Inläsning av apiEndpoint och useModel har flyttats hit. Funktioner och variabler från frontend.js exporteras numer via frontendApi

smartassist/config/smartassist.yaml

@@ -0,0 +1,38 @@
+# Frontend Configuration
+frontend:
+  url: "http://localhost:5004"
+
+# Backend Configuration
+backend:
+  url: "http://localhost:5004"
+  api: "/api/chat"
+
+# Ollama Server Configuration
+ollama:
+  url: "http://localhost:11434" 
+  api_key: "${OLLAMA_API_KEY}" # Refer to environment variable
+  model: "phi3:mini" # Select a model supported by the Ollama server
+  # model: "llama3:70b" # Select a model supported by the Ollama server
+  # model: "llama3:latest" # Select a model supported by the Ollama server
+  # model: "mannix/llama3-8b-ablitered-v3:latest" # Select a model supported by the Ollama server 
+  # model: "mistral-nemo:latest" # Select a model supported by the Ollama server 
+  # model: "gemma2:27b"
+
+# Logging – comment out the whole section for default level which is INFO
+logging:
+  level: DEBUG  # Set log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+  # level: INFO  # Set log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+
+# Additional Configuration Options (Optional – ignored for now)
+
+# Cache Settings (Optional)
+cache:
+  enabled: true
+  timeout: 60 # Seconds 
+
+test:
+  test_key_1: "ett"
+  test_key_2: "ett"
+  test_key_3: "${THIS_IS_A_DUMMY_VAR}"
+
+another_test: "Syns detta?"

smartassist/requirements_dev.txt

@@ -1,6 +1,10 @@
-# A requirements file for the project. This file specifies
+# A requirements file for the project.
-#  the development environment:
+#
-# - Python 3.8+
+# Run 'pip install -r smartassist/requirements_dev.txt' to install all required packages.
-# - pipenv
+
+
 ollama
 Flask
+flask_cors
+requests
+PyYAML

