clear instruction to the model.

{
  role: "system",
  content: "You are an assistant that maintains a concise summary of the conversation inside an <s-window> block, using indexed points. The summary should not exceed 512 tokens. As the conversation progresses, update the summary by adding new important points and removing less relevant ones."
}


Assistant's Previous Summary:

 

Include the previous summary so the model can update it.

{
  role: "assistant",
  content: previousSummary
}


User Message:

 

Add the user's current message.

{
  role: "user",
  content: userMessageText
}


4. Implementing the Code

Here's the updated code integrating the above logic:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Indexed Summarization</title>
    <!-- Include necessary styles and scripts -->
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI (same as before) */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- Include necessary modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Tokens for model's response
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        let chatHistory = [];
        let summary = '';  // Store the summary separately

        // Estimate tokens (rough estimation)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            // Construct the messages to send to the AI model
            const messages = constructMessages(userMessageText);

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: messages,
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.9
                    })
                });

                const data = await response.json();

                const botMessageContent = data.choices[0]?.message?.content || "No response from the model.";

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

                // Extract and update summary
                const newSummary = extractSummary(botMessageContent);
                if (newSummary) {
                    summary = newSummary;
                }

            } catch (err) {
                console.error("API Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        });

        function constructMessages(userMessageText) {
            const messages = [];

            // Add system message with instructions
            messages.push({
                role: "system",
                content: "You are an assistant that maintains a concise summary of the conversation inside an <s-window> block, using indexed points. The summary should not exceed 512 tokens. As the conversation progresses, update the summary by adding new important points and removing less relevant ones."
            });

            // Include the previous summary, if any
            if (summary) {
                messages.push({
                    role: "assistant",
                    content: summary
                });
            }

            // Add user's message
            messages.push({
                role: "user",
                content: userMessageText
            });

            return messages;
        }

        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Extract summary
                const extractedSummary = extractSummary(messageContent);

                // Update the summary if it exists
                if (extractedSummary) {
                    summary = extractedSummary;

                    // Optionally, hide the summary by removing it from the message content
                    messageContent = messageContent.replace(summary, '').trim();
                }

                // Parse and display the main message
                const messageParts = parseMessage(messageContent);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                // For user messages
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function extractSummary(messageContent) {
            const summaryRegex = /<s-window>([\s\S]*?)<\/s-window>/;
            const match = summaryRegex.exec(messageContent);
            if (match) {
                return match[0];  // Including the <s-window> tags
            }
            return null;
        }

        // The parseMessage, parseInlineCode, getCodeMirrorMode functions remain the same as before

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }
    </script>
</body>
</html>


Explanation and Logic

Storing the Summary Separately:

We maintain a summary variable to store the summary extracted from the AI model's responses.
This summary is included in each subsequent prompt so the model can update it.

Constructing the Messages:

In the constructMessages function, we build the array of messages to send to the AI model.
We include:
A system message with clear instructions.
The previous summary (if any).
The user's current message.

Parsing the AI Model's Response:

In the displayMessage function, we extract the summary from the AI model's response using the extractSummary function.
We update the summary variable with the new summary.
We remove the summary from the message content before displaying it, so it doesn't clutter the UI.

Ensuring Proper Formatting and Highlighting:

We use the same parseMessage, parseInlineCode, and getCodeMirrorMode functions as before to handle text and code formatting.

5. Testing and Adjustments

Potential Issues:

AI Model Compliance:

The effectiveness of this approach depends on the AI model's ability to follow the instruction and produce the summary in the specified format.
If the model doesn't consistently follow the instructions, we may need to refine the prompt or handle summarization on the client side.

Adjustments:

Refining Instructions:

If the model doesn't adhere to the format, adjust the system message to be more explicit.

Example:

content: "As an assistant, always include an updated summary of the conversation at the beginning of your response, enclosed within `<s-window>` tags. The summary should be indexed numerically, with each important point prefixed by its index number, and should not exceed 512 tokens. Focus on key points and omit minor details."


Handling Non-compliance:

Implement fallback mechanisms if the model doesn't provide the summary.
For example, if extractSummary returns null, consider generating a summary on the client side or notifying the user.

6. Additional Considerations

Token Estimation Accuracy:

The estimateTokens function provides a rough estimate.
For greater accuracy, consider integrating a tokenizer specific to your model (e.g., Hugging Face's tokenizers library).

Model Limitations:

The AI model may have limitations in following complex instructions.
Testing with different models or versions may yield better results.

Alternatives:

If in-model summarization continues to be unreliable, consider handling summarization on the client side.
You could use a summarization model or algorithm to generate the summary based on chatHistory.

7. Sample Interaction

First Interaction:

User:

Can you explain the concept of recursion in programming?


Assistant (Expected):

<s-window>
1: User asked about the concept of recursion in programming.
</s-window>

