.gitea/workflows/datadog-sca.yml

@@ -0,0 +1,20 @@
+on: [push]
+
+name: Datadog Software Composition Analysis
+
+jobs:
+  software-composition-analysis:
+    runs-on: ubuntu-latest
+    name: Datadog SBOM Generation and Upload
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+    - name: Check imported libraries are secure and compliant
+      id: datadog-software-composition-analysis
+      uses: DataDog/datadog-sca-github-action@main
+      with:
+        dd_api_key: ${{ secrets.DD_API_KEY }}
+        dd_app_key: ${{ secrets.DD_APP_KEY }}
+        dd_service: jarvis
+        dd_env: ci
+        dd_site: us5.datadoghq.com

.gitea/workflows/datadog-static-analysis.yml

@@ -0,0 +1,21 @@
+on: [push]
+
+name: Datadog Static Analysis
+
+jobs:
+  static-analysis:
+    runs-on: ubuntu-latest
+    name: Datadog Static Analyzer
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+    - name: Check code meets quality and security standards
+      id: datadog-static-analysis
+      uses: DataDog/datadog-static-analyzer-github-action@v1
+      with:
+        dd_api_key: ${{ secrets.DD_API_KEY }}
+        dd_app_key: ${{ secrets.DD_APP_KEY }}
+        dd_service: jarvis
+        dd_env: ci
+        dd_site: us5.datadoghq.com
+        cpu_count: 2

.github/workflows/datadog-sca.yml

@@ -0,0 +1,20 @@
+on: [push]
+
+name: Datadog Software Composition Analysis
+
+jobs:
+  software-composition-analysis:
+    runs-on: ubuntu-latest
+    name: Datadog SBOM Generation and Upload
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+    - name: Check imported libraries are secure and compliant
+      id: datadog-software-composition-analysis
+      uses: DataDog/datadog-sca-github-action@main
+      with:
+        dd_api_key: ${{ secrets.DD_API_KEY }}
+        dd_app_key: ${{ secrets.DD_APP_KEY }}
+        dd_service: jarvis
+        dd_env: ci
+        dd_site: us5.datadoghq.com

.github/workflows/datadog-static-analysis.yml

@@ -0,0 +1,21 @@
+on: [push]
+
+name: Datadog Static Analysis
+
+jobs:
+  static-analysis:
+    runs-on: ubuntu-latest
+    name: Datadog Static Analyzer
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+    - name: Check code meets quality and security standards
+      id: datadog-static-analysis
+      uses: DataDog/datadog-static-analyzer-github-action@v1
+      with:
+        dd_api_key: ${{ secrets.DD_API_KEY }}
+        dd_app_key: ${{ secrets.DD_APP_KEY }}
+        dd_service: jarvis
+        dd_env: ci
+        dd_site: us5.datadoghq.com
+        cpu_count: 2

.gitignore

@@ -174,3 +174,46 @@ cython_debug/
 pyvenv.cfg
 .venv
 pip-selfcheck.json
+
+
+# Logs
+logs
+*.log
+npm-debug.log*
+
+# Runtime data
+pids
+*.pid
+*.seed
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (http://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules
+jspm_packages
+
+# Optional npm cache directory
+.npm
+
+# Optional REPL history
+.node_repl_history
+.next
+
+config.ini
+*.db

Dockerfile

@@ -0,0 +1,20 @@
+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 5001 available to the world outside this container
+EXPOSE 5001
+
+# Define environment variable
+ENV FLASK_APP=main.py
+
+# Run app.py when the container launches
+CMD ["python", "main.py"]