smartassist/src/backend.py

@@ -0,0 +1,179 @@
+
+# Import the necessary functions from ollama, Flask, requests, threading
+from ollama import Client
+from flask import Flask, request, jsonify, send_from_directory, render_template, session, make_response
+from flask_cors import CORS, cross_origin # CORS stands for Cross-Origin Resource Sharing. This is necessary to allow the frontend to make requests to our backend.
+import requests
+import json
+import logging
+import os
+import utils
+from utils import GlobalState
+
+# Create a logger for this module
+global_state = GlobalState() # Import the singleton that holds global states (e.g., logger)
+logger = global_state.getLogger(__name__)  # Logger for this module, inherit properties of the root logger
+
+
+# Find out the path to current directory according to the Python interpreter (venv)
+logger.debug("Current working directory: %s", os.getcwd())
+
+# Initialize a Flask application
+app = Flask(__name__)
+app.config['STATIC_FOLDER'] = 'static' # Adjust if needed 
+
+# Set the secret key for session management
+secret_key = os.urandom(24)
+app.config['SECRET_KEY'] = secret_key # When do I need this. How is it retained between sessions?
+
+# Optionally set other configuration options
+app.config['SESSION_PERMANENT'] = False  # Session will expire after each request
+app.config['SESSION_TYPE'] = 'filesystem'  # Store sessions on the filesystem
+
+
+logger.debug("flask app template folder: %s", app.template_folder) 
+
+@app.route('/')
+def index():
+    """
+    This route serves index.html to connecting clients
+    """
+
+    session['chat_history'] = []   # The session object (actually, a dictonary) holds the chat session
+    logger.debug("Entering route '/'")
+    # api_endpoint = os.environ['BE_API_ENDPOINT']  # Retrieve the environment variable 
+    api_endpoint = global_state.get_backend_api_ep()  # Retrieve the environment variable 
+    logger.debug("Backend API endpoint: %s", api_endpoint)
+    use_model = global_state.get_llm()
+    with open('smartassist/src/html/client.html', 'r') as f:
+        client_html = f.read()
+    logger.debug("Client HTML (first few characters): %s", client_html[:50]) # Print to see if it's loading
+    # logger.debug("Client HTML (all characters): %s", client_html) # Print to see if it's loading
+
+    return render_template('index.html', api_endpoint=api_endpoint, use_model = use_model, client_content=client_html)
+
+@app.route('/set_session')
+def set_session():
+    resp = make_response()
+    resp.set_cookie('session', 'some-value', samesite='None', secure=True) # Add SameSite attribute here
+    return resp
+
+@app.route('/profile')
+def profile():
+    # Retrieve data from the session
+    user_id = session.get('user_id')
+
+    if user_id:
+        return f'User ID: {user_id}'
+    else:
+        return 'No user ID found'
+    
+
+@app.route('/<path:filename>')
+def serve_static(filename):
+    return send_from_directory(app.config['STATIC_FOLDER'], filename)
+
+
+# CORS(app, resources={
+#     r"/api/chat": {
+#         "origins": "*",
+#         "headers": ["Origin", "Content-Type", "Authorization"],
+#     }
+# })
+
+CORS(app, resources={
+    r"/api/chat": {
+        "origins": "*"    
+    }
+})
+    
+
+@app.route('/api/chat', methods=['POST'])
+def chat(url_server = "http://localhost:11434/api/generate", model = "phi3:mini"):
+    """
+    This function handles the chat. The frontend client (web browser) calls the 
+    backend server through this endpoint (/api/chat) that manage queries
+    to the LLM (Large Language Model) server and it also manages the response 
+    from the LLM server.
+    """
+    # Get the message from the JSON in the request body
+    data = request.get_json()
+    message = data.get('query')
+    url_server = data.get('url_server', url_server)  # Use provided URL or default
+    model = data.get('model', model)  # Use provided model or default
+    
+    # Get chat history from session storage (e.g., a dictionary)
+    chat_history = session.get('chat_history', [])
+    
+    # Add the new message to the chat history
+    chat_history.append({'role': 'user', 'message': message})
+    
+    # Update the session with the new chat history
+    session['chat_history'] = chat_history
+    
+    # Create the data dictionary with chat history
+    data_to_send = {
+        "model": model,
+        'prompt': '\n'.join([f"{item['role']}: {item['message']}" for item in chat_history]),
+        "stream": False           
+    }
+    
+    try:
+        url = url_server
+        headers = {
+                    "Content-Type": "application/json",
+        }
+        
+        response = requests.post(url,
+                                 headers=headers, 
+                                 data=json.dumps(data_to_send))
+        response.raise_for_status()  # Raise an exception for bad status codes
+        llm_response = response.json()['response']   # Assuming the LLM's response is under 'response' key
+        chat_history.append({'role': 'assistant', 'message': llm_response})  # Add assistant response to chat history
+        logger.debug(f"Chat History: {chat_history}")
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        logger.error("Request Exception: %s", str(e))        
+        return jsonify({'error': 'Failed to process request'}), 500
+    except json.JSONDecodeError as e:
+        logger.error("JSON Decode Error: %s", str(e))   # Corresponds to print(f"JSON Decode Error: {e}")             
+        return jsonify({'error': 'Invalid JSON response from server'}), 500
+
+
+
+@app.route('/smartassist', methods=["POST"])
+def smartassist():
+    # Extract the query from the incoming JSON data
+    data = request.json
+    user_query = data['query']
+
+    # Get the response from the OLLAMA API based on the user's query
+    # NOTE: Should we append message history here? Maybe interact with SQLlite?
+    response = get_response(user_query)
+
+    # Return the response as a JSON object in the HTTP response
+    return jsonify({"response": response})
+
+def get_response(user_query):
+    client = Client()   # Create a client object for interacting with OLLAMA API
+    response = client.generate_response(user_query)   # Generate and retrieve the response based on user's query
+    return response     
+
+
+def run_flask(fport=5005):
+    """
+    Starts the Flask server
+    """
+    # Flask endpoint for user interaction
+    logger.debug("Entering run_flask()")
+    # app.run(port = str(str(fport)), debug=False)
+    app.run(port = str(str(fport)), debug=True)
+    # app.run(port=5000, debug=True, use_reloader=False)
+    logger.debug("Exiting run_flask()")
+
+
+if __name__ == '__main__':
+    # Run the Flask application
+    run_flask()
+
+