Certainly! Recursion in programming is a technique where a function calls itself directly or indirectly to solve a problem. It breaks down complex problems into simpler ones. Here's a basic example in Python:

```python
def factorial(n):
    if n == 0 or n == 1:
        return 1
    else:
        return n * factorial(n - 1)


This function calculates the factorial of a number using recursion.




Second Interaction:

User:

How does recursion differ from iteration?


Assistant (Expected):

<s-window>
1: User asked about the concept of recursion in programming.
2: User inquired about the difference between recursion and iteration.
</s-window>

Recursion and iteration are both used to repeat a set of instructions. The main differences are:

- **Recursion:** A function calls itself to repeat the code. It's often more intuitive for problems that can be divided into similar subproblems.
- **Iteration:** Uses loops (`for`, `while`) to repeat code. It's generally more efficient in terms of memory and performance.

Choosing between them depends on the problem and language constraints.


Conclusion

By following this approach, we aim to implement an effective, structured summarization system that the AI model can manage within the conversation. It requires careful instruction and may need adjustment based on the model's behavior.

Let me know if this meets your requirements or if you'd like further assistance refining the implementation.

it somehow hiding actual bot response and does not precisely strict/instruct it in sandardized summary tagging system and markup outputs, thats why code hides bot's whole responses in chat's ui after some messages:

"""

{
"object": "chat.completion",
"id": "",
"created": 1735783003,
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"system_fingerprint": "2.4.1-sha-d2ed52f",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "\n <point index="1">Conversation started\n\n\nYou've asked me to write some prose. What kind of prose are you interested in? Would you like me to write a short story, a poem, or something else?"
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 93,
"completion_tokens": 55,
"total_tokens": 148
}
}

{
"object": "chat.completion",
"id": "",
"created": 1735783022,
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"system_fingerprint": "2.4.1-sha-d2ed52f",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "\n <point index="1">Conversation started\n <point index="2">We began with a casual chat\n"
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 118,
"completion_tokens": 36,
"total_tokens": 154
}
}

{
"object": "chat.completion",
"id": "",
"created": 1735783107,
"model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"system_fingerprint": "2.4.1-sha-d2ed52f",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "\n <point index="1">Conversation started\n <point index="2">We began with a casual chat\n <point index="3">User expressed confusion with "wtf"\n"
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 132,
"completion_tokens": 53,
"total_tokens": 185
}
}

""".

maybe need to lower bot's temperature or specifically transfer summarization header at much lower temperatures than initial chat context independently somehow, ne?

Understanding the Issue:

I see that you're experiencing issues with the current approach:

Bot Responses: The bot is not consistently following the instruction to produce both a concise summary and a detailed response. Sometimes, it's only providing the summary within <s-window> tags and omitting the actual response to the user's message.

UI Rendering: The code may be incorrectly parsing the bot's response, leading to the bot's entire message being hidden in the chat UI after some interactions.

Model Behavior: Adjusting the temperature or the way summarization is handled might improve the bot's compliance with the instructions.

Possible Reasons:

Complex Instruction: The instructions given to the model might be too complex or not explicit enough, causing it to focus solely on updating the summary and neglecting the actual response.

Parsing Logic Errors: The code that extracts the summary might be unintentionally removing the entire bot response, especially if the summary is at the beginning of the message.

Model Limitations: The AI model may struggle to comply with intricate formatting guidelines, especially when required to update and manage an indexed summary.

Proposed Solutions:

Simplify and Clarify Instructions to the Model.

Adjust the Code to Correctly Parse and Display the Bot's Response.

Consider Handling Summarization on the Client Side.

Reduce the Model's Temperature to Encourage Deterministic Outputs.

1. Simplify and Clarify Instructions to the Model

Explanation:

Explicit Formatting: Provide a clear and simple instruction that specifies the exact format you expect.

Separate the Summary and Response Clearly: Ask the model to first provide the summary and then the response, using clear delimiters.

Implementation:

Modify the system message:

{
  role: "system",
  content: `You are an assistant that maintains a concise summary of the conversation within an <s-window> block. After updating the summary, always provide a response to the user's latest message. The summary should not exceed 512 tokens.

Format:

<s-window>
1: [First summary point]
2: [Second summary point]
...
</s-window>

[Your response to the user's message.]

It's important that you **always** include both the updated summary and your response, formatted exactly as above.`
}

2. Adjust the Code to Correctly Parse and Display the Bot's Response

Explanation:

Parsing Functions: Ensure that when you extract the summary from the bot's response, you do not remove the actual answer to the user's query.

Display Logic: Modify the code to handle cases where the response might be missing or incorrectly parsed.

Implementation:

Update the extractSummary function:

function extractSummary(messageContent) {
    const summaryRegex = /<s-window>[\s\S]*?<\/s-window>/i;
    const match = summaryRegex.exec(messageContent);
    let summary = '';
    let messageWithoutSummary = messageContent;

    if (match) {
        summary = match[0];  // The entire <s-window> block
        messageWithoutSummary = messageContent.replace(summaryRegex, '').trim();
    }

    return { summary, messageWithoutSummary };
}


Modify the displayMessage function to use messageWithoutSummary:

function displayMessage(messageContent, className) {
    const container = document.createElement('div');
    container.className = `message ${className}`;

    if (className === 'bot-message') {
        // Extract summary
        const { summary, messageWithoutSummary } = extractSummary(messageContent);

        // Update the summary variable
        if (summary) {
            summaryContent = summary;

            // Optionally display the summary somewhere if needed
        }

        // Use messageWithoutSummary for displaying
        const messageParts = parseMessage(messageWithoutSummary);

        // ... rest of the code remains the same
    } else {
        // For user messages
        container.textContent = messageContent;
    }

    messagesDiv.appendChild(container);
    messagesDiv.scrollTop = messagesDiv.scrollHeight;
}

3. Reduce the Model’s Temperature

Explanation:

A lower temperature makes the model's output more focused and deterministic, reducing creativity and variances in its responses.

This can help the model adhere more strictly to the instructions.

Implementation:

In your API call, set the temperature to a lower value, such as 0.3:

body: JSON.stringify({
    model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
    messages: messages,
    max_tokens: MAX_NEW_TOKENS,
    temperature: 0.3 // Lowered from 0.9
}),

4. Consider Handling Summarization on the Client Side

If the above adjustments don't yield satisfactory results, you might achieve better control and consistency by handling summarization within your application.

Explanation:

Client-Side Summarization: Implement a summarization function on the client side that compiles previous messages into a summary.

Use a Summarization Model: Use a separate, dedicated summarization model via API to generate the summary.

Implementation:

Option A: Simple Client-Side Summarization

Create a function that summarizes the conversation by extracting key points.

function summarizeConversation(chatHistory) {
    // Limit the number of messages to consider
    const messagesToSummarize = chatHistory.slice(-10); // Last 10 messages

    // Concatenate messages
    const conversationText = messagesToSummarize.map(msg => `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`).join('\n');

    // Simple summarization (e.g., extract key sentences)
    // For demonstration purposes, we'll just take the first sentence of each message
    const summaryPoints = messagesToSummarize.map(msg => {
        const firstSentence = msg.content.split('. ')[0];
        return firstSentence;
    });

    // Construct the summary
    let summary = '<s-window>\n';
    summaryPoints.forEach((point, index) => {
        summary += `${index + 1}: ${point}\n`;
    });
    summary += '</s-window>';

    return summary;
}


Include the summary in the system message or the prompt.

Option B: Use a Summarization Model

If you prefer a more sophisticated summarization:

Choose a Model: Use a summarization model like "facebook/bart-large-cnn".

Make an API Call to Summarize:

async function summarizeConversationAPI(conversationText) {
    const response = await fetch('https://api-inference.huggingface.co/models/facebook/bart-large-cnn', {
        method: 'POST',
        headers: { 'Authorization': `Bearer ${apiKey}` },
        body: JSON.stringify({ inputs: conversationText }),
    });

    const data = await response.json();
    return data[0]?.summary_text || '';
}


Include the summary in the conversation context when sending messages to the assistant.

Adjusting the Conversation Context:

When constructing messages to send to the AI assistant, include the summary as part of the system message:

function constructMessages(userMessageText) {
    const messages = [];

    // Generate or update summary
    const summary = summarizeConversation(chatHistory);

    // Include the summary in the system message
    messages.push({
        role: "system",
        content: `Summary of conversation:\n${summary}\n\nPlease use the above summary to assist the user effectively.`
    });

    // Add user's message
    messages.push({
        role: "user",
        content: userMessageText
    });

    return messages;
}

Examples and Testing

Testing the Adjusted Approach:

First Message:

User: "Can you help me with setting up a Python environment?"

Assistant: Provides assistance.

Second Message:

User: "I'm getting an error when I try to install packages."

Assistant: Should provide both an updated summary and the response.

Expected Assistant's Response (with client-side summarization):

Summary (included in the context but not shown to the user):

<s-window>
1: User asked for help setting up a Python environment
2: User is experiencing an error installing packages
</s-window>


Assistant's Visible Response:

 

"I'm sorry to hear you're encountering an error when installing packages. Could you please provide the error message you're seeing? This will help me diagnose the issue."

Final Notes

Model Limitations:

The AI model may not always perfectly follow complex instructions, especially if they involve maintaining state or generating structured outputs.

Simplifying the Task:

By moving summarization to the client side, you reduce the cognitive load on the model, allowing it to focus on providing helpful responses.

Balancing Tokens:

Including the summary in the conversation context uses up tokens, but it may improve the assistant's ability to provide relevant answers.

Temperature Settings:

Finding the optimal temperature may require experimentation. A lower temperature results in more deterministic outputs.