README.md

@@ -0,0 +1,5 @@
+# Jarvis
+
+it's actually not that smart!
+
+

client.py

@@ -0,0 +1,138 @@
+import time
+
+import requests
+
+
+class LLMChatClient:
+    def __init__(self, base_url, api_key):
+        self.base_url = base_url.rstrip("/")
+        self.api_key = api_key
+        self.headers = {"X-API-Key": api_key, "Content-Type": "application/json"}
+
+    def submit_query(self, message):
+        """
+        Submit a query to the LLM Chat Server.
+
+        Args:
+            message (str): The message to send to the server.
+
+        Returns:
+            str: The query ID for the submitted query.
+
+        Raises:
+            requests.RequestException: If the request fails.
+
+        Example:
+            client = LLMChatClient('http://localhost:5001', 'your-api-key')
+            query_id = client.submit_query('What is the capital of France?')
+            print(f"Query ID: {query_id}")
+
+        cURL equivalent:
+            curl -X POST http://localhost:5001/api/v1/query \
+                 -H "Content-Type: application/json" \
+                 -H "X-API-Key: your-api-key" \
+                 -d '{"message": "What is the capital of France?"}'
+        """
+        url = f"{self.base_url}/api/v1/query"
+        data = {"message": message}
+        response = requests.post(url, json=data, headers=self.headers)
+        response.raise_for_status()
+        return response.json()["query_id"]
+
+    def get_query_status(self, query_id):
+        """
+        Get the status of a submitted query.
+
+        Args:
+            query_id (str): The ID of the query to check.
+
+        Returns:
+            dict: A dictionary containing the status and conversation history (if completed).
+
+        Raises:
+            requests.RequestException: If the request fails.
+
+        Example:
+            client = LLMChatClient('http://localhost:5001', 'your-api-key')
+            status = client.get_query_status('query-id-here')
+            print(f"Query status: {status['status']}")
+            if status['status'] == 'completed':
+                print(f"Conversation history: {status['conversation_history']}")
+
+        cURL equivalent:
+            curl -X GET http://localhost:5001/api/v1/query_status/query-id-here \
+                 -H "X-API-Key: your-api-key"
+        """
+        url = f"{self.base_url}/api/v1/query_status/{query_id}"
+        response = requests.get(url, headers=self.headers)
+        response.raise_for_status()
+        return response.json()
+
+    def submit_query_and_wait(self, message, max_wait_time=300, poll_interval=2):
+        """
+        Submit a query and wait for the result.
+
+        Args:
+            message (str): The message to send to the server.
+            max_wait_time (int): Maximum time to wait for the result in seconds.
+            poll_interval (int): Time between status checks in seconds.
+
+        Returns:
+            dict: The completed conversation history.
+
+        Raises:
+            requests.RequestException: If the request fails.
+            TimeoutError: If the query doesn't complete within max_wait_time.
+
+        Example:
+            client = LLMChatClient('http://localhost:5001', 'your-api-key')
+            result = client.submit_query_and_wait('What is the capital of France?')
+            print(f"Conversation history: {result}")
+        """
+        query_id = self.submit_query(message)
+        start_time = time.time()
+
+        while time.time() - start_time < max_wait_time:
+            status = self.get_query_status(query_id)
+            if status["status"] == "completed":
+                return status["conversation_history"]
+            time.sleep(poll_interval)
+
+        raise TimeoutError(f"Query did not complete within {max_wait_time} seconds")
+
+
+class LLMChatAdminClient:
+    def __init__(self, base_url, admin_key):
+        self.base_url = base_url.rstrip("/")
+        self.admin_key = admin_key
+        self.headers = {"X-Admin-Key": admin_key, "Content-Type": "application/json"}
+
+    def generate_api_key(self, username):
+        """
+        Generate a new API key for a user.
+
+        Args:
+            username (str): The username to generate the API key for.
+
+        Returns:
+            dict: A dictionary containing the username and generated API key.
+
+        Raises:
+            requests.RequestException: If the request fails.
+
+        Example:
+            admin_client = LLMChatAdminClient('http://localhost:5001', 'your-admin-key')
+            result = admin_client.generate_api_key('new_user')
+            print(f"Generated API key for {result['username']}: {result['api_key']}")
+
+        cURL equivalent:
+            curl -X POST http://localhost:5001/admin/generate_key \
+                 -H "Content-Type: application/json" \
+                 -H "X-Admin-Key: your-admin-key" \
+                 -d '{"username": "new_user"}'
+        """
+        url = f"{self.base_url}/admin/generate_key"
+        data = {"username": username}
+        response = requests.post(url, json=data, headers=self.headers)
+        response.raise_for_status()
+        return response.json()

docker-compose.yml

@@ -0,0 +1,16 @@
+version: '3.8'
+
+services:
+  llm-chat-server:
+    build: .
+    ports:
+      - "5001:5001"
+    volumes:
+      - ./llm_chat_server.db:/app/llm_chat_server.db
+      - ./config.ini:/app/config.ini
+    environment:
+      - FLASK_ENV=production
+    restart: unless-stopped
+
+volumes:
+  llm_chat_server_db:

