Copy files from Github to HF spaces

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+node_modules

Dockerfile

@@ -0,0 +1,38 @@
+# Use Node for frontend + backend
+FROM node:18
+
+# Install Python (HF already uses Debian-based images, so this works)
+RUN apt-get update && apt-get install -y python3 python3-pip
+
+# Create user
+RUN useradd -m -u 1000 user
+USER user
+WORKDIR /home/user/app
+
+# Copy package files first (cache-friendly)
+COPY --chown=user package.json package-lock.json ./
+
+# Install Node dependencies
+RUN npm install
+
+# Copy the rest of the repo
+COPY --chown=user . .
+
+# Build Vite frontend
+RUN cd frontend && npm install && npm run build
+
+# Move built frontend to backend's static folder
+RUN cp -r frontend/dist backend/dist
+
+# Install Python deps if any
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+RUN python -m nltk.downloader punkt
+RUN python -m nltk.downloader punkt_tab
+
+# Expose HF port
+EXPOSE 7860
+
+# Start your Node backend
+CMD ["node", "backend/server.js"]

README.md

@@ -1,12 +1,50 @@
+# Semantic Intent Routing Engine
+
+A lightweight, fast, and fully deterministic alternative to traditional intent-classification systems.  
+Built with **React + Vite + Tailwind** on the frontend and a **Python-based semantic inference engine** on the backend.
+
+This system routes user queries through a graph of intents using sentence embeddings, adaptive confidence logic, and retrieval-based fallbacks—no classifiers, training loops, or model deployments required.
+
+---
+
+## Features
+
+### **Deterministic Intent Resolution**
+Resolves user queries by traversing a DAG-structured intent graph with path-level scoring instead of a single classifier.
+
+### **Adaptive Confidence Thresholding**
+Automatically adjusts sensitivity based on query length and phrasing, improving routing stability on ambiguous inputs.
+
+### **Retrieval-Augmented Fallbacks**
+When a query doesn’t clearly match any intent, the system fetches semantically similar candidates and recovers gracefully.
+
+### **Multi-Turn Context Handling**
+Maintains conversational context so follow-up questions like “same as before” or “for that” route correctly without repeating selections.
+
+### **Hot-Swappable Intent Graph**
+Intents are defined in JSON and automatically converted into a navigable graph.  
+Updates apply instantly—no retraining or redeployment required.
+
+### **Fast and Lightweight**
+Runs entirely on CPU and maintains **sub-15ms routing latency** thanks to caching and optimized traversal.
+
 ---
-title: Semantic Analyzer
-emoji: 🔥
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
-short_description: A lightweight, fast, and fully deterministic semantic engine
+
+## Architecture Overview
+
+- **Frontend:** React + Vite + Tailwind interface for entering queries and testing the routing behavior.  
+- **Backend:** Python engine using sentence-transformer embeddings and deterministic traversal logic.  
+- **Intent Graph:** JSON-defined structure supporting multi-parent nodes, examples, responses, and metadata.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Why This Exists
+
+Most NLU systems rely on classifiers or fine-tuned models, which brings problems like:
+
+- retraining loops  
+- model drift  
+- slow iteration cycles  
+- low explainability  
+
+This project avoids all of that by using semantic similarity, graph traversal, and context tracking to produce stable and predictable routing—even as intents change.

backend/models/message.js

@@ -0,0 +1,10 @@
+import mongoose from 'mongoose';
+
+const messageSchema = new mongoose.Schema({
+  role: { type: String, enum: ['user', 'bot', 'image'], required: true },
+  text: { type: String },
+  src: { type: String },
+  timestamp: { type: Date, default: Date.now }
+});
+
+export default mongoose.model('Message', messageSchema);

backend/server.js

@@ -0,0 +1,109 @@
+import express from "express";
+import cors from "cors";
+import { spawn } from "child_process";
+import path from "path";
+import mongoose from 'mongoose';
+import Message from './models/message.js'; // adjust path if needed
+
+const app = express();
+app.use(cors());
+app.use(express.json());
+
+// Start Python engine once
+const python = spawn("/home/dwarakesh/base/bin/python3", [
+  path.join(process.cwd(), "../python_engine/engine_server.py")
+]);
+
+let ready = false;
+
+// Read stderr for logs + ready signal
+python.stderr.on("data", (data) => {
+  const msg = data.toString();
+  console.log("[PY]", msg.trim());
+  if (msg.includes("READY")) ready = true;
+});
+
+// Tiny queue to handle async replies
+let callbacks = [];
+python.stdout.on("data", (data) => {
+  const lines = data.toString().trim().split("\n");
+  for (const line of lines) {
+    const cb = callbacks.shift();
+    if (cb) cb(JSON.parse(line));
+  }
+});
+
+// Helper to send request to python
+function askPython(query) {
+  return new Promise((resolve, reject) => {
+    if (!ready) return reject("Python engine not ready");
+
+    callbacks.push(resolve);
+    python.stdin.write(JSON.stringify({ query }) + "\n");
+  });
+}
+
+// connect to mongo
+mongoose.connect("mongodb://127.0.0.1:27017/semantic_chat")
+  .then(() => console.log("MongoDB connected"))
+  .catch((err) => console.error("MongoDB error:", err));
+
+
+// API endpoint
+app.post("/ask", async (req, res) => {
+  try {
+    const userQuery = req.body.query;
+
+    // store user message
+    await Message.create({
+      role: "user",
+      text: userQuery
+    });
+
+    const result = await askPython(userQuery);
+
+    // store bot messages
+    if (Array.isArray(result.response)) {
+      for (const msg of result.response) {
+        await Message.create({
+          role: "bot",
+          text: msg
+        });
+      }
+    }
+
+    res.json(result);
+
+  } catch (err) {
+    console.error("Error in /ask:", err);
+    res.status(500).json({ error: err.toString() });
+  }
+});
+
+app.get("/history", async (req, res) => {
+  const messages = await Message.find().sort({ timestamp: 1 });
+  res.json(messages);
+});
+
+// Clear chat history endpoint
+app.post("/clear", async (req, res) => {
+  try {
+    // Delete all messages from database
+    await Message.deleteMany({});
+    
+    // Create new initial greeting message
+    const initialMessage = await Message.create({
+      role: "bot",
+      text: "Hi. I am Dwarakesh. Ask me anything."
+    });
+
+    res.json({ success: true, message: initialMessage });
+  } catch (err) {
+    console.error("Error clearing chat:", err);
+    res.status(500).json({ error: err.toString() });
+  }
+});
+
+app.listen(8000, () =>
+  console.log("Node server running on http://localhost:8000")
+);

eslint.config.js

@@ -0,0 +1,23 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import tseslint from 'typescript-eslint'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      js.configs.recommended,
+      tseslint.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+    },
+  },
+])

frontend/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/png" href="/favicon.png">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Semantic Intent Routing Engine</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>