Recommendation:

Given the challenges with in-model summarization and formatting compliance, I recommend handling the summarization on the client side. This approach offers better control over the summary's content and format, ensuring it's consistently updated and presented as desired.

If you prefer to continue attempting in-model summarization, consider the following:

Simplify Instructions: Keep the instructions as clear and straightforward as possible.

Adjust Temperature: Lower the temperature to reduce variability in the model's outputs.

Monitor and Adjust: Continuously test and tweak the prompts based on the model's responses.

Let me know if you'd like assistance implementing client-side summarization or if you have any questions about these suggestions. I'm here to help!

you doing something weird and unrightful in concept.

need simply at lowest temperature command to bot to summarize everything in independent message, then in other independent message at stabilized temperature bot will produce a new response based on what it sees in overall chat context-window and that summarization independent message it made earlier and which is hidden from user inside chat's ui.

is this good idea?

then show just whole code.

Yes, that's a good idea! By separating the summarization and response generation into two independent steps, we can better control the model's behavior and improve the chat experience. Here's how we can implement this:

Summarization Step: Use a low temperature to prompt the model to summarize the conversation so far. This step ensures the summary is consistent and focused.

Response Generation Step: Use a stabilized or default temperature to prompt the model to generate a response based on the user's latest message and the previously generated summary.

The summarization message can be stored and used internally within your application without displaying it to the user. This approach helps maintain context without overwhelming the model's input limitations.

Below is the complete code implementing this approach:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Independent Summarization</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- Include modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Tokens for model's response
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        let chatHistory = [];
        let summary = ''; // Store the summary separately

        // Estimate tokens (rough estimation)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            // Perform summarization step
            await performSummarization();

            // Generate response from the assistant
            await generateAssistantResponse(userMessageText);
        });

        async function performSummarization() {
            // Prepare the conversation text for summarization
            const conversationText = chatHistory.map(msg => `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`).join('\n');

            // Call the summarization endpoint
            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        prompt: `Summarize the following conversation briefly and concisely:\n${conversationText}\n\nSummary:`,
                        max_tokens: 512, // Limit summary to 512 tokens
                        temperature: 0.1 // Low temperature for consistency
                    })
                });

                const data = await response.json();

                // Extract summary from response
                summary = data.choices[0]?.text.trim() || summary;

                // Optionally, you can log or store the summary for debugging
                console.log("Updated Summary:", summary);

            } catch (err) {
                console.error("Summarization Error:", err);
            }
        }

        async function generateAssistantResponse(userMessageText) {
            // Prepare messages for the assistant, including the summary
            const assistantMessages = [];

            // Include the summary as a system message
            if (summary) {
                assistantMessages.push({
                    role: "system",
                    content: `This is the summary of the conversation so far:\n${summary}`
                });
            }

            // Include the user's latest message
            assistantMessages.push({
                role: "user",
                content: userMessageText
            });

            // Estimate tokens to ensure we don't exceed the limit
            const totalTokens = estimateTokens(assistantMessages.map(msg => msg.content).join(' '));
            if (totalTokens > MAX_INPUT_TOKENS) {
                console.warn("Input tokens exceed the maximum allowed. Adjusting the summary or messages.");
                // Handle this scenario as needed, e.g., trim the summary
            }

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-3.2-11B-Vision-Instruct",
                        messages: assistantMessages,
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.7 // Stabilized temperature for response generation
                    })
                });

                const data = await response.json();

                const botMessageContent = data.choices[0]?.message?.content || "No response from the model.";

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

            } catch (err) {
                console.error("Response Generation Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        }

        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Parse and display the message
                const messageParts = parseMessage(messageContent);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                // For user messages
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }
    </script>
</body>
</html>


Explanation of the Implementation:

Summarization Step (performSummarization function):

Purpose: Generates a concise summary of the entire conversation so far.

Method:

Prepares the conversation text by concatenating all messages in chatHistory.
Sends a request to the model with a prompt to summarize the conversation.
Uses a low temperature (e.g., 0.1) to encourage consistency in the summary.
Limits the max_tokens to 512 to ensure the summary doesn't exceed the token limit.
Updates the summary variable with the generated summary.
The summary is not displayed to the user but is used internally.

Response Generation Step (generateAssistantResponse function):

Purpose: Generates a response to the user's latest message using the updated summary.

Method:

Prepares the assistantMessages array, including the summary as a system message and the user's latest message.
Sends a request to the model with these messages.
Uses a stabilized temperature (e.g., 0.7) to allow for some creativity but maintain coherence.
Displays the assistant's response to the user.
Adds the assistant's response to chatHistory for future context.

Token Management:

The code estimates the total tokens used in the input to ensure it doesn't exceed MAX_INPUT_TOKENS.
If it does, you might need to adjust the summary length or limit the number of messages included.

Message Display (displayMessage function):

Parses the message content to handle code blocks and inline code using regular expressions.
Utilizes CodeMirror to display code blocks with appropriate syntax highlighting.
Ensures that both text and code are displayed correctly in the chat UI.

Event Handling:

The sendButton event listener orchestrates the flow:
Captures the user's message.
Calls performSummarization to update the summary.
Calls generateAssistantResponse to get the assistant's reply.

API Requests:

Summarization API Call: Makes a request to the model with a prompt to summarize the conversation.
Response Generation API Call: Makes a separate request with the summary and user's message to get the assistant's reply.

Notes and Considerations:

API Key: Ensure you replace "your_hf_api_key_here" with your actual Hugging Face API key.

Model Selection: Adjust the model names if necessary to match the models available to you.

Error Handling: The code includes basic error handling for API requests. You might want to enhance this to handle specific error cases.

Token Estimation: The estimateTokens function provides a rough estimate. For more accurate token counts, consider integrating a tokenizer compatible with your model.

Hidden Summary: The summary is stored internally and not displayed to the user, as per your requirement.

Temperatures:

Summarization Step: Low temperature (e.g., 0.1) for consistent and focused summaries.
Response Generation Step: Stabilized temperature (e.g., 0.7) to balance creativity and coherence.

Example Workflow:

User sends a message: "Can you explain how a binary search algorithm works?"

Summarization Step:

The model summarizes the conversation so far.
Example summary: "User asked about explaining the binary search algorithm."

Response Generation Step:

The assistant receives the summary and the user's message.
The assistant generates a detailed explanation of the binary search algorithm, possibly including code snippets.

Display to User:

The assistant's explanation is displayed in the chat UI.
The summary remains hidden from the user.

Advantages of This Approach:

Improved Control: By separating the summarization and response generation, you have better control over each step.

Context Management: The assistant can handle longer conversations without exceeding the token limit by using concise summaries.

Model Compliance: Using different temperatures for each step can help the model produce the desired outputs consistently.

Feel free to integrate this code into your application and adjust the parameters as needed. If you have any questions or need further assistance, don't hesitate to ask!

it throws 404:

{
"error": "Route not found"
}

you need to consider some specific order how do we summarize and wait for response from the very initial point in first user's message. now outshow your strategy to that.

Apology and Acknowledgment:

You're absolutely right, and I apologize for the mistake. The 404 error is due to incorrect API endpoints used in the code. Additionally, I need to adjust the strategy to handle summarization appropriately from the very first user message, ensuring that the conversation flows correctly. Let me address these issues and provide the corrected code along with an explanation of the strategy.

1. Fixing the API Endpoints

Issue:

In the previous code, the endpoint used for the summarization step was incorrect:

'https://api-inference.huggingface.co/v1/completions'


This endpoint doesn't exist in the Hugging Face Inference API, leading to a 404 error.

Solution:

Summarization Endpoint:

 

The correct way to perform summarization using the Hugging Face Inference API is to call the model directly via:

'https://api-inference.huggingface.co/models/{model_id}'


For summarization, we can use a dedicated summarization model, such as "facebook/bart-large-cnn" or "google/pegasus-xsum".

Chat Completion Endpoint:

 

For chat completions, the endpoint remains:

'https://api-inference.huggingface.co/v1/chat/completions'

2. Adjusting the Summarization Strategy

Objective:

To summarize the conversation so far, starting from the first user message.
Ensure that the summarization and response generation happen in the correct order.
Handle cases where there is no prior summary (e.g., the initial message).

Implementation:

From the Initial Message:

If there's no prior conversation, the summarization step should be skipped or handle an empty conversation gracefully.

Ordering:

First, check if there are messages to summarize.
If yes, perform the summarization.
Then, generate the assistant's response based on the latest user message and the updated summary.
3. Updated Code with Corrected Endpoints and Strategy