index.html

@@ -9,6 +9,8 @@
     <script src="https://cdn.jsdelivr.net/npm/moment@2.29.4/moment.min.js"></script>
     <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
     <script src="https://cdn.jsdelivr.net/npm/chartjs-adapter-moment@1.0.1/dist/chartjs-adapter-moment.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/default.min.css">
     <link href="https://fonts.googleapis.com/css2?family=Noto+Sans+Mono:wght@400;700&display=swap" rel="stylesheet">
     <style>
         body {
@@ -86,13 +88,19 @@
             color: #888;
         }
         .thought-summary {
-            cursor: pointer;
-            color: #fff;
-            margin-bottom: 5px;
             font-weight: bold;
-            display: flex;
+            margin-bottom: 5px;
-            align-items: center;
+            padding: 5px;
+            border-radius: 3px;
         }
+        .thought-summary.plan { background-color: #2c3e50; }
+        .thought-summary.decision { background-color: #34495e; }
+        .thought-summary.tool_call { background-color: #16a085; }
+        .thought-summary.tool_result { background-color: #27ae60; }
+        .thought-summary.think_more { background-color: #2980b9; }
+        .thought-summary.answer { background-color: #8e44ad; }
+        .thought-summary.reply { background-color: #f39c12; }
+        .thought-summary.thoughts { background-color: #f39c12; }
         .thought-details {
             display: none;
             margin-left: 20px;
@@ -238,11 +246,111 @@
                 transform: translateX(0);
             }
         }
+
+        .conversation-history-container {
+            margin-top: 20px;
+            background-color: #222;
+            border-radius: 5px;
+            padding: 10px;
+        }
+
+        #conversation-history {
+            color: #fff;
+            font-family: 'Noto Sans Mono', monospace;
+            font-size: 12px;
+        }
+        .history-card {
+            background-color: #2c3e50;
+            border-radius: 5px;
+            padding: 10px;
+            margin-bottom: 10px;
+        }
+        .history-role {
+            font-weight: bold;
+            margin-bottom: 5px;
+        }
+        .history-content {
+            white-space: pre-wrap;
+            word-break: break-word;
+        }
+
+        .error-message {
+            background-color: #ff6b6b;
+            color: #fff;
+            padding: 10px;
+            border-radius: 5px;
+            margin-bottom: 10px;
+        }
+
+        .retrying {
+            background-color: #feca57;
+            color: #333;
+        }
+
+        #clear-history-button {
+            background-color: #e74c3c;
+            color: white;
+            border: none;
+            padding: 10px;
+            margin-bottom: 10px;
+            cursor: pointer;
+            font-family: 'Noto Sans Mono', monospace;
+            font-size: 14px;
+            border-radius: 5px;
+        }
+
+        #clear-history-button:hover {
+            background-color: #c0392b;
+        }
+
+        #chat-tabs {
+            display: flex;
+            background-color: #222;
+            padding: 10px 10px 0 10px;
+        }
+        
+        .chat-tab {
+            background-color: #444;
+            color: #fff;
+            border: none;
+            padding: 10px 20px;
+            margin-right: 5px;
+            cursor: pointer;
+            border-top-left-radius: 5px;
+            border-top-right-radius: 5px;
+        }
+        
+        .chat-tab.active {
+            background-color: #666;
+        }
+        
+        #new-chat-button {
+            background-color: #27ae60;
+            color: #fff;
+            border: none;
+            padding: 10px 20px;
+            cursor: pointer;
+            border-top-left-radius: 5px;
+            border-top-right-radius: 5px;
+        }
+        
+        .close-tab {
+            margin-left: 10px;
+            color: #ff6b6b;
+            cursor: pointer;
+        }
+        
+        .thinking-section {
+            margin-bottom: 20px;
+            border-left: 2px solid #444;
+            padding-left: 10px;
+        }
     </style>
 </head>
 <body>
     <div id="main-container">
         <div id="chat-area">
+            <div id="chat-tabs"></div>
             <div id="chat-container"></div>
             <div id="input-container" class="pdp-panel">
                 <div class="pdp-label">INPUT:</div>
@@ -272,6 +380,12 @@
                 <div class="graph-title">GPU Memory</div>
                 <canvas id="gpuMemoryChart"></canvas>
             </div>
+            
+            <!-- Add this new section for conversation history -->
+            <div class="conversation-history-container">
+                <div class="graph-title">Conversation History</div>
+                <div id="conversation-history"></div>
+            </div>
         </div>
     </div>
 
@@ -280,10 +394,80 @@
         const chatContainer = document.getElementById('chat-container');
         const userInput = document.getElementById('user-input');
         const sendButton = document.getElementById('send-button');
+        const chatTabs = document.getElementById('chat-tabs');
 
-        let thinkingElement = null;
+        let currentChatId = null;
-        let thinkingDetails = null;
+        let chats = {};
-        let thinkingStartTime = null;
+
+        function createNewChat() {
+            const chatId = Date.now().toString();
+            chats[chatId] = {
+                messages: [],
+                thinkingSections: []
+            };
+            addChatTab(chatId);
+            switchToChat(chatId);
+            saveChats();
+        }
+
+        function addChatTab(chatId) {
+            const tab = document.createElement('button');
+            tab.classList.add('chat-tab');
+            tab.textContent = `Chat ${Object.keys(chats).length}`;
+            tab.onclick = () => switchToChat(chatId);
+
+            const closeButton = document.createElement('span');
+            closeButton.classList.add('close-tab');
+            closeButton.textContent = '×';
+            closeButton.onclick = (e) => {
+                e.stopPropagation();
+                closeChat(chatId);
+            };
+
+            tab.appendChild(closeButton);
+            chatTabs.insertBefore(tab, chatTabs.lastElementChild);
+        }
+
+        function switchToChat(chatId) {
+            currentChatId = chatId;
+            document.querySelectorAll('.chat-tab').forEach(tab => tab.classList.remove('active'));
+            document.querySelector(`.chat-tab:nth-child(${Object.keys(chats).indexOf(chatId) + 1})`).classList.add('active');
+            renderChat(chatId);
+        }
+
+        function closeChat(chatId) {
+            delete chats[chatId];
+            saveChats();
+            const tabToRemove = Array.from(chatTabs.children).find(tab => tab.textContent.includes(`Chat ${Object.keys(chats).indexOf(chatId) + 1}`));
+            if (tabToRemove) {
+                chatTabs.removeChild(tabToRemove);
+            }
+            if (currentChatId === chatId) {
+                const remainingChatIds = Object.keys(chats);
+                if (remainingChatIds.length > 0) {
+                    switchToChat(remainingChatIds[0]);
+                } else {
+                    createNewChat();
+                }
+            }
+        }
+
+        function renderChat(chatId) {
+            chatContainer.innerHTML = '';
+            const chat = chats[chatId];
+            chat.messages.forEach(message => addMessage(message.content, message.isUser));
+            chat.thinkingSections.forEach(section => {
+                const thinkingSection = createThinkingSection();
+                section.thoughts.forEach(thought => addThought(thought.type, thought.content, thought.details, thinkingSection));
+            });
+        }
+
+        function createThinkingSection() {
+            const section = document.createElement('div');
+            section.classList.add('thinking-section');
+            chatContainer.appendChild(section);
+            return section;
+        }
 
         function addMessage(message, isUser) {
             const messageElement = document.createElement('div');
@@ -292,65 +476,40 @@
             messageElement.innerHTML = isUser ? message : marked.parse(message);
             chatContainer.appendChild(messageElement);
             chatContainer.scrollTop = chatContainer.scrollHeight;
+
+            if (currentChatId) {
+                chats[currentChatId].messages.push({ content: message, isUser: isUser });
+                saveChats();
+            }
         }
 