smartassist/src/html/client.html

@@ -0,0 +1,88 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Ollama Chat</title>
+    <link rel="stylesheet" href="/css/clientstyle.css"> 
+    <!-- <link rel="stylesheet" href="python_test/smartassist/src/css/clientstyle.css">  -->
+</head>
+<body>
+    <h1>Ollama Chat</h1>
+    <div id="chatbox">
+        <!-- messages will be rendered here -->
+    </div>
+    <textarea id="userInput" placeholder="Type your message..." rows="5"></textarea>
+    <button id="sendButton" onclick="sendMessage()">Send</button>
+
+    <!-- Get the apiEndpoint and the useModel -->
+    <!-- <script>
+        const apiEndpoint = window.apiEndpoint;
+        const useModel = window.useModel;
+        console.log("client.html - API Endpoint: ", apiEndpoint);
+        console.log("client.html - use model: ", useModel);
+    </script> -->
+
+    <!-- <script>
+        let apiEndpoint; // Make variable available outside of the scope of the event listener
+        let useModel; // Make variable available outside of the scope of the event listener
+        window.addEventListener('message', function(event) {
+            if (event.origin === 'http://localhost:5004') { // Make sure this matches your origin
+                const { apiEndpoint, useModel } = event.data;
+                console.log("client.html - API Endpoint: ", apiEndpoint);
+                console.log("client.html - use model: ", useModel);
+                window.apiEndpoint  = apiEndpoint; 
+                window.useModel  = useModel;
+            }   
+        });
+    </script> -->
+
+    <!-- Marked-it for markdown rendering -->
+    <script src="https://cdn.jsdelivr.net/npm/markdown-it@14.1.0/dist/markdown-it.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/markdown-it@14/dist/markdown-it.min.js"></script>
+
+
+
+    <!-- Include MathJax library to render mathematical notation -->
+    <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script>
+        window.MathJax = {
+          loader: { load: ['input/tex', 'output/chtml'] },
+          tex: {
+            packages: ['base', 'ams'],
+            inlineMath: [['$', '$']]
+          }
+        };
+    </script>
+
+
+
+
+    <script>
+        const chatContainer = document.getElementById('chatbox');
+
+        // Handle resize events
+        window.addEventListener('resize', 
+            function() {
+                chatContainer.style.height = 'auto'; 
+        });
+
+        const userInputElement = document.getElementById('userInput');
+
+        userInputElement.addEventListener('keydown', function(event) {
+            if (event.shiftKey && event.key === 'Enter') { // Shift+Enter for newline
+                event.preventDefault(); 
+                userInputElement.value += '\n';
+            } else if (event.key === 'Enter') {  // Enter to send message
+                window.frontendApi.sendMessage();
+                userInputElement.value = ''; // Clear the input field after sending
+                event.preventDefault();
+            }
+        });
+
+    </script>
+
+    <!-- Get the javascript handling communication with the backend -->
+    <script src="/js/frontend.js"></script>
+</body>
+</html>
+

smartassist/src/ollama_interact.py

@@ -1,35 +0,0 @@
-
-# Import the necessary library from ollama module
-from ollama import Client
-
-def get_response(user_query):
-  # Create a client object for interacting with OLLAMA API
-    client = Client()
-
-    # Generate and retrieve the response based on user's query
-    response = client.generate_response(user_query)
-
-    # Return the generated response
-    return response
-
-# Flask endpoint for user interaction
-from flask import Flask, request, jsonify
-
-# Initialize a Flask application
-app = Flask(__name__)
-
-@app.route('/smartassist', methods=['POST'])
-def smartassist():
-    # Extract the query from the incoming JSON data
-    data = request.json
-    user_query = data['query']
-
-    # Get the response from the OLLAMA API based on the user's query
-    response = get_response(user_query)
-
-    # Return the response as a JSON object in the HTTP response
-    return jsonify({"response": response})
-
-# Run the Flask application if this script is executed directly
-if __name__ == '__main__':
-    app.run(debug=True)