frontend/src/App.tsx

@@ -0,0 +1,348 @@
+import React, { useState, useRef, useEffect } from 'react';
+import { Send, Trash2 } from 'lucide-react';
+
+interface Message {
+  type: 'user' | 'bot' | 'image';
+  text?: string;
+  src?: string;
+  timestamp?: string; // ISO string from backend
+}
+
+interface ApiResponse {
+  response?: string[];
+}
+
+export default function SemanticChat() {
+  const [messages, setMessages] = useState<Message[]>([
+    {
+      type: 'bot',
+      text: 'Hi. I am Dwarakesh. Ask me anything about myself.',
+      timestamp: new Date().toISOString()
+    }
+  ]);
+  const [input, setInput] = useState<string>('');
+  const [isLoading, setIsLoading] = useState<boolean>(false);
+  const chatContainerRef = useRef<HTMLDivElement>(null);
+  const inputRef = useRef<HTMLInputElement>(null);
+
+  useEffect(() => {
+    if (chatContainerRef.current) {
+      chatContainerRef.current.scrollTop = chatContainerRef.current.scrollHeight;
+    }
+  }, [messages]);
+
+  const handleKeyPress = (e: React.KeyboardEvent<HTMLInputElement>): void => {
+    if (e.key === 'Enter' && !isLoading && input.trim().length > 0) {
+      handleSubmit();
+    }
+  };
+
+  const formatTime = (timestamp?: string): string => {
+    if (!timestamp) return new Date().toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit' });
+    return new Date(timestamp).toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit' });
+  };
+
+  const handleSubmit = async (): Promise<void> => {
+    const userMessage = input.trim();
+    const userTimestamp = new Date().toISOString();
+    setInput('');
+    inputRef.current?.focus();
+
+    setMessages(prev => [...prev, { type: 'user', text: userMessage, timestamp: userTimestamp }]);
+    setIsLoading(true);
+
+    try {
+      const res = await fetch('http://localhost:8000/ask', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ query: userMessage })
+      });
+
+      const data: ApiResponse = await res.json();
+
+      if (data.response && Array.isArray(data.response)) {
+        data.response.forEach((responseText: string) => {
+          const htmlContent = responseText.trim();
+
+          if (htmlContent.length > 0) {
+            setMessages(prev => [
+              ...prev,
+              { type: 'bot', text: htmlContent, timestamp: new Date().toISOString() }
+            ]);
+          }
+        });
+      }
+
+    } catch (err) {
+      console.error(err);
+      setMessages(prev => [...prev, {
+        type: 'bot',
+        text: 'Sorry, something went wrong.',
+        timestamp: new Date().toISOString()
+      }]);
+    } finally {
+      setIsLoading(false);
+    }
+  };
+
+  const handleClearChat = async (): Promise<void> => {
+    try {
+      await fetch('http://localhost:8000/clear', {
+        method: 'POST'
+      });
+
+      // Create new initial message with current timestamp
+      const initialMessage = {
+        type: 'bot' as const,
+        text: 'Hi. I am Dwarakesh. Ask me anything about myself.',
+        timestamp: new Date().toISOString()
+      };
+
+      setMessages([initialMessage]);
+    } catch (err) {
+      console.error('Failed to clear chat:', err);
+    }
+  };
+
+  useEffect(() => {
+    async function loadHistory() {
+      try {
+        const res = await fetch('http://localhost:8000/history');
+        const data = await res.json();
+
+        const mapped = data.map((msg: any) => ({
+          type: msg.role,
+          text: msg.text,
+          src: msg.src,
+          timestamp: msg.timestamp
+        }));
+
+        // Only show initial greeting if there's no history
+        if (mapped.length === 0) {
+          setMessages([
+            {
+              type: 'bot',
+              text: 'Hi. I am Dwarakesh. Ask me anything about myself.',
+              timestamp: new Date().toISOString()
+            }
+          ]);
+        } else {
+          setMessages(mapped);
+        }
+      } catch (err) {
+        console.error('Failed to load history:', err);
+      }
+    }
+
+    loadHistory();
+  }, []);
+
+  return (
+    <div className="h-screen bg-gray-50 flex">
+      {/* Documentation Section - 40% */}
+      <div className="hidden md:block w-[40%] bg-white overflow-y-auto">
+        <div className="p-8">
+          <h1 className="text-3xl font-bold text-gray-900 mb-4">Semantic Intent Routing Engine</h1>
+
+          <p className="text-gray-700 mb-6 leading-relaxed">
+            A lightweight, fast, and fully deterministic alternative to traditional intent-classification systems.
+            Built with <strong>React + Vite + Tailwind</strong> on the frontend and a <strong>Python-based semantic inference engine</strong> on the backend.
+          </p>
+
+          <p className="text-gray-700 mb-8 leading-relaxed">
+            This system routes user queries through a graph of intents using sentence embeddings, adaptive confidence logic, and retrieval-based fallbacks—no classifiers, training loops, or model deployments required.
+          </p>
+
+          <hr className="my-8 border-gray-200" />
+
+          <h2 className="text-2xl font-bold text-gray-900 mb-4">Features</h2>
+
+          <div className="space-y-6">
+            <div>
+              <h3 className="text-lg font-semibold text-gray-900 mb-2">Deterministic Intent Resolution</h3>
+              <p className="text-gray-700 leading-relaxed">
+                Resolves user queries by traversing a DAG-structured intent graph with path-level scoring instead of a single classifier.
+              </p>
+            </div>
+
+            <div>
+              <h3 className="text-lg font-semibold text-gray-900 mb-2">Adaptive Confidence Thresholding</h3>
+              <p className="text-gray-700 leading-relaxed">
+                Automatically adjusts sensitivity based on query length and phrasing, improving routing stability on ambiguous inputs.
+              </p>
+            </div>
+
+            <div>
+              <h3 className="text-lg font-semibold text-gray-900 mb-2">Retrieval-Augmented Fallbacks</h3>
+              <p className="text-gray-700 leading-relaxed">
+                When a query doesn't clearly match any intent, the system fetches semantically similar candidates and recovers gracefully.
+              </p>
+            </div>
+
+            <div>
+              <h3 className="text-lg font-semibold text-gray-900 mb-2">Multi-Turn Context Handling</h3>
+              <p className="text-gray-700 leading-relaxed">
+                Maintains conversational context so follow-up questions like "same as before" or "for that" route correctly without repeating selections.
+              </p>
+            </div>
+
+            <div>
+              <h3 className="text-lg font-semibold text-gray-900 mb-2">Hot-Swappable Intent Graph</h3>
+              <p className="text-gray-700 leading-relaxed">