-        function startThinking() {
+        function addThought(type, content, details = '', thinkingSection) {
-            thinkingElement = document.createElement('div');
+            const stepElement = document.createElement('div');
-            thinkingElement.classList.add('thought-summary', 'collapsible');
+            stepElement.classList.add('thought-summary', 'collapsible', type);
+            stepElement.textContent = type.charAt(0).toUpperCase() + type.slice(1).replace('_', ' ') + ':';
+            stepElement.onclick = toggleStepDetails;
+
+            const stepDetails = document.createElement('div');
+            stepDetails.classList.add('thought-details');
             
-            const led = document.createElement('div');
+            if (type === 'error') {
-            led.classList.add('led', 'blinking');
+                stepElement.classList.add('error-message');
-            
+                if (content.includes('retrying')) {
-            const textNode = document.createTextNode('Thinking...');
+                    stepElement.classList.add('retrying');
-            
+                }
-            thinkingElement.appendChild(led);
+                stepDetails.innerHTML = marked.parse(content + '\n\nDetails:\n```\n' + details + '\n```');
-            thinkingElement.appendChild(textNode);
+            } else {
-            thinkingElement.onclick = toggleThinkingDetails;
+                stepDetails.innerHTML = marked.parse(content);
-            
+            }
-            thinkingDetails = document.createElement('div');
+
-            thinkingDetails.classList.add('thought-details');
+            thinkingSection.appendChild(stepElement);
-            
+            thinkingSection.appendChild(stepDetails);
-            chatContainer.appendChild(thinkingElement);
-            chatContainer.appendChild(thinkingDetails);
-            
-            thinkingStartTime = Date.now();
             chatContainer.scrollTop = chatContainer.scrollHeight;
-        }
 
-        function addThought(step, content) {
+            if (currentChatId) {
-            if (thinkingDetails) {
+                const currentThinkingSection = chats[currentChatId].thinkingSections[chats[currentChatId].thinkingSections.length - 1];
-                const stepElement = document.createElement('div');
+                currentThinkingSection.thoughts.push({ type, content, details });
-                stepElement.classList.add('thought-summary', 'collapsible');
+                saveChats();
-                stepElement.textContent = step;
-                stepElement.onclick = toggleStepDetails;
-
-                const stepDetails = document.createElement('div');
-                stepDetails.classList.add('thought-details');
-                stepDetails.innerHTML = content;
-
-                thinkingDetails.appendChild(stepElement);
-                thinkingDetails.appendChild(stepDetails);
-                chatContainer.scrollTop = chatContainer.scrollHeight;
-            }
-        }
-
-        function endThinking(thinkingTime) {
-            if (thinkingElement) {
-                const textNode = thinkingElement.childNodes[1];
-                textNode.nodeValue = `Thinking... (${thinkingTime}s)`;
-                const led = thinkingElement.querySelector('.led');
-                led.classList.remove('blinking');
-                led.style.backgroundColor = '#0f0';
-                led.style.boxShadow = '0 0 10px #0f0';
-                thinkingStartTime = null;
-            }
-        }
-
-        function toggleThinkingDetails() {
-            this.classList.toggle('open');
-            const details = this.nextElementSibling;
-            if (details) {
-                details.style.display = details.style.display === 'none' ? 'block' : 'none';
             }
         }
 
@@ -362,34 +521,71 @@
             }
         }
 
-        socket.on('thinking', (data) => {
+        function saveChats() {
-            if (!thinkingElement) startThinking();
+            localStorage.setItem('chats', JSON.stringify(chats));
-            addThought(data.step, 'Started');
+        }
-        });
 
-        socket.on('thought', (data) => {
+        function loadChats() {
-            addThought('Result', data.content);
+            const storedChats = localStorage.getItem('chats');
-        });
+            if (storedChats) {
-
+                chats = JSON.parse(storedChats);
-        socket.on('chat_response', (data) => {
+                Object.keys(chats).forEach(chatId => addChatTab(chatId));
-            endThinking(data.thinking_time);
+                if (Object.keys(chats).length > 0) {
-            addMessage(data.response, false);
+                    switchToChat(Object.keys(chats)[0]);
-        });
+                } else {
-
+                    createNewChat();
-        socket.on('error', (data) => {
+                }
-            endThinking(data.thinking_time);
+            } else {
-            addMessage(`Error: ${data.message}`, false);
+                createNewChat();
-        });
+            }
+        }
 
         function sendMessage() {
             const message = userInput.value.trim();
-            if (message) {
+            if (message && currentChatId) {
                 addMessage(message, true);
-                socket.emit('chat_request', { message: message });
+                chats[currentChatId].thinkingSections.push({ thoughts: [] });
+                socket.emit('chat_request', { 
+                    message: message,
+                    conversation_history: chats[currentChatId].messages.filter(m => !m.isUser).map(m => ({ role: 'assistant', content: m.content }))
+                        .concat(chats[currentChatId].messages.filter(m => m.isUser).map(m => ({ role: 'user', content: m.content })))
+                });
                 userInput.value = '';
             }
         }
 