smartassist/src/startservices.py

@@ -0,0 +1,121 @@
+# Start all services
+import subprocess
+import os
+import yaml
+import json
+import socket
+import urllib.parse
+from backend import run_flask
+import logging
+import utils
+from utils import GlobalState
+
+global_state = GlobalState() # Configure root logger. The level will be adjusted later based on config file
+logger = global_state.getLogger(__name__)  # Logger for this module, inherit properties of the root logger
+
+def configure():
+    """
+    Reads YAML configruation file into dictionary, parse it and fill all referenceed
+    environment variables with their values.
+    """
+    ####################################
+    # Read YAML config
+    ####################################
+    # Load configuration file that defines parameters for services
+    with open('./smartassist/config/smartassist.yaml') as f:
+        config = yaml.safe_load(f)
+    
+    def resolve_env_var(value):
+        if isinstance(value, str) and value.startswith("${") and value.endswith("}"):
+            env_var_name = value[2:-1]  # Extract name between ${}
+            return os.getenv(env_var_name, None)
+        return value
+
+    def update_dict_with_env_vars(d):
+        for key in d:
+            if isinstance(d[key], dict):
+                update_dict_with_env_vars(d[key])  # Recursively check nested dictionaries
+            elif isinstance(d[key], str):
+                d[key] = resolve_env_var(d[key])
+        return d
+    
+    # Update the config dictionary with resolved environment variables
+    updated_config = update_dict_with_env_vars(config)
+ 
+    ####################################
+    # Extract global logging level
+    ####################################
+    if isinstance(updated_config.get('logging'), dict): # Look for 'logging' key in config file
+        logging_config = updated_config['logging'] 
+        if isinstance(logging_config.get('level'), str): # Set to value of the yaml file if specified
+            global_state.set_log_level(logging_config['level'])
+            logger.debug("configure(): This logger now has effective log level %s", logger.getEffectiveLevel())
+
+    ####################################
+    # Extract and export backend API
+    # endpoint as global state variable
+    ####################################
+    if isinstance(updated_config.get('backend'), dict): # Look for 'backend' key
+        if isinstance(updated_config['backend'].get('url'), str): # Look for 'url' key
+            url = updated_config['backend'].get('url')
+            if isinstance(updated_config['backend'].get('api'), str): # Look for 'api' key
+                api = updated_config['backend'].get('api')
+                # backend_api_ep = url+api # Extract API endpoint if defined
+                logger.debug(f"Constructing endpoint address as url+api: {url+api}")
+                global_state.set_backend_api_ep(url+api) # Extract API endpoint if defined and set in global_state 
+                logger.debug(f"Backend API endpoint is set to {global_state.get_backend_api_ep()}")
+    # os.environ['BE_API_ENDPOINT'] = backend_api_ep # Look into alternative way to share this with backend.py
+
+    ####################################
+    # Extract Ollama parameters (url, api_key, model)
+    ####################################
+    if isinstance(updated_config.get('ollama'), dict): # Look for 'ollama' key
+        if isinstance(updated_config['ollama'].get('model'), str): # Look for 'model' key
+            model_to_use = updated_config['ollama'].get('model')
+            global_state.set_llm(model_to_use)
+            logger.debug("configure(): LLM is set to: %s",global_state.get_llm())
+
+    return updated_config
+
+
+# def start_frontend(config):
+#     parsed_url = urllib.parse.urlparse(config['frontend']['url'])
+#     hostname = parsed_url.netloc.split(':')[0]  # Split by ':' and take the first part, i.e., 'localhost', IP, or domain name
+#     port = parsed_url.port  # This is the server port
+
+#     # Use the socket module in Python to check whether a port is in use, 
+#     # which would indicate that a server is already running on that port. 
+#     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+#         try:
+#             s.bind((hostname, port))
+#             logger.debug("No server is running on %s -— starting one.", parsed_url.netloc)
+#             # Start frontend (web server) as a separate process
+#             subprocess.Popen(["python", "-m", "http.server", str(port)])
+#         except socket.error as e:
+#             if e.errno == 48:
+#                 logger.debug("A server is already running on %s -— will use this.", parsed_url.netloc)
+#             else:
+#                 raise  # Unexpected error, re-raise it so we can see the traceback
+#         except Exception as e:
+#             logger.error("Failed to start frontend: %s", str(e)) # Corresponds to print(f"Failed to start frontend: {e}")
+            
+
+
+def start_backend(config):
+    parsed_url = urllib.parse.urlparse(config['backend']['url'])
+    # hostname = parsed_url.netloc.split(':')[0]  # Split by ':' and take the first part, i.e., 'localhost', IP, or domain name
+    port = parsed_url.port  # This is the server port
+    logger.debug('Backend parsed url set to {}'.format(parsed_url))
+    logger.debug('Backend port set to {}'.format(port))
+
+    try:
+        run_flask(fport = port)
+    except Exception as e:
+        logger.error("Failed to start backend: %s", str(e)) # Corresponds to print(f"Failed to start backend: {e}")
+
+if __name__ == '__main__':
+    conf = configure() # Read config from file and set up config dict
+    logger.debug('conf dictionary set to {}'.format(json.dumps(conf, indent=4)))
+    # start_frontend(config=conf) # Not needed as we are using Flask for backend now
+    start_backend(config=conf)
+