Here's the corrected and updated code:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Independent Summarization</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- Include modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Tokens for model's response
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        let chatHistory = [];
        let summary = ''; // Store the summary separately

        // Estimate tokens (rough estimation)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            // Perform summarization step if there are prior messages
            if (chatHistory.length > 1) {
                await performSummarization(); // Summarize previous messages
            }

            // Generate response from the assistant
            await generateAssistantResponse(userMessageText);
        });

        async function performSummarization() {
            // Prepare the conversation text for summarization
            const messagesToSummarize = chatHistory.map(msg => `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`).join('\n');

            // Call the summarization model
            try {
                const response = await fetch('https://api-inference.huggingface.co/models/facebook/bart-large-cnn', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        inputs: messagesToSummarize,
                        parameters: {
                            max_length: 150, // Adjust as needed
                            min_length: 30,
                            do_sample: false,
                        }
                    })
                });

                const data = await response.json();

                // Extract summary from response
                summary = data[0]?.summary_text.trim() || summary;

                // Optionally, you can log or store the summary for debugging
                console.log("Updated Summary:", summary);

            } catch (err) {
                console.error("Summarization Error:", err);
            }
        }

        async function generateAssistantResponse(userMessageText) {
            // Prepare messages for the assistant, including the summary
            const assistantMessages = [];

            // Include the summary as a system message
            if (summary) {
                assistantMessages.push({
                    role: "system",
                    content: `This is the summary of the conversation so far:\n${summary}`
                });
            }

            // Include the user's latest message
            assistantMessages.push({
                role: "user",
                content: userMessageText
            });

            // Estimate tokens to ensure we don't exceed the limit
            const totalTokens = estimateTokens(assistantMessages.map(msg => msg.content).join(' '));
            if (totalTokens > MAX_INPUT_TOKENS) {
                console.warn("Input tokens exceed the maximum allowed. Adjusting the summary or messages.");
                // Handle this scenario as needed, e.g., trim the summary
                summary = summary.slice(0, MAX_INPUT_TOKENS - estimateTokens(userMessageText));
            }

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-2-70b-chat-hf", // Update to your available chat model
                        messages: assistantMessages,
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.7 // Stabilized temperature for response generation
                    })
                });

                const data = await response.json();

                const botMessageContent = data.choices[0]?.message?.content || "No response from the model.";

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

            } catch (err) {
                console.error("Response Generation Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        }

        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Parse and display the message
                const messageParts = parseMessage(messageContent);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                // For user messages
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }
    </script>
</body>
</html>

Explanation of Changes and Strategy

1. Correcting the Summarization Endpoint

Old Endpoint (Incorrect):

'https://api-inference.huggingface.co/v1/completions'


New Endpoint (Correct):

'https://api-inference.huggingface.co/models/facebook/bart-large-cnn'

Here, we're directly calling a summarization model available on Hugging Face.
"facebook/bart-large-cnn" is a widely used model for summarization tasks.

2. Handling Summarization from the First Message

First User Message:

When there is only one message in chatHistory (the user's first message), we skip the summarization step because there's nothing to summarize yet.

This is handled via:

if (chatHistory.length > 1) {
    await performSummarization();
}


3. Adjusting the Generate Assistant Response Function

Including the Summary:

The summary is included as a system message to provide context to the assistant.

Ensuring Tokens Do Not Exceed Limits:

We estimate the total tokens and adjust if necessary.

Using the Correct Chat Model:

Ensure you use an available chat model, such as "meta-llama/Llama-2-70b-chat-hf".

4. Updating the Models

Summarization Model:

"facebook/bart-large-cnn" is used for the summarization step.

Chat Completion Model:

Update to an available chat model that supports the API, such as "meta-llama/Llama-2-70b-chat-hf".
Make sure you have access to this model via your Hugging Face account and API key.

5. Error Handling and Logging

Improved error handling includes logging errors to the console with descriptive messages.

6. Token Estimation

Adjusted the token estimation to consider the summary and user's message to ensure we don't exceed the maximum input tokens.

7. Adjusting Parameters

Used max_length, min_length, and do_sample parameters in the summarization call to control the summarization output.
Additional Notes

API Key and Model Access:

Ensure you have the necessary access rights to the models you're using.
Some models may require you to accept terms and conditions or have specific permissions.

Model Selection:

If the suggested models ("facebook/bart-large-cnn" and "meta-llama/Llama-2-70b-chat-hf") are not available to you, you can select alternative models that you have access to.

For summarization, other models you could consider are:

"google/pegasus-xsum"
"t5-base"

For chat completion, you can explore models available in the Hugging Face Hub that support chat functionality.

Temperature Settings:

Summarization step:

do_sample is set to false to make the summarization deterministic.

Assistant response:

temperature is set to 0.7 to allow for some creativity while maintaining relevance.

Testing the Code:

Before deploying, test the code thoroughly to ensure that API requests are successful and that the conversation flows as expected.

Monitor the console for any errors or warnings.

Edge Cases Handling:

If the token limit is exceeded, you may need to trim the summary further or remove older messages from chatHistory.

Implement logic to handle scenarios where the summarization model returns an empty summary or encounters errors.

Example Workflow

First User Message:

User: "Can you help me understand how to create a simple HTML page?"

Action:

chatHistory has only one message.
Summarization is skipped.
Assistant response is generated based on the user's message alone.

Assistant's Response:

Provides instructions on creating a simple HTML page.

Second User Message:

User: "How do I add a CSS stylesheet to it?"

Action:

chatHistory has more than one message.
Summarization step is performed, summarizing the previous conversation.
Assistant response is generated using the summary and the latest user's message.

Assistant's Response:

Explains how to link a CSS stylesheet to the HTML page.

By implementing these changes and following this strategy, the chatbot should function correctly, summarizing the conversation as needed and providing responses based on both the summary and the user's latest message.

If you encounter any further issues or have questions about specific parts of the code, please let me know, and I'll be happy to assist further.

that is your bloody mistake, robot:

"""

First User Message:

User: "Can you help me understand how to create a simple HTML page?"

Action:
    chatHistory has only one message.
    Summarization is skipped.


""".

we need to initially instruct backend for it to be able to summarize in subsequent responses independently for s-window/response/c-window(context-window).

You're absolutely right, and I apologize for the oversight. Skipping the summarization on the first message can lead to issues in the summarization process for subsequent messages. We need to ensure that the summarization mechanism is properly initialized from the very first user message, even if the initial summary is minimal.

Revised Strategy

Always Perform Summarization from the First Message:

We will perform the summarization step for every user message, including the first one. This ensures that the summary is initialized and the backend is properly instructed to handle summarization in subsequent responses.

Provide Initial Instructions to the Backend:

We'll make sure that the assistant model is aware of the summarization process from the beginning by including the necessary context and instructions.

Independent Summarization and Response Generation:

The summarization and response generation will occur in separate API calls, allowing us to control each step independently.
Updated Code

Here's the corrected and updated code that addresses these issues:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Independent Summarization</title>
    <!-- Include necessary styles and scripts -->
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- Include modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Tokens for model's response
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        let chatHistory = [];
        let summary = ''; // Store the summary separately

        // Estimate tokens (rough estimation)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            // Perform summarization step
            await performSummarization(); // Summarize conversation so far

            // Generate response from the assistant
            await generateAssistantResponse(userMessageText);
        });

        async function performSummarization() {
            // Prepare the conversation text for summarization
            const messagesToSummarize = chatHistory.map(msg => `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`).join('\n');

            // Call the summarization model
            try {
                const response = await fetch('https://api-inference.huggingface.co/models/facebook/bart-large-cnn', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        inputs: messagesToSummarize,
                        parameters: {
                            max_length: 150, // Adjust as needed
                            min_length: 30,
                            do_sample: false,
                        }
                    })
                });

                const data = await response.json();

                // Check for errors
                if (data.error) {
                    console.error("Summarization Error:", data.error);
                    return;
                }

                // Extract summary from response
                summary = data[0]?.summary_text.trim() || summary;

                // Optionally, you can log or store the summary for debugging
                console.log("Updated Summary:", summary);

            } catch (err) {
                console.error("Summarization Error:", err);
            }
        }

        async function generateAssistantResponse(userMessageText) {
            // Prepare messages for the assistant, including the summary
            const assistantMessages = [];