+        socket.on('thinking', (data) => {
+            if (currentChatId) {
+                const newThinkingSection = createThinkingSection();
+                chats[currentChatId].thinkingSections.push({ thoughts: [] });
+                addThought(data.step, 'Started', '', newThinkingSection);
+            }
+        });
+
+        socket.on('thought', (data) => {
+            if (currentChatId) {
+                const currentThinkingSection = chatContainer.querySelector('.thinking-section:last-child');
+                addThought(data.type, data.content, data.details, currentThinkingSection);
+            }
+        });
+
+        socket.on('chat_response', (data) => {
+            if (currentChatId) {
+                addMessage(data.response, false);
+            }
+        });
+
+        socket.on('error', (data) => {
+            if (currentChatId) {
+                const currentThinkingSection = chatContainer.querySelector('.thinking-section:last-child');
+                if (data.type === 'retrying') {
+                    addThought('error', data.content, '', currentThinkingSection);
+                } else {
+                    addThought('error', data.message, '', currentThinkingSection);
+                }
+            }
+        });
+
         sendButton.addEventListener('click', sendMessage);
         userInput.addEventListener('keypress', function(e) {
             if (e.key === 'Enter' && !e.shiftKey) {
@@ -398,6 +594,16 @@
             }
         });
 
+        // Add new chat button
+        const newChatButton = document.createElement('button');
+        newChatButton.id = 'new-chat-button';
+        newChatButton.textContent = '+ New Chat';
+        newChatButton.onclick = createNewChat;
+        chatTabs.appendChild(newChatButton);
+
+        // Load chats when the page loads
+        loadChats();
+
         const chartOptions = {
             type: 'line',
             options: {
@@ -570,6 +776,41 @@
 
         window.addEventListener('resize', checkWindowSize);
         checkWindowSize(); // Initial check
+
+        // Add this new function to update the conversation history
+        function updateConversationHistory(history) {
+            const conversationHistoryElement = document.getElementById('conversation-history');
+            conversationHistoryElement.innerHTML = '';
+            
+            history.forEach(item => {
+                const card = document.createElement('div');
+                card.classList.add('history-card');
+                
+                const role = document.createElement('div');
+                role.classList.add('history-role');
+                role.textContent = item.role.charAt(0).toUpperCase() + item.role.slice(1);
+                
+                const content = document.createElement('pre');
+                content.classList.add('history-content');
+                content.innerHTML = hljs.highlightAuto(item.content).value;
+                
+                card.appendChild(role);
+                card.appendChild(content);
+                conversationHistoryElement.appendChild(card);
+            });
+        }
+
+        // Add this new socket listener
+        socket.on('conversation_history', (data) => {
+            updateConversationHistory(data.history);
+        });
+
+        // Add event listener for the clear history button
+        clearHistoryButton.addEventListener('click', () => {
+            if (confirm('Are you sure you want to clear the conversation history?')) {
+                clearConversationHistory();
+            }
+        });
     </script>
 </body>
 </html>

models.py

@@ -3,30 +3,86 @@ import structlog
 
 logger = structlog.get_logger()
 
+
 class ModelManager:
     def __init__(self):
         self.model_capabilities = {
-            "qwen2.5:7b": ["general_knowledge", "structured_output", "multilingual", "instruction_following", "structured_data"],
+            "ajindal/llama3.1-storm:8b": [
-            "llama3.1:8b": ["general_knowledge", "reasoning", "tool_calling", "conversation", "multilingual", "instruction_following"],
+                "general_knowledge",
-            "qwen2.5-coder:7b": ["code_generation", "code_analysis", "instruction_following", "math_reasoning"],
+                "reasoning",
-            "llama3.2:3b": ["summarization", "instruction_following", "tool_calling", "multilingual"],
+                "tool_calling",
-            "llava:7b": ["visual_reasoning", "visual_conversation", "visual_tool_calling", "vision", "ocr", "multimodal"],
+                "conversation",
+                "multilingual",
+                "instruction_following",
+            ],
+            "llama3.1:8b": [
+                "general_knowledge",
+                "reasoning",
+                "tool_calling",
+                "conversation",
+                "multilingual",
+                "instruction_following",
+            ],
+            "qwen2.5:7b": [
+                "general_knowledge",
+                "reasoning",
+                "tool_calling",
+                "conversation",
+                "multilingual",
+                "instruction_following",
+            ],
+            "llama3.2:3b": [
+                "summarization",
+                "instruction_following",
+                "tool_calling",
+                "multilingual",
+            ],
+            "llava:7b": [
+                "visual_reasoning",
+                "visual_conversation",
+                "visual_tool_calling",
+                "vision",
+                "ocr",
+                "multimodal",
+            ],
         }
-        logger.info("ModelManager initialized", model_capabilities=self.model_capabilities)
+        logger.info(
+            "ModelManager initialized", model_capabilities=self.model_capabilities
+        )
 