smartassist/src/static/css/clientstyle.css

@@ -0,0 +1,85 @@
+body {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background-color: #f4f4f4;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    min-height: 100vh;
+    margin: 0;
+}
+
+h1 {
+    color: #333;
+    margin-bottom: 20px;
+}
+
+#chatbox {
+    width: calc(50% - 60px); /* Adjust width for input and button */
+    /* max-width: 500px; */
+    height: 600px;
+    /* background-color: #fff8bc; */
+    background-color: #ffffff;
+    border-radius: 10px;
+    padding: 20px;
+    box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+    overflow: auto; /* Allow horizontal and vertical scrolling of the chatbox */
+    resize: both; /* Allow resizing vertically */
+    border: 1px solid #ccc; /* Add a thin grey border around chatbox */
+    margin-bottom: 20px; /* Add some space between chatbox and userInput */
+}
+
+.message {
+    margin-bottom: 15px;
+}
+
+.user-message {
+    background-color: #9cc1ecbb;
+    padding: 10px 15px;
+    border-radius: 10px;
+    text-align: left; /* Align user messages to the left */
+}
+
+.ai-response {
+    /* background-color: #f0f8ff; */
+    background-color: #f5ecd0;
+    padding: 10px 15px;
+    border-radius: 10px;
+    text-align: left; /* Align AI responses to the left */
+}
+
+#userInput {
+    width: calc(50% - 60px); /* Adjust width for input and button */
+    padding: 10px;
+    border: 1px solid #ccc;
+    border-radius: 5px;
+    margin-bottom: 10px;
+    font-family: 'Courier New', Courier, monospace; /* Fixed width typeface */
+}
+
+#userInput:focus {
+    outline: none;
+    border-color: #66afe9; /* Blue outline on focus */
+}
+
+button[onclick="sendMessage()"] {
+    background-color: #4CAF50; /* Green */
+    border: none;
+    color: white;
+    padding: 10px 20px;
+    text-align: center;
+    text-decoration: none;
+    display: inline-block;
+    font-size: 16px;
+    border-radius: 5px;
+    cursor: pointer;
+    transition: background-color 0.3s; /* Smooth transition effect */
+}
+
+button[onclick="sendMessage()"]:hover {
+    background-color: #b2b2b2; /* Light Grey on hover */
+}
+
+button[onclick="sendMessage()"]:active {
+    background-color: #6f6f6f; /* Dark Grey when clicked */
+}
+