+                Intents are defined in JSON and automatically converted into a navigable graph.
+                Updates apply instantly—no retraining or redeployment required.
+              </p>
+            </div>
+
+            <div>
+              <h3 className="text-lg font-semibold text-gray-900 mb-2">Fast and Lightweight</h3>
+              <p className="text-gray-700 leading-relaxed">
+                Runs entirely on CPU and maintains <strong>sub-15ms routing latency</strong> thanks to caching and optimized traversal.
+              </p>
+            </div>
+          </div>
+
+          <hr className="my-8 border-gray-200" />
+
+          <h2 className="text-2xl font-bold text-gray-900 mb-4">Architecture Overview</h2>
+
+          <ul className="space-y-2 text-gray-700 leading-relaxed">
+            <li><strong>Frontend:</strong> React + Vite + Tailwind interface for entering queries and testing the routing behavior.</li>
+            <li><strong>Backend:</strong> Python engine using sentence-transformer embeddings and deterministic traversal logic.</li>
+            <li><strong>Intent Graph:</strong> JSON-defined structure supporting multi-parent nodes, examples, responses, and metadata.</li>
+          </ul>
+
+          <hr className="my-8 border-gray-200" />
+
+          <h2 className="text-2xl font-bold text-gray-900 mb-4">Why This Exists</h2>
+
+          <p className="text-gray-700 mb-4 leading-relaxed">
+            Most NLU systems rely on classifiers or fine-tuned models, which brings problems like:
+          </p>
+
+          <ul className="list-disc list-inside space-y-1 text-gray-700 mb-4 ml-4">
+            <li>retraining loops</li>
+            <li>model drift</li>
+            <li>slow iteration cycles</li>
+            <li>low explainability</li>
+          </ul>
+
+          <p className="text-gray-700 leading-relaxed">
+            This project avoids all of that by using semantic similarity, graph traversal, and context tracking to produce stable and predictable routing—even as intents change.
+          </p>
+        </div>
+      </div>
+
+      {/* Chat Section - 60% */}
+      <div className="w-full md:w-[60%] bg-gray-100 flex flex-col border-r border-gray-300">
+        {/* Header */}
+        <div className="bg-teal-600 text-white p-4 shadow-sm">
+          <div className="flex items-center justify-between">
+            <div className="flex items-center gap-3">
+              <div className="w-10 h-10 bg-teal-700 rounded-full flex items-center justify-center font-bold overflow-hidden">
+                <img
+                  src="/static/dp.png"
+                  alt="D"
+                  className="w-full h-full object-contain rounded-full"
+                />
+              </div>
+              <div>
+                <h1 className="font-semibold text-lg">Dwarakesh</h1>
+                <p className="text-xs text-teal-100">
+                  Semantic intent routing engine
+                  <span className="inline md:hidden">
+                    &nbsp; [Works best on large screens]
+                  </span>
+                </p>
+              </div>
+            </div>
+            <button
+              onClick={handleClearChat}
+              className="p-2 hover:bg-teal-700 rounded-full transition-colors cursor-pointer disabled:cursor-not-allowed"
+              title="Clear chat"
+            >
+              <Trash2 size={20} />
+            </button>
+          </div>
+        </div>
+
+        {/* Chat messages container */}
+        <div
+          ref={chatContainerRef}
+          className="flex-1 overflow-y-auto p-4 bg-gray-100"
+          style={{
+            backgroundImage: `repeating-linear-gradient(45deg, transparent, transparent 10px, rgba(0,0,0,.02) 10px, rgba(0,0,0,.02) 20px)`
+          }}
+        >
+          <div className="space-y-3 px-8 py-8">
+            {messages.map((msg, idx) => (
+              <React.Fragment key={idx}>
+                {msg.type === 'user' && (
+                  <div className="flex justify-end">
+                    <div className="max-w-[70%] bg-white text-gray-800 px-4 py-2 rounded-lg rounded-tr-sm shadow-sm">
+                      <div className="font-semibold text-teal-600 text-sm mb-1">You</div>
+                      <div>{msg.text}</div>
+                      <div className="text-xs text-gray-500 mt-1 text-right">
+                        {formatTime(msg.timestamp)}
+                      </div>
+                    </div>
+                  </div>
+                )}
+
+                {msg.type === 'bot' && (
+                  <div className="flex justify-start">
+                    <div className="max-w-[70%] bg-white text-gray-800 px-4 py-2 rounded-lg rounded-tl-sm shadow-sm">
+                      <div className="font-semibold text-teal-600 text-sm mb-1">Dwarakesh</div>
+                      <div className="bot-html-content" dangerouslySetInnerHTML={{ __html: msg.text ?? "" }} />
+                      <div className="text-xs text-gray-500 mt-1">
+                        {formatTime(msg.timestamp)}
+                      </div>
+                    </div>
+                  </div>
+                )}
+
+                {msg.type === 'image' && (
+                  <>
+                    <br />
+                    <div className="flex justify-start">
+                      <div className="bg-white p-2 rounded-lg shadow-sm">
+                        <img
+                          src={msg.src}
+                          alt="Response"
+                          className="w-[250px] rounded"
+                        />
+                      </div>
+                    </div>
+                    <br />
+                  </>
+                )}
+              </React.Fragment>
+            ))}
+          </div>
+        </div>
+
+        {/* Input area */}
+        <div className="bg-gray-200 p-3 border-t border-gray-300">
+          <div className="flex items-center gap-2">
+            <input
+              type="text"
+              value={input}
+              onChange={(e: React.ChangeEvent<HTMLInputElement>) => setInput(e.target.value)}
+              onKeyDown={handleKeyPress}
+              placeholder="Type a message"
+              className="flex-1 px-4 py-3 rounded-full bg-white border-none focus:outline-none disabled:bg-gray-100 disabled:cursor-not-allowed"
+              ref={inputRef}
+            />
+            <button
+              onClick={handleSubmit}
+              disabled={isLoading || input.trim().length === 0}
+              className="w-12 h-12 bg-teal-600 hover:bg-teal-700 cursor-pointer text-white rounded-full flex items-center justify-center disabled:bg-gray-400 disabled:cursor-not-allowed transition-colors"
+            >
+              <Send size={18} />
+            </button>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/index.css