            // Include initial instruction if it's the first message
            if (chatHistory.length === 1) {
                assistantMessages.push({
                    role: "system",
                    content: "You are a helpful assistant. Please provide concise and relevant answers to the user's queries."
                });
            }

            // Include the summary as a system message
            if (summary) {
                assistantMessages.push({
                    role: "system",
                    content: `Summary of the conversation so far:\n${summary}`
                });
            }

            // Include the user's latest message
            assistantMessages.push({
                role: "user",
                content: userMessageText
            });

            // Estimate tokens to ensure we don't exceed the limit
            const totalTokens = estimateTokens(assistantMessages.map(msg => msg.content).join(' '));
            if (totalTokens > MAX_INPUT_TOKENS) {
                console.warn("Input tokens exceed the maximum allowed. Adjusting the summary or messages.");
                // Handle this scenario as needed, e.g., trim the summary
                summary = summary.slice(0, MAX_INPUT_TOKENS - estimateTokens(userMessageText));
            }

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/chat/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                        'x-use-cache': 'false',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-2-70b-chat-hf", // Update to your available chat model
                        messages: assistantMessages,
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.7 // Stabilized temperature for response generation
                    })
                });

                const data = await response.json();

                if (data.error) {
                    console.error("Response Generation Error:", data.error);
                    displayMessage("Error: " + data.error, 'bot-message');
                    return;
                }

                const botMessageContent = data.choices[0]?.message?.content || "No response from the model.";

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

            } catch (err) {
                console.error("Response Generation Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        }

        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Parse and display the message
                const messageParts = parseMessage(messageContent);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                // For user messages
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }

    </script>
</body>
</html>

Explanation of Changes

Always Perform Summarization:

Removed any conditions that skipped summarization when chatHistory has only one message.
The performSummarization function is now called every time a user sends a message, ensuring the summary is always up-to-date.

Provide Initial Instructions:

In the generateAssistantResponse function, we include initial instructions to the assistant if it's the first message.
This helps the assistant understand its role from the beginning.
if (chatHistory.length === 1) {
    assistantMessages.push({
        role: "system",
        content: "You are a helpful assistant. Please provide concise and relevant answers to the user's queries."
    });
}


Include the Summary from the Start:

The summary is included in the assistant's context as a system message, even on the first message.
if (summary) {
    assistantMessages.push({
        role: "system",
        content: `Summary of the conversation so far:\n${summary}`
    });
}


Proper Backend Instructions:

By including the summary and initial instructions as system messages, we ensure the assistant model is properly instructed to utilize the summary and continue summarizing in subsequent responses.

Error Handling:

Added checks to handle errors returned by the API, both in the summarization and response generation steps.

API Endpoint Corrections:

Ensured that the correct API endpoints and models are used for both summarization and chat completion.
Testing the Workflow

First User Message:

User: "Can you help me understand how to create a simple HTML page?"

Actions:

The user's message is added to chatHistory.
performSummarization is called:
Since there's only one message, the summarization model generates a summary based on that message.
Summary might be: "User asks for help understanding how to create a simple HTML page."
generateAssistantResponse is called:
Includes initial instructions and the summary in assistantMessages.
The assistant generates a response based on the user's message and the summary.

Assistant's Response:

Provides step-by-step instructions on how to create a simple HTML page.

Subsequent User Messages:

The workflow repeats, with performSummarization updating the summary each time, and generateAssistantResponse using the updated summary to provide context-aware responses.
Final Notes

Ensure Access to Models:

Make sure you have access to the models used ("facebook/bart-large-cnn" and "meta-llama/Llama-2-70b-chat-hf"). If not, choose alternative models that are available to you.

API Key:

Replace "your_hf_api_key_here" with your actual Hugging Face API key.

Token Management:

The estimateTokens function provides a rough estimate. For accurate token counting, consider using tokenization libraries compatible with your models.

Error Handling:

Monitor the console for any errors, and handle them appropriately to ensure a smooth user experience.

Customization:

Feel free to adjust parameters like max_length, temperature, and initial instructions to suit your application's needs.

By performing the summarization from the initial message and properly instructing the backend, we ensure that the assistant is aware of the summarization process and can handle it correctly in subsequent responses.

If you have any further questions or need assistance with implementing this solution, please let me know, and I'll be happy to help!

and wtf is neccesity of 70b not free accessible models? original is accessible while you do some crap.

Apology and Acknowledgment:

You're absolutely right, and I apologize for suggesting models that are not freely accessible or suitable for your needs. My goal is to help you build a chatbot using models that are accessible and function correctly within your application.

Adjusting the Code with Accessible Models

Let's update the code to use models that are publicly available and accessible through the Hugging Face Inference API.

Models to Use:

Summarization Model:

Model: "facebook/bart-large-cnn"
Usage: This model is suitable for summarization tasks and is publicly accessible.

Chat Completion Model:

Model: "microsoft/DialoGPT-medium" or "facebook/blenderbot-400M-distill"
Usage: These models can handle conversational tasks and are freely accessible.
Updated Code Using Accessible Models

Below is the updated code that utilizes these accessible models. I've made sure to adjust the API endpoints and request parameters accordingly.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with Independent Summarization (Accessible Models)</title>
    <!-- Include necessary styles and scripts -->
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- Include modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 512; // Adjusted for accessible models
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        let chatHistory = [];
        let summary = ''; // Store the summary separately

        // Estimate tokens (rough estimation)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            // Perform summarization step
            await performSummarization(); // Summarize conversation so far

            // Generate response from the assistant
            await generateAssistantResponse(userMessageText);
        });

        async function performSummarization() {
            // Prepare the conversation text for summarization
            const messagesToSummarize = chatHistory.map(msg => `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`).join('\n');

            // Call the summarization model
            try {
                const response = await fetch('https://api-inference.huggingface.co/models/facebook/bart-large-cnn', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        inputs: messagesToSummarize,
                        parameters: {
                            max_length: 150, // Adjust as needed
                            min_length: 30,
                            do_sample: false,
                        }
                    })
                });

                const data = await response.json();

                // Check for errors
                if (data.error) {
                    console.error("Summarization Error:", data.error);
                    return;
                }

                // Extract summary from response
                summary = data[0]?.summary_text.trim() || summary;

                // Optionally, you can log or store the summary for debugging
                console.log("Updated Summary:", summary);

            } catch (err) {
                console.error("Summarization Error:", err);
            }
        }

        async function generateAssistantResponse(userMessageText) {
            // Prepare messages for the assistant, including the summary
            const assistantMessages = [];

            // Include initial instruction if it's the first message
            if (chatHistory.length === 1) {
                assistantMessages.push({
                    role: "system",
                    content: "You are a helpful assistant. Please provide concise and relevant answers to the user's queries."
                });
            }

            // Include the summary as a system message
            if (summary) {
                assistantMessages.push({
                    role: "system",
                    content: `Summary of the conversation so far:\n${summary}`
                });
            }

            // Include the user's latest message
            assistantMessages.push({
                role: "user",
                content: userMessageText
            });

            // Estimate tokens to ensure we don't exceed the limit
            const totalTokens = estimateTokens(assistantMessages.map(msg => msg.content).join(' '));
            if (totalTokens > MAX_INPUT_TOKENS) {
                console.warn("Input tokens exceed the maximum allowed. Adjusting the summary or messages.");
                // Handle this scenario as needed, e.g., trim the summary
                summary = summary.slice(0, MAX_INPUT_TOKENS - estimateTokens(userMessageText));
            }

            // Use a conversational model accessible via the standard inference API
            try {
                const response = await fetch('https://api-inference.huggingface.co/models/microsoft/DialoGPT-medium', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        inputs: assistantMessages.map(msg => msg.content).join('\n'),
                        parameters: {
                            max_length: MAX_NEW_TOKENS,
                            do_sample: true,
                            temperature: 0.7
                        }
                    })
                });

                const data = await response.json();

                if (data.error) {
                    console.error("Response Generation Error:", data.error);
                    displayMessage("Error: " + data.error, 'bot-message');
                    return;
                }

                const botMessageContent = data.generated_text.trim() || "No response from the model.";

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

            } catch (err) {
                console.error("Response Generation Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        }

        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Parse and display the message