smartassist/src/static/js/frontend.js

@@ -0,0 +1,192 @@
+
+// // Get the user input element from the DOM
+// const chatbox = document.getElementById('chatbox');
+// const userInput = document.getElementById('userInput');
+
+// const parser = window.markdownit({
+//   linkify: true,
+//   strikethrough: true,
+// });
+
+// parser.enable(['table']);
+
+// // const apiEndpoint  = window.apiEndpoint;  // Get the API endpoint
+// // const useModel  = window.useModel;      // Get whether to use a model or not
+// // console.log("frontend.js - API Endpoint:  ", window.apiEndpoint);
+// // console.log("frontend.js - Use model:  ", window.useModel);
+
+// let apiEndpoint; // Make variable available outside of the scope of the event listener
+// let useModel; // Make variable available outside of the scope of the event listener
+// window.addEventListener('message', function(event) {
+//     if (event.origin === 'http://localhost:5004') { // Make sure this matches your origin
+//         const { apiEndpoint, useModel } = event.data;
+//         console.log("client.html - API Endpoint: ", apiEndpoint);
+//         console.log("client.html - use model: ", useModel);
+//         window.apiEndpoint  = apiEndpoint; 
+//         window.useModel  = useModel;
+//     }   
+// });
+
+// console.log("frontend.js  - API Endpoint:  ", window.apiEndpoint);
+// console.log("frontend.js  - Use model:  ", window.useModel);
+
+// // Define a function to send the user's message to the AI
+// function sendMessage() {
+//   // Get the user's input message and trim any whitespace
+//     const query = userInput.value.trim();
+
+//     // Check if the message is not empty
+//     if (query !== '') {
+ 
+//         // fetch(`${apiEndpoint}`, {
+//         fetch(apiEndpoint, {
+//             method: 'POST',
+//             headers: { 'Content-Type': 'application/json' },
+//             body: JSON.stringify({ query, model: useModel }), // Add these parameters here
+//             // body: JSON.stringify({ query, url_server: "http://your-custom-url", model: "phi3:mini" }), // Add these parameters here
+//         })
+//         .then(response => response.json())
+//         .then(data => {
+//             // Get the AI's response from the API data
+//             const aiResponse = data.response;
+
+//             // Render the user's original message in the chatbox
+//             renderMessage(query, 'user-message');
+
+//             // Render the AI's response in the chatbox
+//             renderMessage(aiResponse, 'ai-response');
+
+//             // Clear the user input field for the next message
+//             userInput.value = '';
+//         })
+//         .catch(error => console.error('Error sending message:', error));
+//     }
+// }
+
+
+
+// // Define a function to render a message in the chatbox with a specific class name
+// function renderMessage(text, className) {
+//     // Create a new div element to hold the message
+//     const messageElement = document.createElement('div');
+
+//     // Add the specified class name to the element
+//     messageElement.className = className;
+
+//     // // Set the text content of the element to the message text
+//     // messageElement.textContent = text;
+
+//     // Use the markdown-it parser
+//     const html = parser.render(text);
+//     messageElement.innerHTML = html;
+
+//     // Append the message element to the chatbox immediately
+//     // chatbox.appendChild(messageElement);
+
+//     // Typeset math in the message element
+//     MathJax.typesetPromise([messageElement]).then(() => {
+//         // No need to append anything here, it's already appended above
+//         chatbox.appendChild(messageElement);
+
+//     });
+// }
+
+// // Make the button toggle colour when user presses Enter on keyboard
+// const sendButton = document.getElementById('sendButton');
+
+// document.addEventListener('keydown', function(event) {
+//     if (event.key === 'Enter') {
+//         sendButton.style.backgroundColor = '#6f6f6f'; // Dark Grey when Enter is pressed
+//     }
+// });
+
+// document.addEventListener('keyup', function() {
+//     sendButton.style.backgroundColor = ''; // Restore the original style when any key is released
+// });
+
+
+
+// Get the user input element from the DOM
+const chatbox = document.getElementById('chatbox');
+const userInput = document.getElementById('userInput');
+const parser = window.markdownit({
+  linkify: true,
+  strikethrough: true,
+});
+parser.enable(['table']);
+
+let apiEndpoint; // Make variable available outside of the scope of the event listener
+let useModel; // Make variable available outside of the scope of the event listener
+
+const frontendApi = {
+  // Define a function to send the user's message to the AI
+  sendMessage: function() {
+    if (!window.apiEndpoint || !window.useModel) { // Check if we're ready before proceeding
+      console.error("Not ready yet. Please wait for apiEndpoint and useModel to be set.");
+      return;
+    }
+    
+    // Get the user's input message and trim any whitespace
+    const query = userInput.value.trim();
+    // Check if the message is not empty
+    if (query !== '') {
+      fetch(window.apiEndpoint, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ query, model: window.useModel }), // Add these parameters here
+      })
+      .then(response => response.json())
+      .then(data => {
+        // Get the AI's response from the API data
+        const aiResponse = data.response;
+        // Render the user's original message in the chatbox
+        this.renderMessage(query, 'user-message');
+        // Render the AI's response in the chatbox
+        this.renderMessage(aiResponse, 'ai-response');
+        // Clear the user input field for the next message
+        userInput.value = '';
+      })
+      .catch(error => console.error('Error sending message:', error));
+    }
+  },
+  // Define a function to render a message in the chatbox with a specific class name
+  renderMessage: function(text, className) {
+    // Create a new div element to hold the message
+    const messageElement = document.createElement('div');
+    // Add the specified class name to the element
+    messageElement.className = className;
+    // Use the markdown-it parser
+    const html = parser.render(text);
+    messageElement.innerHTML = html;
+    // Append the message element to the chatbox immediately
+    chatbox.appendChild(messageElement);
+  },
+};
+
+// Wait for the event listener to set apiEndpoint and useModel
+window.addEventListener('message', function(event) {
+  if (event.origin === 'http://localhost:5004') { // Make sure this matches your origin
+    const { apiEndpoint, useModel } = event.data;
+    console.log("fronend.js - API Endpoint: ", apiEndpoint);
+    console.log("fronend.js - use model: ", useModel);
+    window.apiEndpoint = apiEndpoint;
+    window.useModel = useModel;
+  }
+});
+
+// Wait for the DOM to be fully loaded before making the API available
+document.addEventListener('DOMContentLoaded', function() {
+  window.frontendApi = frontendApi;
+});
+
+
+// Make the button toggle colour when user presses Enter on keyboard
+const sendButton = document.getElementById('sendButton');
+document.addEventListener('keydown', function(event) {
+  if (event.key === 'Enter') {
+    sendButton.style.backgroundColor = '#6f6f6f'; // Dark Grey when Enter is pressed
+  }
+});
+document.addEventListener('keyup', function() {
+  sendButton.style.backgroundColor = ''; // Restore the original style when any key is released
+});