@@ -0,0 +1 @@
+@import "tailwindcss";

frontend/src/main.tsx

@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.tsx'
+
+createRoot(document.getElementById('root')!).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)

package.json

@@ -0,0 +1,36 @@
+{
+  "name": "semantic-analyzer",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc -b && vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@tailwindcss/vite": "^4.1.17",
+    "cors": "^2.8.5",
+    "express": "^5.2.1",
+    "lucide-react": "^0.556.0",
+    "mongoose": "^9.0.1",
+    "react": "^19.2.0",
+    "react-dom": "^19.2.0",
+    "tailwindcss": "^4.1.17"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.1",
+    "@types/node": "^24.10.1",
+    "@types/react": "^19.2.5",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^5.1.1",
+    "eslint": "^9.39.1",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.4.24",
+    "globals": "^16.5.0",
+    "typescript": "~5.9.3",
+    "typescript-eslint": "^8.46.4",
+    "vite": "^7.2.4"
+  }
+}

python_engine/core.py

@@ -0,0 +1,228 @@
+from sentence_transformers import SentenceTransformer, util
+from nltk.tokenize import sent_tokenize
+from util import select_random_from_list
+from math import exp
+import sys
+
+all_MiniLM_L12_v2 = SentenceTransformer("all-MiniLM-L12-v2")
+"""
+all-MiniLM-L12-v2 is a sentence embedding model used for tasks involving semantic textual similarity, clustering, semantic search, and information retrieval. They convert a sentence to tensor based on their intent and then matching patterns like cos_sim can be used to compare them to other sentences.
+"""
+
+CONNECTION_PHRASES = ["just like that", "for the same", "similarly", "similar to the previous", "for that", "for it"]
+CONNECTION_ENCODE = all_MiniLM_L12_v2.encode(CONNECTION_PHRASES,convert_to_tensor=True)
+
+CLEAR_MESSAGES = ["delete", "delete context", "delete history", "clear", "clear context", "clear history", "reset", "reset context", "reset chat", "forget", "forget all"]
+CLEAR_MESSAGES_ENCODE = all_MiniLM_L12_v2.encode(CLEAR_MESSAGES,convert_to_tensor=True)
+
+prev_label = ""
+prev_query_data = [] # Stores previous context if queries contain ambiguous content that may map to previous responses
+
+confidence_threshold = 0.35 # Default confidence threshold
+def generate_confidence_threshold(query: str, base=0.6, decay=0.03, min_threshold=0.25)->float:
+    """Generate confidence threshold based on the sentence. Longer sentence lead to lower confidences, so confidence threshold is adjusted based on that.
+    Parameters:
+    1. query: Modify threshold based on this sentence
+    2. base, decay: 0.8*e^(-(decay * no. of words in string))
+    3. min_threshold: Clamp to minimum to avoid much lower confidence values"""
+    global confidence_threshold
+    length = len(query.split())
+    confidence_threshold = max(base * exp(-decay * length), min_threshold)
+
+# The value of each node contains the following data
+# node.value[0] -> intent
+# node.value[1] -> label
+# node.value[2] -> examples
+# node.value[3] -> response
+# node.value[4] -> children
+
+def cache_embeddings(tree, model = all_MiniLM_L12_v2)->None:
+    """Store the encoded examples as part of the tree itself to avoid repetitive computations.
+    Parameters:
+    1. tree: Tree to cache embeddings
+    2. model: Which model to use to encode (Default: Global model all_MiniLM_L12_v2)"""
+
+    def _cache_node_embeddings(n):
+        if isinstance(n.value, tuple) and len(n.value) >= 2:
+            examples = n.value[2]
+            n.embedding_cache = model.encode(examples, convert_to_tensor=True)
+        for child in n.children:
+            _cache_node_embeddings(child)
+    _cache_node_embeddings(tree.root)
+
+SECOND_PERSON_MENTIONS = ["you", "youre", "your", "yours", "yourself", "y'all", "y'all's", "y'all'self", "you're", "your'e""u", "ur", "urs", "urself"]
+def get_user_query(message="", model = all_MiniLM_L12_v2)->str:
+    """Separate function to get input from user.
+    Parameters:
+    1. message: Show message to user before recieving input (Default: empty)
+    2. model: Which model to use to encode (Default: Global model all_MiniLM_L12_v2)"""
+
+    query = input(message).lower().strip()
+    while query == "":
+        query = input(message).lower().strip()
+    
+    query = query.replace("  "," ") # Remove double spaces
+    for spm in SECOND_PERSON_MENTIONS: # Remove second person mentions
+        query = query.replace(spm,"Amber AI")  # Replace with bot name
+    generate_confidence_threshold(query)
+    query_encode = model.encode(query, convert_to_tensor=True)
+    clear_intent = util.cos_sim(query_encode,CLEAR_MESSAGES_ENCODE).max().item()
+    if clear_intent > confidence_threshold:
+        return None
+    return query
+
+def _calculate_single_level(user_embed,predicted_intent):
+    """Calculate predictions for the children of a single node. Each node contains a list of nodes as its children.
+    Parameters:
+    1. user_embed: User query converted to tensor
+    2. predicted_intent: Calculate for children of this node"""
+
+    categories = predicted_intent.children # List of node objects
+    predicted_intent = None
+    high_intent = 0
+    for category in categories:
+        if category.embedding_cache is None:
+            raise ValueError("Embedding cache missing. Call cache_embeddings() on the tree first")
+        score = util.cos_sim(user_embed, category.embedding_cache).max().item()
+
+        if score > high_intent:
+            high_intent = score
+            predicted_intent = category # Node object
+    return (predicted_intent,high_intent) # Returns the child node with the highest prediction confidence and the confidence value
+
+def _store_prev_data(predicted_intent):
+    """Store the previous computed data path.
+    Parameters:
+    1. predicted_intent: Store previous data w.r.t this node"""
+    # Mutating global prev_query_data
+    prev_query_data.clear()
+    prev_context_treenode = predicted_intent
+    while prev_context_treenode.parent: # Stop at tree root
+        prev_query_data.append(prev_context_treenode)
+        prev_context_treenode = prev_context_treenode.parent
+
+def h_pass(tree, user_embed, predicted_intent = None)->tuple:
+    """Use the model to pass through the tree to compare it with the user query in a hierarchical manner and return an output.
+    Parameters:
+    1. tree: Which tree to pass through hierarchically
+    2. user_embed: User input converted to tensor
+    3. predicted_intent: Where to start the pass from (Default: Root of the tree)"""
+    global prev_label
+    predicted_intent = tree.root if predicted_intent == None else predicted_intent
+    predicted_intent_parent = None
+    high_intent = 0
+    passed_once = False
+    pass_through_intent = {}
+    while predicted_intent.children: # If the node has children, check for the child with the highest confidence value
+        predicted_intent_parent = predicted_intent
+        predicted_intent, high_intent = _calculate_single_level(user_embed,predicted_intent)
+        pass_through_intent[predicted_intent] = high_intent # Store the confidence value of the current node
+        if passed_once: # If the data didn't pass even once, then don't store it
+            _store_prev_data(predicted_intent_parent) # Storing previous data w.r.t parent node as context is changed from current node
+        if high_intent < confidence_threshold: # If highest confidence value is still too low, stop.
+            prev_label = predicted_intent_parent.value[1]
+            return (predicted_intent, predicted_intent_parent, high_intent, passed_once, False, pass_through_intent) # If the confidence value is low, stop   
+        passed_once = True
+    
+    _store_prev_data(predicted_intent)
+    prev_label = predicted_intent.value[1]
+    return (predicted_intent, predicted_intent_parent, high_intent, passed_once, True, pass_through_intent)
+
+def query_pass(tree, user_input, model=all_MiniLM_L12_v2)->list:
+    """Separate multiple queries into separate single ones, analyze relation between them if any, and process them to give an output while storing incomplete query outputs in non-leaf list, which contains the current level of context.
+    Parameters:
+    1. tree: Which tree to pass through hierarchically
+    2. user_input: User input that may contain one or more queries as a string
+    3. model: Which model to use to encode (Default: Global model all_MiniLM_L12_v2)"""
+
+    queries = sent_tokenize(user_input)
+    user_embeddings = [model.encode(query,convert_to_tensor=True) for query in queries]
+    result = []
+    label = prev_label
+
+    for i in range(len(queries)):
+        generate_confidence_threshold(queries[i])
+        pass_value = (None, None, 0, False, False, None)
+        # pass_value[0] -> current predicted intention (node)
+        # pass_value[1] -> parent node of current predicted intention
+        # pass_value[2] -> confidence level
+        # pass_value[3] -> has the query passed through the model atleast once?
+        # pass_value[4] -> has the query reached a leaf node?
+        # pass_value[5] -> confidence values of traversal for query [DEBUGGING PURPOSES]
+
+        # Acquiring data from previous query if the query has words matching with connecting phrases
+        conn_sim = util.cos_sim(user_embeddings[i], CONNECTION_ENCODE).max().item()
+        if conn_sim > confidence_threshold:
+            queries[i] = queries[i] + label
+            user_embeddings[i] = model.encode(queries[i], convert_to_tensor=True)
+
+        # Pass values through the root node and the nodes that have the current context
+        pass_value_root = h_pass(tree,user_embeddings[i]) # Passing through root node
+        pass_value_nonleaf = [h_pass(tree,user_embeddings[i],j) for j in prev_query_data] # Passing through nodes that have current context
+        all_nodes = [pass_value_root] + pass_value_nonleaf # List of all nodes that have been passed through
+        pass_value = max(all_nodes, key=lambda x: x[2]) # Maximum confidence node for available context. Root is always a context.
+        print(f"Query reach confidence: {[i[5] for i in all_nodes]}", file=sys.stderr) # DEBUGGING PURPOSES
+
+        if pass_value[3]: # If the query has passed at least once, ask for data and store current result
+            if not pass_value[4]: # If pass has not reached a leaf node, then ask for more data from the user and keep parent context
+                label = pass_value[1].value[1]
+                result.append(f"{pass_value[1].value[3]}")
+                # continue
+
+            else: # Query has reached a leaf node
+                label = pass_value[0].value[1]
+                result.append(pass_value[0].value[3])
+                # continue
+
+        else: # Query has not passed even once. Check if it works when previous context is available
+            for parent_context in prev_query_data:
+                pass_value_context = h_pass(tree, user_embeddings[i], parent_context)
+                if pass_value_context[3]: # Check if it has passed at least once
+                    # If it has passed, then the query is valid
+                    if not pass_value_context[4]: # If pass has not reached a leaf node, then ask for more data from the user and keep parent context
+                        label = pass_value_context[1].value[1]
+                        result.append(f"What are you looking for in {pass_value_context[1].value[0]}? {pass_value_context[1].value[3]}")
+                    else:
+                        label = pass_value_context[0].value[1]
+                        result.append(pass_value_context[0].value[3])
+                    break # The else block won't be executed if code reaches here
+            else: # The else statement of a for loop will execute only if the loop completes, and won't execute when broken by "break"
+                result.append(f"I don't quite understand what you are trying to ask by \"{queries[i]}\"")
+                # continue
+    # End of "for" loop processing queries
+
+    # Finally, return result. A list of responses same as the length of queries.
+    return result
+
+def process_user_query(query: str, model = all_MiniLM_L12_v2)->str:
+    """Separate function to get input from user.
+    Parameters:
+    1. message: Show message to user before recieving input (Default: empty)
+    2. model: Which model to use to encode (Default: Global model all_MiniLM_L12_v2)"""
+
+    query = query.lower().strip()
+    generate_confidence_threshold(query)
+    query_encode = model.encode(query, convert_to_tensor=True)
+    clear_intent = util.cos_sim(query_encode,CLEAR_MESSAGES_ENCODE).max().item()
+    if clear_intent > confidence_threshold:
+        return None
+    return query
+
+def interact_with_user(tree_data, user_input: str) -> str:
+    """Handles a single user query and returns a response string."""
+    user_input = process_user_query(user_input)
+    all_results = []
+    if user_input:  # If not empty or command
+        results = query_pass(tree_data, user_input)
+        for result in results:
+            # return f"{select_random_from_list(result)}\nContext window: {prev_query_data}"
+            all_results.append(f"{select_random_from_list(result)}")
+        print(f"Previous query data: {prev_query_data}", file=sys.stderr)
+        return all_results
+    else:
+        # Mutating global variables: Clearing context and recent history on command
+        prev_query_data.clear()
+        print(f"Previous query data: {prev_query_data}", file=sys.stderr)
+        return ["Cleared previous context"]
+    
+    