     def get_model_capabilities(self, model_name):
         capabilities = self.model_capabilities.get(model_name, [])
-        logger.debug("Retrieved model capabilities", model=model_name, capabilities=capabilities)
+        logger.debug(
+            "Retrieved model capabilities", model=model_name, capabilities=capabilities
+        )
         return capabilities
 
     def select_best_model(self, required_capability):
-        suitable_models = [model for model, capabilities in self.model_capabilities.items() if required_capability in capabilities]
+        suitable_models = [
-        selected_model = suitable_models[0] if suitable_models else list(self.model_capabilities.keys())[0]
+            model
-        logger.info("Selected best model", required_capability=required_capability, selected_model=selected_model)
+            for model, capabilities in self.model_capabilities.items()
+            if required_capability in capabilities
+        ]
+        selected_model = (
+            suitable_models[0]
+            if suitable_models
+            else list(self.model_capabilities.keys())[0]
+        )
+        logger.info(
+            "Selected best model",
+            required_capability=required_capability,
+            selected_model=selected_model,
+        )
         return selected_model
 
-    def generate_text(self, model_name, prompt, max_length=100, system="You are a helpful assistant."):
+    def generate_text(
-        logger.debug("Generating text", model=model_name, prompt=prompt, max_length=max_length)
+        self,
+        model_name,
+        prompt,
+        max_length=100,
+        system="You are a helpful assistant.",
+        tools=[],
+    ):
         # Check if model exists
         try:
             ollama.pull(model_name)
@@ -38,8 +94,16 @@ class ModelManager:
             else:
                 logger.exception("Error pulling model", model=model_name, error=str(e))
                 raise e
-        response = ollama.generate(model=model_name, prompt=prompt, system=system)
-        logger.debug("Text generated", model=model_name, response=response['response'])
-        return response['response']
 
-model_manager = ModelManager()
+        response = ollama.generate(
+            model=model_name,
+            prompt=prompt,
+            system=system,
+            tools=tools,
+            max_tokens=max_length,
+        )
+        logger.debug("Text generated", model=model_name, response=response["response"])
+        return response["response"]
+
+
+model_manager = ModelManager()

requirements.txt

@@ -4,11 +4,19 @@ aiohttp==3.10.5
 aiosignal==1.3.1
 annotated-types==0.7.0
 anyio==4.6.0
+art==6.3
 attrs==24.2.0
+beautifulsoup4==4.12.3
+bidict==0.23.1
+black==24.8.0
+blinker==1.8.2
+bs4==0.0.2
 certifi==2024.7.4
+chardet==5.2.0
 charset-normalizer==3.3.2
 click==8.1.7
 cloudpickle==3.0.0
+cssselect==1.2.0
 datasets==3.0.0
 dill==0.3.8
 diskcache==5.6.3
@@ -17,9 +25,13 @@ duckduckgo_search==6.2.6
 einops==0.8.0
 fastapi==0.115.0
 filelock==3.15.4
+Flask==3.0.3
+flask-openapi3==3.1.3
+Flask-SocketIO==5.3.7
 frozenlist==1.4.1
 fsspec==2024.6.1
 gguf==0.9.1
+GPUtil==1.4.0
 h11==0.14.0
 httpcore==1.0.5
 httptools==0.6.1
@@ -29,6 +41,8 @@ idna==3.7
 importlib_metadata==8.5.0
 inquirerpy==0.3.4
 interegular==0.3.3
+isort==5.13.2
+itsdangerous==2.2.0
 Jinja2==3.1.4
 jiter==0.5.0
 jsonschema==4.23.0
@@ -36,6 +50,9 @@ jsonschema-specifications==2023.12.1
 lark==1.2.2
 llvmlite==0.43.0
 lm-format-enforcer==0.10.6
+lxml==5.3.0
+lxml_html_clean==0.2.2
+markdownify==0.13.1
 MarkupSafe==2.1.5
 mistral_common==1.4.3
 mpmath==1.3.0
@@ -43,6 +60,7 @@ msgpack==1.1.0
 msgspec==0.18.6
 multidict==6.1.0
 multiprocess==0.70.16
+mypy-extensions==1.0.0
 nest-asyncio==1.6.0
 networkx==3.3
 numba==0.60.0
@@ -60,13 +78,16 @@ nvidia-ml-py==12.560.30
 nvidia-nccl-cu12==2.20.5
 nvidia-nvjitlink-cu12==12.6.20
 nvidia-nvtx-cu12==12.1.105
+ollama==0.3.3
 openai==1.47.1
 outlines==0.0.46
 packaging==24.1
 pandas==2.2.3
 partial-json-parser==0.2.1.1.post4
+pathspec==0.12.1
 pfzy==0.3.4
 pillow==10.4.0
+platformdirs==4.3.6
 primp==0.5.5
 prometheus-fastapi-instrumentator==7.0.0
 prometheus_client==0.21.0
@@ -81,10 +102,13 @@ pydantic==2.9.2
 pydantic_core==2.23.4
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
+python-engineio==4.9.1
+python-socketio==5.11.4
 pytz==2024.2
 PyYAML==6.0.2
 pyzmq==26.2.0
 ray==2.36.1
+readability-lxml==0.8.1
 referencing==0.35.1
 regex==2024.7.24
 requests==2.32.3
@@ -92,9 +116,12 @@ rpds-py==0.20.0
 safetensors==0.4.4
 sentencepiece==0.2.0
 setuptools==72.1.0
+simple-websocket==1.0.0
 six==1.16.0
 sniffio==1.3.1
+soupsieve==2.6
 starlette==0.38.6
+structlog==24.4.0
 sympy==1.13.2
 tiktoken==0.7.0
 tokenizers==0.19.1
@@ -113,6 +140,8 @@ vllm-flash-attn==2.6.1
 watchfiles==0.24.0
 wcwidth==0.2.13
 websockets==13.1
+Werkzeug==3.0.4
+wsproto==1.2.0
 xformers==0.0.27.post2
 xxhash==3.5.0
 yarl==1.12.0