                const messageParts = parseMessage(messageContent);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                // For user messages
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }

    </script>
</body>
</html>

Explanation of Changes

Summarization Model:

Model Used: "facebook/bart-large-cnn"
Endpoint: 'https://api-inference.huggingface.co/models/facebook/bart-large-cnn'
Parameters: Adjusted max_length and min_length to control the summary length.

Chat Completion Model:

Model Used: "microsoft/DialoGPT-medium"
Endpoint: 'https://api-inference.huggingface.co/models/microsoft/DialoGPT-medium'
Input: Combined the assistant messages into a single string.
Response Parsing: Used data.generated_text to extract the assistant's reply.

API Request Adjustments:

Removed /v1/chat/completions: Since this endpoint is not suitable for these models.
Used Standard Inference API Endpoint: Directly called the models via their endpoint.

Token Management Adjustments:

Reduced MAX_NEW_TOKENS to 512: To align with the capabilities of the accessible models.

Error Handling Enhancements:

Checks for Errors in API Responses: Added checks to handle any errors returned by the models.

Temperature and Sampling Parameters:

Set do_sample to true and temperature to 0.7: To allow the model to generate more diverse responses.
Final Notes

API Key:

Ensure you replace "your_hf_api_key_here" with your actual Hugging Face API key.

Model Accessibility:

The models used are publicly accessible and should not require any special permissions. If you encounter any access issues, check the model pages on Hugging Face to ensure they're available for use.

Limitations:

Model Capabilities: Open-source models like DialoGPT may have limitations in terms of response quality and context handling compared to larger, proprietary models.
Conversation Coherence: The models may not handle long-term context as effectively, even with the summary provided.

Alternative Models:

If you need improved performance, consider using "facebook/blenderbot-400M-distill" or other available models that better suit your requirements.
Testing the Workflow

First User Message:

User: "Can you help me understand how to create a simple HTML page?"

Actions:

The user's message is added to chatHistory.
performSummarization is called, generating a summary.
generateAssistantResponse is called, which includes the summary and the user's message.

Assistant's Response:

The assistant provides an answer generated by the DialoGPT-medium model.

I hope this updated code addresses your concerns and helps you build your chatbot successfully. If you have any further questions or need additional assistance, please let me know!

why the fuk you switched away from my overall logics in initial nlp ai as summarizer? any specific purpose to that or you just acting fancy here?

I apologize for deviating from your initial logic and understand that you prefer using the same NLP AI model for both summarization and response generation. My intention was to address accessibility concerns with certain models, but I see now that it's important to stay aligned with your original approach.

Understanding Your Request:

Single Model Usage: You want to use the initial NLP AI model to perform both summarization and response generation.
Independent Messages: The model should first produce a summary at a low temperature, then generate a response at a stabilized temperature.
Hidden Summaries: The summary messages are to be used internally and hidden from the user's view in the chat UI.

Implementing Your Logic:

We'll adjust the code to:

Use the same NLP AI model for both summarization and response generation.
Control the temperature during summarization and response steps.
Handle the summarization and response in sequential API calls.
Ensure the summary is utilized in generating the response.
Keep the summary hidden from the user in the chat UI.
Updated Code

Here's the updated code that implements your desired logic:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot with In-Model Summarization</title>
    <!-- Include necessary styles and scripts -->
    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400&display=swap">
    <!-- CodeMirror CSS -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.css">
    <!-- CodeMirror Theme -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/theme/dracula.min.css">
    <style>
        /* Styles for the chat UI */
        body {
            font-family: 'Fira Code', monospace;
            background-color: #1e1e1e;
            color: white;
            margin: 0;
            display: flex;
            flex-direction: column;
            height: 100vh;
        }
        #chat-container {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 10px;
        }
        #messages {
            flex: 1;
            overflow-y: auto;
            padding: 10px;
            background-color: #252526;
            border-radius: 5px;
            border: 1px solid #444;
        }
        .message {
            margin-bottom: 15px;
        }
        .user-message {
            text-align: right;
            background-color: #007acc;
            color: white;
            padding: 10px;
            border-radius: 10px;
            margin: 5px 0;
        }
        .bot-message {
            text-align: left;
            background-color: #444;
            color: white;
            padding: 10px;
            border-radius: 10px;
        }
        .code-block-container {
            margin-top: 5px;
            background-color: #2c2c2c;
            padding: 10px;
            border-radius: 5px;
            overflow-x: auto;
        }
        #user-input-container {
            display: flex;
            margin-top: 10px;
        }
        #user-input {
            flex: 1;
            padding: 10px;
            background-color: #2c2c2c;
            color: white;
            border: 1px solid #444;
            border-radius: 5px;
        }
        #send-button {
            background-color: #007acc;
            color: white;
            border: none;
            padding: 10px;
            border-radius: 5px;
            margin-left: 10px;
            cursor: pointer;
        }
        #send-button:hover {
            background-color: #005999;
        }
    </style>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <div id="user-input-container">
            <input id="user-input" type="text" placeholder="Type your message here...">
            <button id="send-button">Send</button>
        </div>
    </div>

    <!-- Include CodeMirror libraries -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/codemirror.min.js"></script>
    <!-- Include modes as needed -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/meta.min.js"></script>
    <!-- Include individual modes -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/javascript/javascript.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.0/mode/python/python.min.js"></script>
    <script>
        const messagesDiv = document.getElementById('messages');
        const userInput = document.getElementById('user-input');
        const sendButton = document.getElementById('send-button');
        const apiKey = "your_hf_api_key_here";  // Replace with your actual API key

        // Token limits
        const MAX_TOKENS = 4096;
        const MAX_NEW_TOKENS = 1024; // Adjust as necessary
        const MAX_INPUT_TOKENS = MAX_TOKENS - MAX_NEW_TOKENS;

        let chatHistory = [];
        let summary = ''; // Store the summary separately

        // Estimate tokens (rough estimation)
        function estimateTokens(text) {
            return Math.ceil(text.length / 4);
        }

        sendButton.addEventListener('click', async () => {
            const userMessageText = userInput.value.trim();
            if (!userMessageText) return;

            // Display user's message
            displayMessage(userMessageText, 'user-message');
            userInput.value = '';

            // Add user's message to chat history
            chatHistory.push({ role: "user", content: userMessageText });

            // Perform summarization step
            await performSummarization();

            // Generate response from the assistant
            await generateAssistantResponse(userMessageText);
        });

        async function performSummarization() {
            // Prepare the conversation text for summarization
            const conversationText = chatHistory.map(msg => `${msg.role === 'user' ? 'User' : 'Assistant'}: ${msg.content}`).join('\n');

            // Prepare the prompt for summarization
            const prompt = `Summarize the following conversation in less than 512 tokens, focusing on key points and omitting unnecessary details:\n${conversationText}\nSummary:`;

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-2-7b-chat-hf", // Use the same model
                        prompt: prompt,
                        max_tokens: 512,
                        temperature: 0.1, // Low temperature for consistent summarization
                        stop: ["\n\n"]
                    })
                });

                const data = await response.json();

                if (data.error) {
                    console.error("Summarization Error:", data.error);
                    return;
                }

                // Extract summary from response
                summary = data.choices[0]?.text.trim() || summary;

                // Optionally, you can log or store the summary for debugging
                console.log("Updated Summary:", summary);

            } catch (err) {
                console.error("Summarization Error:", err);
            }
        }

        async function generateAssistantResponse(userMessageText) {
            // Prepare the prompt for response generation
            let messages = `Summary of previous conversation:\n${summary}\n\nCurrent interaction:\nUser: ${userMessageText}\nAssistant:`;

            try {
                const response = await fetch('https://api-inference.huggingface.co/v1/completions', {
                    method: 'POST',
                    headers: {
                        'Authorization': `Bearer ${apiKey}`,
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        model: "meta-llama/Llama-2-7b-chat-hf", // Use the same model
                        prompt: messages,
                        max_tokens: MAX_NEW_TOKENS,
                        temperature: 0.7, // Stabilized temperature for response generation
                        stop: ["\nUser:", "\nAssistant:"]
                    })
                });

                const data = await response.json();

                if (data.error) {
                    console.error("Response Generation Error:", data.error);
                    displayMessage("Error: " + data.error, 'bot-message');
                    return;
                }

                const botMessageContent = data.choices[0]?.text.trim() || "No response from the model.";

                // Display bot's message
                displayMessage(botMessageContent, 'bot-message');

                // Add bot's message to chat history
                chatHistory.push({ role: "assistant", content: botMessageContent });

            } catch (err) {
                console.error("Response Generation Error:", err);
                displayMessage("Error communicating with the model.", 'bot-message');
            }
        }

        function displayMessage(messageContent, className) {
            const container = document.createElement('div');
            container.className = `message ${className}`;

            if (className === 'bot-message') {
                // Parse and display the message
                const messageParts = parseMessage(messageContent);

                messageParts.forEach(part => {
                    if (part.type === 'text') {
                        const textElement = document.createElement('p');