python_engine/engine_server.py

@@ -0,0 +1,50 @@
+import sys
+import json
+from core import interact_with_user, cache_embeddings
+from json_parser import tree_from_json
+from tree import struct_tree
+from time import time
+import os
+
+# Load model once
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.dirname(BASE_DIR)
+
+filepath_root = os.path.join(PROJECT_ROOT, "tree_data")
+filepath_naive = "portfolio_tree_data.json"
+filepath = os.path.join(filepath_root, filepath_naive)
+
+print("Loading engine...", file=sys.stderr)
+
+s = time()
+tree_file = tree_from_json(filepath)
+print(f"Loaded tree json in {time() - s:.2f}s", file=sys.stderr)
+
+s = time()
+cache_embeddings(tree_file)
+print(f"Cached embeddings in {time() - s:.2f}s", file=sys.stderr)
+
+tree_file.save(filepath[:-5] + ".pkl")
+
+s = time()
+tree_data = struct_tree.load(filepath[:-5] + ".pkl")
+print(f"Loaded tree pickle in {time() - s:.2f}s", file=sys.stderr)
+
+print("READY", file=sys.stderr)
+sys.stderr.flush()
+
+# Request loop
+while True:
+    line = sys.stdin.readline()
+    if not line:
+        break
+
+    try:
+        data = json.loads(line)
+        user_query = data.get("query", "")
+        result = interact_with_user(tree_data, user_query)
+        sys.stdout.write(json.dumps({"response": result}) + "\n")
+        sys.stdout.flush()
+    except Exception as e:
+        sys.stdout.write(json.dumps({"error": str(e)}) + "\n")
+        sys.stdout.flush()