schema.sql

@@ -0,0 +1,16 @@
+CREATE TABLE IF NOT EXISTS Keys (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    username TEXT NOT NULL UNIQUE,
+    api_key TEXT NOT NULL UNIQUE
+);
+
+CREATE TABLE IF NOT EXISTS Queries (
+    id TEXT PRIMARY KEY,
+    ip TEXT NOT NULL,
+    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
+    query TEXT NOT NULL,
+    api_key_id INTEGER,
+    status TEXT NOT NULL,
+    conversation_history TEXT,
+    FOREIGN KEY (api_key_id) REFERENCES Keys (id)
+);

tools.py

@@ -0,0 +1,369 @@
+import subprocess
+import tempfile
+import time
+import json
+import requests
+from markdownify import markdownify as md
+from readability.readability import Document
+import duckduckgo_search
+import datetime
+import random
+import math
+import re
+import base64
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageFont
+import ollama
+import os
+
+class Tool:
+    def __init__(self, name: str, description: str, arguments: dict, returns: str):
+        self.name = name
+        self.description = description
+        self.arguments = arguments
+        self.returns = returns
+
+    def execute(self, arguments: dict) -> str:
+        pass
+
+
+class ToolManager:
+    def __init__(self):
+        self.tools = []
+
+    def add_tool(self, tool: Tool):
+        self.tools.append(tool)
+
+    def get_tool(self, name: str) -> Tool:
+        for tool in self.tools:
+            if tool.name == name:
+                return tool
+        return None
+
+    def get_tools_and_descriptions_for_prompt(self):
+        return "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
+
+    def get_tools_for_ollama_dict(self):
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": tool.name,
+                    "description": tool.description,
+                    "parameters": tool.arguments,
+                },
+            }
+            for tool in self.tools
+        ]
+
+
+class DefaultToolManager(ToolManager):
+    def __init__(self):
+        super().__init__()
+        self.add_tool(SearchTool())
+        self.add_tool(GetReadablePageContentsTool())
+        self.add_tool(CalculatorTool())
+        self.add_tool(PythonCodeTool())
+        self.add_tool(DateTimeTool())
+        self.add_tool(RandomNumberTool())
+        self.add_tool(RegexTool())
+        self.add_tool(Base64Tool())
+        self.add_tool(SimpleChartTool())
+        self.add_tool(LLAVAImageAnalysisTool())
+
+
+class SearchTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "search_web",
+            "Search the internet for information",
+            {
+                "type": "object",
+                "properties": {
+                    "query": {"type": "string", "description": "The search query"}
+                },
+            },
+            "results:list[string]",
+        )
+
+    def execute(self, arg: dict) -> str:
+        try:
+            res = duckduckgo_search.DDGS().text(arg["query"], max_results=5)
+            return "\n\n".join([f"{r['title']}\n{r['body']}\n{r['href']}" for r in res])
+        except Exception as e:
+            return f"Error searching the web: {str(e)}"
+
+
+def get_readable_page_contents(url: str) -> str:
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        doc = Document(response.content)
+        content = doc.summary()
+        return md(content)
+    except Exception as e:
+        return f"Error fetching readable content: {str(e)}"
+
+
+class GetReadablePageContentsTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "get_readable_page_contents",
+            "Get the contents of a web page in a readable format",
+            {
+                "type": "object",
+                "properties": {
+                    "url": {"type": "string", "description": "The url of the web page"}
+                },
+            },
+            "contents:string",
+        )
+
+    def execute(self, arg: dict) -> str:
+        return get_readable_page_contents(arg["url"])
+
+
+class CalculatorTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "calculator",
+            "Perform a calculation using python's eval function",
+            {
+                "type": "object",
+                "properties": {
+                    "expression": {
+                        "type": "string",
+                        "description": "The mathematical expression to evaluate, should be a python mathematical expression",
+                    }
+                },
+            },
+            "result:string",
+        )
+
+    def execute(self, arg: dict) -> str:
+        try:
+            return str(eval(arg["expression"]))
+        except Exception as e:
+            return f"Error executing code: {str(e)}"
+
+
+class PythonCodeTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "python_code",
+            "Execute python code using a temporary file and a subprocess. You must print results to stdout.",
+            {
+                "type": "object",
+                "properties": {
+                    "code": {
+                        "type": "string",
+                        "description": "The python code to execute, can be multiline",
+                    }
+                },
+            },
+            "result:string",
+        )
+
+    def execute(self, arg: dict) -> str:
+        try:
+            with tempfile.NamedTemporaryFile(
+                suffix=".py", mode="w", delete=False
+            ) as temp_file:
+                temp_file.write(arg["code"])
+                temp_file.flush()
+
+                start_time = time.time()
+                process = subprocess.Popen(
+                    ["python", temp_file.name],
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.PIPE,
+                    text=True,
+                )
+                stdout, stderr = process.communicate(timeout=10)  # 10 second timeout
+                end_time = time.time()
+                execution_time = end_time - start_time
+
+                result = {
+                    "stdout": stdout,
+                    "stderr": stderr,