smartassist/src/templates/index.html

@@ -0,0 +1,54 @@
+<!DOCTYPE html>
+<html>
+<head>
+<!-- <title>Frontend</title> -->
+</head>
+<body>
+
+
+    <!-- This iframe will hold the content from client.html -->
+    <!-- Passing the API endpoint as a query parameter to the srcdoc attribute -->
+    <!-- srcdoc="{{ client_content }}?apiEndpoint={{ api_endpoint }}/"> -->
+
+    <iframe id="client-frame" 
+            style="width: 100%; height: 100vh;" 
+            srcdoc="{{ client_content }}">
+    </iframe>
+
+    <!-- <script>
+        // Extract apiEndpoint for use in your frontend code...
+        const apiEndpoint = '{{ api_endpoint }}';  // Templating syntax (Jinja2)
+        const useModel = '{{ use_model }}';  // Templating syntax (Jinja2)
+        // Tell the iframe about the apiEndpoint
+        document.getElementById('client-frame').contentWindow.apiEndpoint = apiEndpoint;
+        document.getElementById('client-frame').contentWindow.useModel = useModel;
+        console.log("index.html - API Endpoint: ", apiEndpoint);
+        console.log("index.html - use model: ", useModel);
+    </script> -->
+
+    <script>
+        // Extract apiEndpoint for use in frontend.js
+        const apiEndpoint = '{{ api_endpoint }}';  // Templating syntax (Jinja2)
+        const useModel = '{{ use_model }}';  // Templating syntax (Jinja2)
+        window.addEventListener('load', function() {
+            const clientFrame = document.getElementById('client-frame').contentWindow;
+            clientFrame.postMessage({ apiEndpoint, useModel }, '*'); // Send the data to the iframe
+        });
+        console.log("index.html - API Endpoint: ", apiEndpoint);
+        console.log("index.html - use model: ", useModel);
+    </script>
+
+    <!-- Responsive scaling and some padding -->
+    <script>
+        const clientFrame = document.getElementById('client-frame');
+      
+        function resizeIframe() {
+            clientFrame.style.height = window.innerHeight - 50 + 'px'; // Adjust the subtraction for padding/margins if needed
+        }
+      
+        window.addEventListener('resize', resizeIframe);
+        resizeIframe(); // Call it once on page load
+    </script>
+
+</body>
+</html>