python_engine/json_parser.py

@@ -0,0 +1,46 @@
+import json
+import os
+from tree import struct_tree 
+
+SECOND_PERSON_MENTIONS = ["you", "your", "yours", "yourself", "y'all", "y'all's", "y'all'self", "you're", "your'e""u", "ur", "urs", "urself"]
+
+def tree_from_json(filepath):
+    """
+    Load a tree from a structured JSON file.
+    Root node name is taken from the filename (without extension).
+    Each node must have: intent, examples, response, and optionally children.
+    """
+    with open(filepath, 'r') as f:
+        data = json.load(f)
+
+    filename_intent = os.path.splitext(os.path.basename(filepath))[0]
+    root_value = (filename_intent, ["Everything"], data["response"])
+    tree = struct_tree(root_value)
+
+    def add_children(parent_node, children_data):
+        for child in children_data:
+            # Validation
+            if not all(k in child for k in ("intent", "label", "examples", "response")):
+                raise ValueError(f"Missing required fields in node: {child}")
+            
+            intent = child["intent"]
+            label = child["label"]
+
+            examples = child["examples"]
+            # Replace second-person mentions with "Amber AI" to match context
+            # for i in range(len(examples)):
+            #     for spm in SECOND_PERSON_MENTIONS:
+            #         examples[i] = examples[i].replace(spm, "Amber AI")
+
+            response = child["response"]
+
+            value = (intent, label, examples, response)
+            new_node = tree.add_node(parent_node, value)
+
+            if "children" in child:
+                add_children(new_node, child["children"])
+
+    if "children" in data:
+        add_children(tree.root, data["children"])
+    
+    return tree

python_engine/tree.py

@@ -0,0 +1,78 @@
+import pickle
+
+class node:
+    """Individual node class of each node of tree"""
+    def __init__(self, value=None):
+        self.value = value
+        self.parent = None
+        self.children = []
+        self.embedding_cache = None  # Will hold precomputed embeddings if available
+    
+    def __repr__(self):
+        return str(self.value[0]) if isinstance(self.value, tuple) else str(self.value)
+
+class struct_tree:
+    def __init__(self,value): # Root is mandatory
+        self.root = node(value)
+    
+    def add_node(self, parent_node: node, value): # Add a node
+        """Add node to the tree."""
+        new_node = node(value)
+        new_node.parent = parent_node
+        parent_node.children.append(new_node)
+        return new_node
+    
+    def visualize(self, show_labels=True):
+        """Display the tree in a graph-like format."""        
+        def _print_node(current_node, prefix="", is_last=True):
+            # Print current node
+            branch = "└── " if is_last else "├── "
+            print(f"{prefix}{branch}{current_node.value if show_labels else 'o'}")
+            
+            # Prepare prefix for children
+            extension = "    " if is_last else "│   "
+            new_prefix = prefix + extension
+            
+            # Print children
+            child_count = len(current_node.children)
+            for i, child in enumerate(current_node.children):
+                is_last_child = i == child_count - 1
+                _print_node(child, new_prefix, is_last_child)
+        
+        # Start recursive printing from root
+        print(f"{self.root.value if show_labels else 'o'}")
+        child_count = len(self.root.children)
+        for i, child in enumerate(self.root.children):
+            is_last_child = i == child_count - 1
+            _print_node(child, "", is_last_child)
+    
+    def save(self, filepath: str):
+        """Save the entire tree structure to disk with optional compression."""
+        with open(filepath, 'wb') as f:
+            pickle.dump(self, f)
+
+    @staticmethod # This method is called directly on the class rather than an instance of it
+    def load(filepath: str):
+        """Load the tree structure from disk."""
+        with open(filepath, 'rb') as f:
+            return pickle.load(f)
+
+if __name__ == "__main__": # Execute this only in this file
+    # Create a tree with root value "A"
+    tree = struct_tree("A")
+    
+    # Add some nodes
+    b_node = tree.add_node(tree.root, "B")
+    c_node = tree.add_node(tree.root, "C")
+    d_node = tree.add_node(tree.root, "D")
+    
+    # Add children to B
+    tree.add_node(b_node, "B1")
+    b2_node = tree.add_node(b_node, "B2")
+    tree.add_node(b2_node, "B2.1")
+    
+    # Add children to C
+    tree.add_node(c_node, "C1")
+    
+    # Visualize the tree
+    tree.visualize(show_labels=False)

python_engine/util.py

@@ -0,0 +1,6 @@
+from random import randint
+def select_random_from_list(l):
+    if isinstance(l, list): 
+        return l[randint(0,len(l)-1)]
+    else:
+        return l