+                    "return_value": process.returncode,
+                    "execution_time": execution_time,
+                }
+
+        except subprocess.TimeoutExpired:
+            process.kill()
+            return "Error: Code execution timed out after 10 seconds"
+        except Exception as e:
+            return f"Error executing code: {str(e)}"
+
+        return "\n".join([f"{k}:\n{v}" for k, v in result.items()])
+
+
+class DateTimeTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "get_current_datetime",
+            "Get the current date and time",
+            {"type": "object", "properties": {}},
+            "datetime:string"
+        )
+
+    def execute(self, arg: dict) -> str:
+        return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+class RandomNumberTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "generate_random_number",
+            "Generate a random number within a given range",
+            {
+                "type": "object",
+                "properties": {
+                    "min": {"type": "number", "description": "The minimum value"},
+                    "max": {"type": "number", "description": "The maximum value"}
+                }
+            },
+            "random_number:number"
+        )
+
+    def execute(self, arg: dict) -> str:
+        return str(random.uniform(arg["min"], arg["max"]))
+
+
+class RegexTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "regex_match",
+            "Perform a regex match on a given text",
+            {
+                "type": "object",
+                "properties": {
+                    "text": {"type": "string", "description": "The text to search in"},
+                    "pattern": {"type": "string", "description": "The regex pattern to match"}
+                }
+            },
+            "matches:list[string]"
+        )
+
+    def execute(self, arg: dict) -> str:
+        matches = re.findall(arg["pattern"], arg["text"])
+        return json.dumps(matches)
+
+
+class Base64Tool(Tool):
+    def __init__(self):
+        super().__init__(
+            "base64_encode_decode",
+            "Encode or decode a string using Base64",
+            {
+                "type": "object",
+                "properties": {
+                    "action": {"type": "string", "enum": ["encode", "decode"], "description": "Whether to encode or decode"},
+                    "text": {"type": "string", "description": "The text to encode or decode"}
+                }
+            },
+            "result:string"
+        )
+
+    def execute(self, arg: dict) -> str:
+        if arg["action"] == "encode":
+            return base64.b64encode(arg["text"].encode()).decode()
+        elif arg["action"] == "decode":
+            return base64.b64decode(arg["text"].encode()).decode()
+        else:
+            return "Invalid action. Use 'encode' or 'decode'."
+
+
+class SimpleChartTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "generate_simple_chart",
+            "Generate a simple bar chart image",
+            {
+                "type": "object",
+                "properties": {
+                    "data": {"type": "array", "items": {"type": "number"}, "description": "List of numerical values for the chart"},
+                    "labels": {"type": "array", "items": {"type": "string"}, "description": "Labels for each bar"}
+                }
+            },
+            "image_base64:string"
+        )
+
+    def execute(self, arg: dict) -> str:
+        data = arg["data"]
+        labels = arg["labels"]
+        
+        # Create a simple bar chart
+        width, height = 400, 300
+        img = Image.new('RGB', (width, height), color='white')
+        draw = ImageDraw.Draw(img)
+        
+        # Draw bars
+        max_value = max(data)
+        bar_width = width // (len(data) + 1)
+        for i, value in enumerate(data):
+            bar_height = (value / max_value) * (height - 50)
+            left = (i + 1) * bar_width
+            draw.rectangle([left, height - bar_height, left + bar_width, height], fill='blue')
+        
+        # Add labels
+        font = ImageFont.load_default()
+        for i, label in enumerate(labels):
+            left = (i + 1) * bar_width + bar_width // 2
+            draw.text((left, height - 20), label, fill='black', anchor='ms', font=font)
+        
+        # Convert to base64
+        buffered = BytesIO()
+        img.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        return img_str
+
+
+class LLAVAImageAnalysisTool(Tool):
+    def __init__(self):
+        super().__init__(
+            "analyze_image",
+            "Analyze an image using the LLAVA model",
+            {
+                "type": "object",
+                "properties": {
+                    "image_base64": {"type": "string", "description": "Base64 encoded image"},
+                    "question": {"type": "string", "description": "Question about the image"}
+                }
+            },
+            "analysis:string"
+        )
+
+    def execute(self, arg: dict) -> str:
+        try:
+            # Decode base64 image
+            image_data = base64.b64decode(arg["image_base64"])
+            image = Image.open(BytesIO(image_data))
+
+            # Save image to a temporary file
+            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
+                image.save(temp_file, format="PNG")
+                temp_file_path = temp_file.name
+
+            # Call LLAVA model
+            response = ollama.chat(
+                model="llava:7b",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": arg["question"],
+                        "images": [temp_file_path]
+                    }
+                ]
+            )
+
+            # Clean up temporary file
+            os.remove(temp_file_path)
+
+            # Unload LLAVA model
+            ollama.delete("llava:7b")
+
+            return response['message']['content']
+        except Exception as e:
+            return f"Error analyzing image: {str(e)}"