smartassist/src/utils.py

@@ -0,0 +1,75 @@
+# This module contains definitions of variables, functions, classes, et cetera, that are
+# imported to more than one other module. The rational for defining these things here
+# is that it is easier to avoid circular imports when they are defined in a central location.
+import logging
+
+class GlobalState:
+    """
+     This class holds various variables and methods which are accessible across 
+     different modules in the Python project using the Singleton design pattern. 
+     This ensures that only one instance of the class is created and shared among 
+     all modules, preventing circular imports and providing a centralized location 
+     for managing shared resources.
+    """
+    _instance = None  # Private class attribute to hold the single instance of the class
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super(GlobalState, cls).__new__(cls)
+            cls._instance.log_level = 'INFO'  # Default logging level
+            cls._instance.logger = logging.getLogger() # Get root logger for the caller module
+            handler = logging.StreamHandler() # Or other handler (FileHandler for logs to file)
+            formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+            handler.setFormatter(formatter)
+            cls._instance.logger.addHandler(handler)
+            cls._instance.logger.setLevel(getattr(logging, cls._instance.log_level))  # Initialize root logger level
+            cls._instance.logger.info(" __new__(cls): Logger in GlobalState created: %s", cls._instance.logger)
+            cls._instance.llm = "phi3:mini" # Default LLM for queries. TODO: Check with ollama server that it actually exists
+            cls._instance.backend_api_ep = "http://localhost:5005/api/chat" # Default backend API endpoint
+        return cls._instance
+
+    def configure_logging(self, level=None):
+        """Set up logging for the project."""
+        if level is None:
+            level = self.log_level
+        # numeric_level = getattr(logging, level.upper())  # Convert string to numeric level
+        numeric_level = getattr(logging, level.upper())  # Convert string to numeric level
+        self.logger.setLevel(numeric_level)
+        self.logger.debug(f"utils.py -- configure_logging(): effective log level is {level} which is {self.logger.getEffectiveLevel()}")
+
+    def set_log_level(self, level = 'INFO'):
+        """Set the logging level."""
+        self.log_level = level
+        self.configure_logging()
+
+    def get_log_level(self):
+        """Getter for log_level attribute."""
+        return self.log_level
+    
+    def get_effective_log_level(self):
+        """Getter for effective log level of loggerattribute."""
+        return self.logger.getEffectiveLevel()
+    
+    def getLogger(self, module_name = None):
+        """Return a logger based on the module name."""
+        if module_name is None:
+            module_name = __name__
+        logger = logging.getLogger(module_name)
+        return logger
+    
+    def set_llm(self, model_name="phi3:mini"):
+        """Set LLM for queries"""
+        self.llm = model_name
+
+    def get_llm(self):
+        """Getter for which LLM is used for queries"""
+        return self.llm
+    
+    def set_backend_api_ep(self, be_api_ep=None):
+        """Set backend API endpoint"""
+        self.backend_api_ep = be_api_ep
+
+    def get_backend_api_ep(self):
+        """Getter for backend API endpoint"""
+        return self.backend_api_ep
+