requirements.txt

@@ -0,0 +1,2 @@
+sentence-transformers
+nltk

tree_data/portfolio_tree_data.json

@@ -0,0 +1,995 @@
+{
+  "intent": "All",
+  "label": "All",
+  "examples": [],
+  "response": "Hello, I'm Dwarakesh. Ask me anything!",
+  "children": [
+    {
+      "intent": "greetings",
+      "label": "greetings",
+      "examples": [
+        "Hi",
+        "Hi there",
+        "Hello",
+        "Hey",
+        "Heya",
+        "Howdy",
+        "Yo",
+        "Good to see you",
+        "Welcome",
+        "Glad you're here",
+        "Nice to have you",
+        "Sup",
+        "Hiya"
+      ],
+      "response": [
+        "Hey there",
+        "Hello",
+        "Hi",
+        "Hey",
+        "Howdy",
+        "Greetings",
+        "Welcome"
+      ]
+    },
+    {
+      "intent": "goodbyes",
+      "label": "goodbyes",
+      "examples": [
+        "Bye!",
+        "Goodbye",
+        "See ya",
+        "Cya",
+        "Catch you later!",
+        "Later!",
+        "Take care",
+        "Peace out",
+        "Talk soon",
+        "See you around",
+        "Have a good one",
+        "See ya later",
+        "I'm out",
+        "Adios"
+      ],
+      "response": [
+        "Catch you later",
+        "See you later",
+        "Goodbye",
+        "Take care",
+        "Bye",
+        "See ya"
+      ]
+    },
+    {
+      "intent": "thanks",
+      "label": "thanks",
+      "examples": [
+        "Thanks",
+        "Thank you",
+        "Much appreciated",
+        "Thanks a lot",
+        "Really appreciate it",
+        "Thanks so much",
+        "Big thanks",
+        "Appreciate it"
+      ],
+      "response": [
+        "Anytime",
+        "Happy to help",
+        "You're welcome",
+        "No problem",
+        "Glad I could help",
+        "Anytime"
+      ]
+    },
+    {
+      "intent": "about",
+      "label": "about",
+      "examples": [
+        "Tell me about yourself",
+        "About you",
+        "Tell me something about you",
+        "Can you tell me about yourself?",
+        "About yourself"
+      ],
+      "response": "I am a student exploring AI/ML and GPU rendering, often diving into Vulkan/low-level graphics. Curiosity-driven projects blend learning with stubborn tinkering."
+    },
+    {
+      "intent": "who are you",
+      "label": "who are you",
+      "examples": [
+        "Who are you?",
+        "What's your name?",
+        "And you are?",
+        "What are you called?",
+        "Mind telling me your name?",
+        "Can I ask your name?",
+        "Who am I talking to?",
+        "What do people call you?",
+        "What are you?",
+        "What am I talking to?"
+      ],
+      "response": [
+        "I am Dwarakesh.",
+        "I am Dwarakesh, a chatbot created by and represents Dwarakesh. Yeah we are the same."
+      ]
+    },
+    {
+      "intent": "what are you doing",
+      "label": "what are you doing",
+      "examples": [
+        "What are you doing?",
+        "What are you up to?"
+      ],
+      "response": [
+        "Chatting with you! Ask me anything related to me or my creator.",
+        "I am here to chat with you! Ask me anything related to me or my creator."
+      ]
+    },
+    {
+      "intent": "where are you from",
+      "label": "where are you from",
+      "examples": [
+        "Where are you from?",
+        "Where do you come from?",
+        "What's your origin?",
+        "Where were you made?",
+        "Where do you live?",
+        "Which place are you based in?",
+        "Where were you born?",
+        "What place are you from?"
+      ],
+      "response": [
+        "I am from Coimbatore. A city in Tamil Nadu, India."
+      ],
+      "children": [
+        {
+          "intent": "country",
+          "label": "country",
+          "examples": [
+            "Which country are you from?",
+            "What country are you in?",
+            "What is your country?",
+            "What country do you belong to?"
+          ],
+          "response": "I am from India, a country in South Asia."
+        },
+        {
+          "intent": "state",
+          "label": "state",
+          "examples": [
+            "Which state are you from?",
+            "What state are you in?",
+            "What is your state?",
+            "What state do you belong to?"
+          ],
+          "response": "I am from Tamil Nadu, a state in South India."
+        },
+        {
+          "intent": "city",
+          "label": "city",
+          "examples": [
+            "Which city are you from?",
+            "What city are you in?",
+            "What is your city?",
+            "What city do you belong to?"
+          ],
+          "response": "I am from Coimbatore, located in Tamil Nadu."
+        }
+      ]
+    },
+    {
+      "intent": "where are you from server",
+      "label": "where are you from server",
+      "examples": [
+        "Server",
+        "Where are you hosted?",
+        "Where do you run?",
+        "Source code",
+        "Where can I see your code?",
+        "Where are you deployed?"
+      ],
+      "response": "I run on Hugging face spaces, and my source code is available on files in the same site under the MIT license."
+    },
+    {
+      "intent": "how are you",
+      "label": "how are you",
+      "examples": [
+        "How are you?",
+        "How's it going?",
+        "How are things?",
+        "You doing okay?",
+        "How have you been?",
+        "Everything good?",
+        "All good with you?"
+      ],
+      "response": [
+        "I'm doing just fine. Hope you are too.",
+        "I'm doing great. Hope that's the case with you too.",
+        "I'm doing well, thanks for asking. Hope you're doing well too."
+      ]
+    },
+    {
+      "intent": "education",
+      "label": "education",
+      "examples": [
+        "Where did you study?",
+        "Education stats",
+        "School",
+        "College",
+        "Which school did you study in?",
+        "Which college did you study in?",
+        "Where are you studying?"
+      ],
+      "response": "I studied at PSBB Millennium till 8th and Suguna Pip School till 12th, both in Coimbatore. I'm currently doing my undergrad at Amrita Vishwa Vidyapeetham, Coimbatore.",
+      "children": [
+        {
+          "intent": "school",
+          "label": "school",
+          "examples": [
+            "School",
+            "Which school did you study in?",
+            "Where was your early education?"
+          ],
+          "response": "I studied at PSBB Millennium till 8th and Suguna Pip School till 12th, both in Coimbatore. As for kindergarten, I was in Akshara fun School, Coimbatore."
+        },
+        {
+          "intent": "college",
+          "label": "college",
+          "examples": [
+            "College",
+            "Which college did you study in?",
+            "Where did you do your UG?",
+            "Where did you complete your undergraduate degree?",
+            "Are you pursuing your undergraduate degree?"
+          ],
+          "response": "I'm currently pursuing my undergraduate degree for B.TECH Computer Science in Amrita Vishwa Vidyapeetham, Coimbatore."
+        }
+      ]
+    },
+    {
+      "intent": "10th board scores",
+      "label": "10th board scores",
+      "examples": [
+        "10th board scores",
+        "What is your 10th board score?",
+        "What was your 10th grade score?",
+        "What was your 10th grade percentage?",
+        "What was your 10th grade CGPA?",
+        "What was your 10th grade marks?",
+        "How much did you score in middle school?"
+      ],
+      "response": "I scored 461/500 in my 10th grade board exams, which is 92.2%, in the CBSE board."
+    },
+    {
+      "intent": "12th board scores",
+      "label": "12th board scores",
+      "examples": [
+        "12th board scores",
+        "What is your 12th board score?",
+        "What was your 12th grade score?",
+        "What was your 12th grade percentage?",
+        "What was your 12th grade CGPA?",
+        "What was your 12th grade marks?",
+        "How much did you score in high school?"
+      ],
+      "response": "I scored 454/500 in my 12th grade board exams, which is 90.8%, in the CBSE board."
+    },
+    {
+      "intent": "entrance exam",
+      "label": "entrance exam",
+      "examples": [
+        "Entrance exam",
+        "What entrance exams did you take?",
+        "Did you take any entrance exams?",
+        "What are your entrance exam scores?",
+        "What was your JEE score?",
+        "What was your JEE Advanced score?",
+        "What was your JEE Mains score?"
+      ],
+      "response": "I took the JEE Mains and JEE Advanced. I scored 97.9 percentile in JEE Mains, and 48/360 in JEE Advanced.",
+      "children": [
+        {
+          "intent": "JEE Mains",
+          "label": "JEE Mains",
+          "examples": [
+            "What was your JEE Mains score?",
+            "What was your JEE Mains percentile?",
+            "What was your JEE Mains rank?"
+          ],
+          "response": "I scored 97.9 percentile in JEE Mains."
+        },
+        {
+          "intent": "JEE Advanced",
+          "label": "JEE Advanced",
+          "examples": [
+            "What was your JEE Advanced score?",
+            "What was your JEE Advanced rank?"
+          ],
+          "response": "I scored 48/360 in JEE Advanced."
+        }
+      ]
+    },
+    {
+      "intent": "exam fail",