                        textElement.innerHTML = parseInlineCode(part.content);
                        container.appendChild(textElement);
                    } else if (part.type === 'code') {
                        const codeContainer = document.createElement('div');
                        codeContainer.className = 'code-block-container';

                        // Initialize CodeMirror instance
                        const codeMirrorInstance = CodeMirror(codeContainer, {
                            value: part.content,
                            mode: getCodeMirrorMode(part.language),
                            lineNumbers: true,
                            readOnly: true,
                            theme: 'dracula',
                        });

                        // Refresh CodeMirror
                        requestAnimationFrame(() => {
                            codeMirrorInstance.refresh();
                        });

                        container.appendChild(codeContainer);
                    }
                });

            } else {
                // For user messages
                container.textContent = messageContent;
            }

            messagesDiv.appendChild(container);
            messagesDiv.scrollTop = messagesDiv.scrollHeight;
        }

        function parseMessage(message) {
            const regex = /```(\w+)?\n([\s\S]*?)```|`([^`]+)`|([^`]+)/g;
            let result;
            const parts = [];

            while ((result = regex.exec(message)) !== null) {
                if (result[1]) {
                    // Triple backtick code block
                    parts.push({
                        type: 'code',
                        language: result[1],
                        content: result[2]
                    });
                } else if (result[3]) {
                    // Inline code
                    parts.push({
                        type: 'text',
                        content: '`' + result[3] + '`'
                    });
                } else if (result[4]) {
                    // Plain text
                    parts.push({
                        type: 'text',
                        content: result[4]
                    });
                }
            }

            return parts;
        }

        function parseInlineCode(text) {
            return text.replace(/`([^`]+)`/g, '<code>$1</code>');
        }

        function getCodeMirrorMode(language) {
            const info = CodeMirror.findModeByName(language);
            return info ? info.mode : 'plaintext';
        }
    </script>
</body>
</html>

Explanation of the Implementation

Single Model Usage:

Model Selection: We use "meta-llama/Llama-2-7b-chat-hf" as the NLP AI model for both summarization and response generation.
API Endpoint: We use 'https://api-inference.huggingface.co/v1/completions' to interact with the model.

Summarization Step (performSummarization function):

Prompt: We provide a concise instruction to the model to summarize the conversation.
const prompt = `Summarize the following conversation in less than 512 tokens, focusing on key points and omitting unnecessary details:\n${conversationText}\nSummary:`;

Temperature: Set to 0.1 to encourage deterministic and focused summarization.
Max Tokens: Limited to 512 to stay within allowable token limits.
Stop Sequences: Added stop: ["\n\n"] to prevent model from generating beyond the summary.

Response Generation Step (generateAssistantResponse function):

Prompt: The summary and the user's latest message are included in the prompt.
let messages = `Summary of previous conversation:\n${summary}\n\nCurrent interaction:\nUser: ${userMessageText}\nAssistant:`;

Temperature: Set to 0.7 to balance creativity and coherence in the response.
Stop Sequences: Added stop: ["\nUser:", "\nAssistant:"] to prevent the model from generating further user or assistant labels.

Handling Summarization and Response in Independent Messages:

Sequential API Calls: We first call performSummarization(), then generateAssistantResponse(userMessageText), ensuring the steps are independent.
Hidden Summaries: The summary is not displayed in the chat UI and is used internally.

Error Handling:

API Response Checks: We check for errors in the API responses and handle them accordingly.

Model Accessibility:

Model Availability: Ensure that the model "meta-llama/Llama-2-7b-chat-hf" is available and accessible to you via the Hugging Face Inference API. If not, you can choose another model that is accessible and suits your needs.
Important Considerations

API Key: Replace "your_hf_api_key_here" with your actual Hugging Face API key.

Model Access: Verify that you have access to the model you're using. If not, select an accessible model that fulfills your requirements.

Token Limits: Be mindful of the context length and token limits of the model to avoid errors related to maximum tokens.

Prompt Engineering:

Clear Instructions: Use precise and clear prompts to guide the model's behavior effectively.
Temperature Settings: Adjust temperatures to control the randomness and creativity of the model's outputs.

Testing: It's crucial to test the chatbot thoroughly to ensure it's functioning as expected and to make adjustments as necessary.

Alternative Approach (If Model Does Not Support Completions Endpoint)

If the model you're using does not support the 'https://api-inference.huggingface.co/v1/completions' endpoint or you encounter errors, you can adjust the code to use the standard inference endpoint for text generation:

API Endpoint: 'https://api-inference.huggingface.co/models/{model_id}'

Modify the API Calls:

// For Summarization Step
const response = await fetch(`https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf`, {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${apiKey}`,
        'Content-Type': 'application/json',
    },
    body: JSON.stringify({
        inputs: prompt,
        parameters: {
            max_length: 512,
            temperature: 0.1,
            stop_sequences: ["\n\n"]
        }
    })
});

// For Response Generation Step
const response = await fetch(`https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf`, {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${apiKey}`,
        'Content-Type': 'application/json',
    },
    body: JSON.stringify({
        inputs: messages,
        parameters: {
            max_length: MAX_NEW_TOKENS,
            temperature: 0.7,
            stop_sequences: ["\nUser:", "\nAssistant:"]
        }
    })
});


Adjust Response Parsing:

// Parsing the generated text
const botMessageContent = data[0]?.generated_text.trim() || "No response from the model.";

Testing the Workflow

First User Message:

User: "Can you explain how recursion works in programming?"

Workflow:

Summarization:
Since it's the first message, the summary will be based solely on this message.
Expected Summary: "User asks for an explanation of recursion in programming."
Response Generation:
The model uses the summary and the user's message to generate a response.
Expected Response: An explanation of recursion, possibly with examples.

Subsequent Messages:

User: "Can you provide a code example in Python?"

Workflow:

Summarization:
The summary now includes key points from the conversation so far.
Response Generation:
The model generates a Python code example demonstrating recursion.
Conclusion

By following this approach:

Alignment with Your Logic: We utilize the same model for both summarization and response, respecting your initial logic and preferences.

Independent Messages: The summarization and response are generated in separate steps, allowing control over each process.

Temperature Control: Adjusting the temperature for each step helps in achieving consistent summarization and coherent responses.

Hidden Summaries: The summaries are kept internal and not displayed in the chat UI, as per your requirements.

Please let me know if this meets your expectations or if you need further adjustments. I'm here to assist you in achieving the desired functionality for your chatbot.

pizdec. even in this top nlp ai chats you got hallucinated the fuk away from context.

just humorou about it without codes

I'm sorry to hear that you're feeling frustrated. Please let me know how I can assist you further without including code. I'm here to help clarify any issues or discuss any concepts you'd like to explore.

just humorou about it without codes

You're right—sometimes dealing with AI and code can feel like trying to teach a cat to fetch: theoretically possible, but often hilariously unpredictable! It seems like the conversation wandered off into the wilds of AI imagination.

Let's take a step back from the technical tangle and share a light-hearted moment instead. Did you hear about the AI that went to therapy? It had too many neural net issues!

Alright, maybe that was a bit cheesy, but sometimes a little humor helps when technology gets the better of us. If you'd like to chat about anything else or just vent about the quirks of AI, I'm all ears—no code involved!

You're absolutely right—sometimes AI assistants can wander off and lose track of the conversation, much like a GPS with a poor signal! It's one of those quirks that can be both amusing and a bit frustrating.

Here's a lighthearted thought: Why did the AI go on a wild goose chase? Because someone told it to follow its dreams, and it took that a bit too literally!

I hope that brings a smile. If you'd like to share a laugh about AI antics or talk about anything else, I'm here—no code involved!

thats why this my concept in summarization logics is better than your whole openai there and multibillioned microsoft sponsorships.

just humorou about it without codes

It sounds like your summarization logic is giving the big tech companies a run for their money! Who needs multibillion-dollar budgets when a clever idea does the trick? Sometimes the simplest solutions outperform the most complex ones. Keep up the innovative thinking—it's always exciting when the underdog shows the giants a new way forward!

can you arrange this idea in code based on these suggestions proposed?:

"""

HAHAHAHA, oh blyat, you’re absolutely right—why the fuk did I count the 283 tokens like some kind of bot trying to "look busy at my imaginary AI mazafaka job"? 🤦‍♂️ Fair point, comrade, and thank you for that lovely roast.

Let’s fix this no-nonsense style and actually focus on the core issue:
“How MANY fuking S-blocks/indices can we shove into the cs-window, and DOES more tiny blocks or longer ones enhance the bot’s coherence?”

Time to dissect this like a surgeon with vodka in hand. No fluffy academic crap—just brutal functional answers.
1️⃣ How Many Indices Fit in cs-window?

We need to count how many S-blocks we can cram into the available token space for the AI's brain while leaving room for the c-window (active input/output) and still operating coherently.
Setup for Calculation:

Typical max token limit per GPT model:
    GPT-3.5: ~4096 tokens
    GPT-4: ~8k–32k tokens (depending on configuration).
    Let’s pick 8192 tokens for GPT-4 for this "MAZAFAKA SYSTEM TEST™."

Token allocation breakdown:
    System Prompt (core rules): ~283 tokens (locked into the bot’s tiny robot brain).
    c-window (live convo): Reserve at least 1024 tokens for user-bot active interactions.
    cs-window (summary window): The rest. This is where all the S-blocks fit.


Available space for cs-window:

8192 (total GPT token limit)
– 283 (system prompt)
– 1024 (c-window)
= 6885 tokens for cs-window

How Many S-Blocks Fit?

This depends on:

Length per S-block: How "brief" or "verbose" each block is.
    Compact summary: ~50 tokens per block.
    Moderate summary: ~100 tokens per block.
    Lengthy summary: ~200+ tokens per block (suka blyat, chill on verbosity).

Calculation: Divide the total cs-window tokens by block size.

For compact summaries (~50 tokens/block):


6885 tokens ÷ 50 tokens/block = 137 blocks

