Merge pull request #2 from CodeRifle/feat/llm-chat-integration

Integrate Language Model for chat functionality

Author: CodeRifle

index.html

@@ -116,6 +116,30 @@ <h2>Get In Touch</h2>
             </div>
         </footer>
     </div>
+    <!-- Assuming Google Gemini SDK. Replace with the correct SDK if different -->
+    <script type="module">
+        // This is a placeholder. The actual LanguageModel library (e.g., from a CDN)
+        // would make `LanguageModel` available globally or as a module.
+        // For demonstration, we'll mock a simple version if it's not already defined.
+        if (typeof window.LanguageModel === 'undefined') {
+            console.log("Mocking LanguageModel for development purposes.");
+            window.LanguageModel = {
+                create: async () => {
+                    console.log("Mock LanguageModel.create() called");
+                    return {
+                        prompt: async (message) => {
+                            console.log(`Mock session.prompt() called with: "${message}"`);
+                            await new Promise(resolve => setTimeout(resolve, 1000)); // Simulate network delay
+                            if (message.toLowerCase().includes("error test")) {
+                                throw new Error("Simulated LLM error.");
+                            }
+                            return `Mock response to: "${message}"`;
+                        }
+                    };
+                }
+            };
+        }
+    </script>
     <script src="script.js"></script>
 </body>
 </html>

script.js

@@ -1,27 +1,53 @@
-const responses = {
-    'who is rishabh gupta?': `Rishabh Gupta is an accomplished AI Engineer with expertise in developing and implementing cutting-edge artificial intelligence solutions. He has a strong background in machine learning, deep learning, and natural language processing, with a proven track record of delivering innovative AI-powered applications.`,
-
-    'what are his skills?': `Rishabh's technical skills include:
-    • Deep Learning & Machine Learning (PyTorch, TensorFlow)
-    • Natural Language Processing
-    • Computer Vision
-    • Large Language Models (LLMs)
-    • Python, JavaScript, and various AI/ML frameworks
-    • Cloud platforms (AWS, GCP)`,
-
-    'show me his projects': `Here are some of Rishabh's notable projects:
-    1. Developed an advanced NLP system for automated customer support
-    2. Created a computer vision solution for real-time object detection
-    3. Implemented a recommendation engine using deep learning
-    4. Built scalable ML pipelines for production environments`,
-
-    'default': `I'm not sure about that specific query, but I'd be happy to tell you about Rishabh's experience, skills, or projects. Feel free to ask about those topics!`
-};
+// const responses = {
+//     'who is rishabh gupta?': `Rishabh Gupta is an accomplished AI Engineer with expertise in developing and implementing cutting-edge artificial intelligence solutions. He has a strong background in machine learning, deep learning, and natural language processing, with a proven track record of delivering innovative AI-powered applications.`,
+//
+//     'what are his skills?': `Rishabh's technical skills include:
+//     • Deep Learning & Machine Learning (PyTorch, TensorFlow)
+//     • Natural Language Processing
+//     • Computer Vision
+//     • Large Language Models (LLMs)
+//     • Python, JavaScript, and various AI/ML frameworks
+//     • Cloud platforms (AWS, GCP)`,
+//
+//     'show me his projects': `Here are some of Rishabh's notable projects:
+//     1. Developed an advanced NLP system for automated customer support
+//     2. Created a computer vision solution for real-time object detection
+//     3. Implemented a recommendation engine using deep learning
+//     4. Built scalable ML pipelines for production environments`,
+//
+//     'default': `I'm not sure about that specific query, but I'd be happy to tell you about Rishabh's experience, skills, or projects. Feel free to ask about those topics!`
+// };
 
 const chatMessages = document.querySelector('.chat-messages');
 const userInput = document.getElementById('userInput');
 const typingIndicator = document.querySelector('.typing-indicator');
 
+// Variable to hold the LanguageModel session
+let modelSession = null;
+
+// Initialize the LanguageModel session
+async function initializeSession() {
+    try {
+        // Assuming LanguageModel is available globally e.g. via a <script> tag
+        if (window.LanguageModel && typeof window.LanguageModel.create === 'function') {
+            modelSession = await window.LanguageModel.create();
+            console.log("LanguageModel session initialized.");
+        } else {
+            console.error("LanguageModel library not found or 'create' method is missing.");
+            addMessage("Error: Chat functionality is currently unavailable. LanguageModel library not found.", false);
+            typingIndicator.style.display = 'none';
+        }
+    } catch (error) {
+        console.error("Error initializing LanguageModel session:", error);
+        addMessage("Error: Could not initialize the chat assistant. Please try again later.", false);
+        typingIndicator.style.display = 'none';
+    }
+}
+
+// Call initializeSession when the script loads
+initializeSession();
+
+
 function addMessage(message, isUser = false) {
     const messageDiv = document.createElement('div');
     messageDiv.className = `message ${isUser ? 'user-message' : 'bot-message'}`;
@@ -37,25 +63,39 @@ function addMessage(message, isUser = false) {
 
 function simulateTyping() {
     typingIndicator.style.display = 'block';
-    return new Promise(resolve => setTimeout(resolve, Math.random() * 1000 + 1000));
+    // We don't need a fixed timeout anymore as we wait for the actual LLM response
 }
 
-async function getResponse(message) {
-    const normalizedMessage = message.toLowerCase().trim();
-    await simulateTyping();
-    typingIndicator.style.display = 'none';
-    return responses[normalizedMessage] || responses['default'];
-}
+// async function getResponse(message) {
+//     const normalizedMessage = message.toLowerCase().trim();
+//     await simulateTyping();
+//     typingIndicator.style.display = 'none';
+//     return responses[normalizedMessage] || responses['default'];
+// }
 
 async function sendMessage() {
     const message = userInput.value.trim();
     if (!message) return;
 
     addMessage(message, true);
     userInput.value = '';
+    simulateTyping(); // Show typing indicator
 
-    const response = await getResponse(message);
-    addMessage(response);
+    if (!modelSession) {
+        addMessage("Chat assistant is not available. Session not initialized.", false);
+        typingIndicator.style.display = 'none';
+        return;
+    }
+
+    try {
+        const result = await modelSession.prompt(message);
+        addMessage(result);
+    } catch (error) {
+        console.error("Error getting response from LanguageModel:", error);
+        addMessage("Sorry, I encountered an error trying to respond. Please try again.", false);
+    } finally {
+        typingIndicator.style.display = 'none'; // Hide typing indicator
+    }
 }
 
 function usePrompt(prompt) {