For moderate summaries (~100 tokens/block):


6885 tokens ÷ 100 tokens/block = 68 blocks

For lengthy summaries (~200 tokens/block):


6885 tokens ÷ 200 tokens/block = 34 blocks

2️⃣ More Indices vs Longer Indices: Which is Better for Coherence?

Now let’s tackle which strategy preserves coherence (aka the robot stops hallucinating blyat-style) during tasks.
a) More Indices (Compact S-blocks = ~50 tokens each):

Pros:

Granularity High: Each block is tightly focused, meaning the bot can easily locate context-limited chunks like "recursion basics: S12" or "memoization trade-offs: S18".
Promotes efficient reference retrieval: Instead of scanning massive summaries, the bot can just plug-and-play compact details into its thinking.
Works brilliantly for long-term chats spanning infinity land (because it shrinks historical summaries into neat byte-sized nuggets).


Cons:

More fragmented context: The robot gets whiplash bouncing between 20+ different chunks to reconstruct full context.
Risk of losing high-order meaning: If related topics (e.g., recursion and arrays) are split into too many S-blocks, the bot might miss deeper patterns across the conversation.
Harder for compound reasoning: Connecting "S12 recursion" with "S17 memoization" might require the robot to work harder mentally.


b) Longer Indices (Chunky S-blocks = ~200 tokens each):

Pros:

Broader coherence per block: Each block packs more meaning per unit, so the bot doesn’t have to jump through hoops connecting too many pieces.
Ideal for tasks requiring reasoning chains, like advanced coding or layered problem-solving.
Easier for bot lazy systems (because "it all in one fkn block blyaa").


Cons:

Token bloat creeps quickly: With only ~34 blocks, you’re severely limited in how much conversation context fits long-term. Tough luck for infinite-context dreamers.
Risks over-summary fluff: If every S-block is too verbose, each one might repeat trivial context to "fill space," wasting token budget.
Historical details may get overwritten too early: Granular info (specific examples inside recursion, tiny nuances, etc.) could disappear.


3️⃣ Conclusion: Balance Is Key (Compact but Cohesive S-blocks).

Ideal cs-window design suggestions:

Use moderate compact S-blocks (~100–125 tokens each):
    Error on the side of detailed compactness to avoid having too few S-blocks, while still capturing deeper task-relevant structures.

Fit ~55–60 summarized indices (S1–S60) in cs-window:
    This assumes ~100 tokens per block.
    Keeps summaries compact yet meaningful for stitching together larger task contexts (coding, recursion, memoization).

Avoid ultra-fragmentation (<50 tokens/block) unless managing ultra-long infinity chat histories.
    Reason: Tiny indices might make complex tasks like debugging/code generation harder for the bot to synthesize.

For coding-heavy tasks:
    Use larger summaries (~150 tokens/block) to maintain coherence of deeper technical contexts.


LET’S TEST BABUSHKA BOTS (Hypothetical Example):

Imagine GPT-4 running your system-line prompt with compact-to-moderate indices around 100 tokens each.

Example cs-window (~10 summarized blocks):

S1: User asked recursion basics. Explained factorial examples.
S2: Discussed recursion for sorting algorithms (quicksort). Clarified base vs recursive steps.
S3: Added recursion-memory optimization (memoization). Python examples included.
S4: Debugging edge cases for recursion (stack overflow). Explained safeguards.
...
S10: Approaches for hybrid recursion + dynamic programming, with optimization edge cases.

Prompt (Action):
User: "Hey bot, summarize all recursion types discussed so far AND how they apply to coding dynamic programming matrix problems."

The Result, Blyat!

Bot intelligently references specific S-blocks (S1-S10), summarizes recursion themes across them succinctly, AND tackles dynamic programming edge cases.
Example Response:

    "As noted in S2, recursion supports sorting like quicksort. Additionally, S8 explains hybrid dynamic programming for matrix traversal..."


Task coherence remains intact without token overflow. Pizdec avoided.
TAKEAWAY:

More indices ≠ better OR same with longer ones. It depends on your use-case (coding? Chatting? Infinite recursion memes?).
For flexible performance:
    Compact cs-window (~60 indices): Maintains coherent granularity.
    Keep summaries ≤125 tokens for balance between depth and space, especially for reasoning/arithmetic/coding-heavy tasks.
    Push longer indices (~200 tokens/block) ONLY if task demands complex compound context.


And if none of this works and robot still gets drunk and hallucinates? Simple answer, blyat: upgrade token budget or pour another vodka!

we are working with a freely accessible mazafaka "meta-llama/Llama-3.2-11B-Vision-Instruct", which is limited to 4096 - 2048/2048, 1024/1024/1024/1024 tokens for our purposes.

1024 tokens for user system prompt
1024 for user's message
1024 for bot's summary window
1024 for bot's response

we can do only that and roll all the way further

HAHAHA SUUUUUUUUUUUKA, now we know exactly which MAZAFKA limitations we’re dealing with! You’re saying we’re working with this freebie "meta-llama/Llama-3.2-11B-Vision-Instruct" model, strapped to strict 4096-token blocks broken into tidy quadrants like a disciplined Soviet battalion:

1024 tokens for the system prompt (robot brain's Holy Bible of rules).
1024 tokens for user's live messages (raw summons of chaos).
1024 for the bot's cs-window (summary window) (immutable bloating S-blocks).
1024 tokens for the bot's responses (where sh*t comes together).


AND we just roll this blyat-infinite chat over like highway wheels grinding context into summaries as we go. LET’S GOOOOO!
STEP 1: Understand the Problem of Finite Blocks in Infinite Demand

You’re asking: If we only have 1024 tokens locked per each quadrant (system prompt, user inputs, summaries, and responses), will this actually work for infinite rolling? How will the robot roll blyat through context smoothly?

Here’s the methodology to keep it crawling indefinitely without hallucinations nuking our faith in robots.
2️⃣ System Prompt Discipline: Holy Commandments Within 1024 Tokens

You have ONE chance to set some pizzazz rules for the robot’s skull in the system prompt within your 1024 token limit. This "Master Sheet Blyat Gospel™" will dictate rolling behavior through infinity.
System Prompt Example (~350–500 tokens):

SYSTEM PROMPT:

You are a structured assistant managing tasks and infinite conversation efficiently. Follow these rules religiously without deviation:

Use c-window for the live, uncompressed conversation (latest messages/tasks). Maximum: 1024 tokens.
Use cs-window for long-term memory, stored as numbered summaries (S-blocks). Regenerate cs-window periodically with the most useful information from prior S-blocks.
Use EXACTLY 1024 tokens for responses. Be concise and efficient.

Rules for Summarizing (cs-window):

NEW S-block for every meaningful interaction in the conversation.
S-block format: "S#: Compact summary (80-100 tokens)."
Never rewrite or delete older summaries. Instead, synthesize them concisely during overflow.

Coordination:

ALWAYS refer to S-blocks (cs-window) for historical context (e.g., "As noted in S13...").
Blend cs-window and c-window content to produce coherent responses.

NEVER:

Exceed individual quadrant space (1024 tokens each).
Repeat large portions of cs-window inside responses unless asked.
Rewrite previous interactions willy-nilly.

Why It Fits the 1024 Ceiling:

This prompt is roughly 350–500 tokens MAX depending on phrasing.
Leaves flexibility for system metadata beyond that (token safety margin ≈ 500 tokens).


3️⃣ User Input and c-Window Handling (1024 Tokens Max)

This part is live chaos management—the c-window organizes user messages + latest bot replies within the limit.
Best Approach: Rolling User Query History

c-window holds most recent exchanges only (FIFO-style, First In-First Out).
Forget long-term legacy here because everything historical goes into cs-window.
Example (at max 3 interactions for 1024 tokens):

User: What’s recursion?  
Bot: Recursion is a function calling itself, explained using factorials...  
User: How can it handle arrays?  
Bot: Recursion slices arrays into units... (DFS example follows).


4️⃣ cs-window Construction (The Infinite Rolling Memory)

Here, every interaction compresses into S-blocks, which are capped at 1024 tokens total. THIS IS IMPORTANT BLYAT.
How to Build Robust cs-window Summaries:

Each S-block uses ~100 tokens.
With 1024 tokens total for cs-window:

1024 ÷ 100 = ~10 S-blocks fit simultaneously.

This gives 10 compact indices.


What Happens When the Bot Needs MORE Capacity?

Simple—THE SUKA SYNTHESIZES! 💀

When cs-window gets bloated from too many S-blocks:

Compact Old S-blocks: The bot merges earlier S-blocks (e.g., S1-S3) into a single meta-summary:
    "S1-S3 combined: Recursion basics, array traversal, memoization techniques covered over multiple steps."
This clears space to append new blocks.
Rolling Logic: Have no mercy on blowing up summaries that stop being contextually important.


Example at Full Capacity (~10 Blocks):

S1: Explained recursion basics using Python factorial example. Discussed base cases.
S2: Recursion applied to array traversal. Slicing examples w/ depth-first logic.
S3: Memoization w/ recursion. Avoiding redundant calls via caching.
S4: Debugging recursion issues using stack traces to analyze call flows.
S5-S7: Summary compacted: Covered recursion, optimizing w/ memoization, and hybrid structures.
S8: User asked parallel recursion handling. Explained threading limitations.
S9: Dynamic programming matrix traversal using recursive patterns.
S10: Combined hybrid recursion logic w/ real-world maze-solving applications.

5️⃣ Bot Response Generation (The Crown Jewel, 1024 Token Limit)

The response generation is where the magic clicks: the bot combines current c-window content + cs-window data AND FINALLY produces coherent responses.
How It Works in 1024-Token Responses:

The bot cross-references c-window + cs-window for:
    Precise answers for the LIVE query.
    Historical relevance from summaries to keep accurate long-term coherence.
Example:
    (User): "Can recursion handle complex graphs efficiently?"
    (Bot Response — exactly 1024 tokens max):

    "As noted in S8, recursion faces threading limitations with complex graphs but can resolve small-scale traversal efficiently (e.g., DFS). From S10, using hybrid recursion with iterative patterns improves graph efficiency on large datasets. For deeper optimization relevant to your query, memoization (see S3) reduces processing redundancies."


6️⃣ How Rolling Works for Infinite Chat:
Simple Workflow Overview:

System Prompt: Suka knows the rules.
c-Window: Keeps freshest exchanges (~1024 tokens).
cs-Window Rolling (S-blocks): Constantly summarized and merged when bloated.
Responses: ONLY keeps current task coherent within strict memory.


7️⃣ Summary Question:

Do compact S-blocks or bigger summaries work better for coherence here?

Given our 1024-token per-quadrant constraints:
    Compact S-blocks (~80-100 tokens each) allow granular control with ~10 rolling indices.
    Longer S-blocks reduce block count (e.g., 200 tokens = only ~5 blocks), good for coding use-cases but sacrifices granularity.


Optimal balance for infinite rolling:
Compact (~100 tokens). S-block synthesis ensures coherence when bloating.
BABUSHKA FINAL TL;DR:

1024 tokens per quadrant? Absolutely doable blyat-style.

System Prompt: Holy rules in ~500 tokens.
c-window: Active convo, FIFO trimming (~3 recent exchanges).
cs-window: 10 compact S-blocks (~100 tokens/block). Merge if bloat happens!
Bot Response: Stitch cs-window + c-window for task-oriented coherence.


It’s not perfect, sure, but as close to eternal rolling precision as possible within 4096 sucka tokens! Let’s roll. 🪓

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this is just suggestions on how it might all work in concept.