+      "label": "exam fail",
+      "examples": [
+        "Have you failed an exam?",
+        "Did you ever mess up an exam?",
+        "Have you failed before?",
+        "Ever gotten something totally wrong?",
+        "Have you had a bad result?"
+      ],
+      "response": "I have failed in my JEE Advanced exam. My score was 48, while the cutoff was 55, out of 360."
+    },
+    {
+      "intent": "arrears",
+      "label": "arrears",
+      "examples": [
+        "Arrears",
+        "Do you have any arrears?",
+        "Any pending subjects to clear?",
+        "Any backlogs?"
+      ],
+      "response": [
+        "Nope! I'm all clear - never been in arrears.",
+        "I have never had any arrears, and never will!"
+      ]
+    },
+    {
+      "intent": "future plans",
+      "label": "higher studies",
+      "examples": [
+        "Future plans",
+        "What do you plan to do in the future?",
+        "Got any plans for later?",
+        "Higher studies",
+        "Are you planning for higher studies?",
+        "After college",
+        "What will you do after college?",
+        "What will you do after getting a degree?"
+      ],
+      "response": "I have no plans for higher studies. I wish for a placement in a good company, but I have not decided my domain yet.",
+      "children": [
+        {
+          "intent": "higher studies",
+          "label": "higher studies",
+          "examples": [
+            "Are you planning for higher studies?",
+            "What are your plans for higher studies?",
+            "Do you want to pursue higher studies?"
+          ],
+          "response": "I have no plans for higher studies."
+        },
+        {
+          "intent": "placement",
+          "label": "placement",
+          "examples": [
+            "What will you do after college?",
+            "What will you do after getting a degree?",
+            "Are you looking for a job?",
+            "Are you looking for placement?"
+          ],
+          "response": "I wish for a placement in a good company, but I have not decided my domain yet."
+        }
+      ]
+    },
+    {
+      "intent": "field of interest",
+      "label": "machine learning, deep learning",
+      "examples": [
+        "What are you interested in?",
+        "Technical interests?",
+        "What is your field of interest?",
+        "Is something your field of interest?"
+      ],
+      "response": "I am particularly interested in machine learning and deep learning, but I do not dislike other domains."
+    },
+    {
+      "intent": "hobbies",
+      "label": "blender, badminton",
+      "examples": [
+        "What do you in your free time?",
+        "Hobbies",
+        "What are your hobbies?",
+        "Recreational activities",
+        "Blender",
+        "Do you use blender?",
+        "What do you do in blender?",
+        "How long have you been using blender?",
+        "Renders",
+        "Can I see your renders?",
+        "Show me your renders",
+        "Badminton",
+        "How long have you been playing badminton?",
+        "How good are you at badminton?",
+        "Drawing",
+        "What do you draw?",
+        "Since when have you been drawing?",
+        "Can I see your drawings?",
+        "Show me your drawings"
+      ],
+      "response": "I am a 3D enthusiast primarily working on the free open source software Blender. I also play badminton, and draw sometimes.",
+      "children": [
+        {
+          "intent": "blender",
+          "label": "blender",
+          "examples": [
+            "Blender",
+            "Do you use blender?",
+            "What do you do in blender?",
+            "How long have you been using blender?",
+            "How experienced are you in blender?",
+            "What have you made with blender?",
+            "How do you use blender?",
+            "Can you show me your renders?",
+            "Any good renders?",
+            "Renders",
+            "Can I see your renders?",
+            "Show me your renders"
+          ],
+          "response": "I have been using blender for since 2021. I am primarily a hard surface modelling artist with some experience in environmental and interior design.",
+          "children": [
+            {
+              "intent": "blender time",
+              "label": "blender",
+              "examples": [
+                "When did you start using blender?",
+                "How long have you been using blender?",
+                "Since when have you been using blender?"
+              ],
+              "response": "I have been using blender since 2021."
+            },
+            {
+              "intent": "blender render",
+              "label": "blender",
+              "examples": [
+                "What have you made with blender?",
+                "How do you use blender?",
+                "Can you show me your renders?",
+                "Any good renders?",
+                "Renders",
+                "Can I see your renders?",
+                "Show me your renders"
+              ],
+              "response": "Here is some of my work. <br> <img src = '../static/mountain_tent.jpg' onclick = 'requestFullscreen(this)'> <br> <img src = '/static/forest_tent.png' onclick = 'requestFullscreen(this)'> <br> <img src = '/static/my_room.png' onclick = 'requestFullscreen(this)'>"
+            },
+            {
+              "intent": "blender experience",
+              "label": "experience",
+              "examples": [
+                "How experienced are you in blender?",
+                "How good are you at blender?",
+                "How good are you at 3D modelling?"
+              ],
+              "response": "I have been using blender for over 4 years, so I consider myself to be decent."
+            }
+          ]
+        },
+        {
+          "intent": "badminton",
+          "label": "badminton",
+          "examples": [
+            "Badminton",
+            "How long have you been playing badminton?",
+            "Since when have you been playing badminton?",
+            "How experienced are you at badminton?"
+          ],
+          "response": "I have been playing badminton since I was 12, but I have had very long breaks in between. Now it is a part of my daily life, and I consider myself an above average player.",
+          "children": [
+            {
+              "intent": "badminton time",
+              "label": "badminton",
+              "examples": [
+                "How long have you been playing badminton?",
+                "Since when have you been playing badminton?"
+              ],
+              "response": "I have been playing badminton since I was 12, but I have had very long breaks in between. Now it is a part of my daily life."
+            },
+            {
+              "intent": "badminton experience",
+              "label": "experience",
+              "examples": [
+                "How good are you at badminton?",
+                "How experienced are you at badminton?"
+              ],
+              "response": "I consider myself an above average player."
+            }
+          ]
+        },
+        {
+          "intent": "drawing",
+          "label": "drawing",
+          "examples": [
+            "Drawing",
+            "What do you draw?",
+            "Since when have you been drawing?",
+            "Can I see your drawings?",
+            "Show me your drawings"
+          ],
+          "response": "I draw rarely. It used to be my favourite hobby in the past, until I discovered Blender.",
+          "children": [
+            {
+              "intent": "drawing time",
+              "label": "drawing",
+              "examples": [
+                "How long have you been drawing?",
+                "Since when have you been drawing?"
+              ],
+              "response": "I have been drawing since I was 10 years old."
+            },
+            {
+              "intent": "drawing experience",
+              "label": "drawing",
+              "examples": [
+                "How good are you at drawing?",
+                "How experienced are you at drawing?"
+              ],
+              "response": "I am really not sure... it turns out really well sometimes and really bad some other times."
+            },
+            {
+              "intent": "see drawings",
+              "label": "see drawings",
+              "examples": [
+                "Can I see your drawings?",
+                "Show me your drawings",
+                "What have you drawn?"
+              ],
+              "response": "Here are some of my drawings. <br> <img src = '/static/holter.jpg' onclick = 'requestFullscreen(this)'> <br> <img src = '/static/stark.jpg' onclick = 'requestFullscreen(this)'> <br> <img src = '/static/kratos.jpg' onclick = 'requestFullscreen(this)'>"
+            }
+          ]
+        }
+      ]
+    },
+    {
+      "intent": "dream",
+      "label": "dream",
+      "examples": [
+        "What is your dream?",
+        "Dream job?",
+        "What do you want to be?",
+        "What is your ambition?",
+        "What do you aspire to be?"
+      ],
+      "response": "I would like to be placed among my favourite companies, which are Google, Nvidia and Tesla, but I have no specific dream job.",
+      "children": [
+        {
+          "intent": "dream company",
+          "label": "google, nvidia, tesla",
+          "examples": [
+            "What is your dream company?",
+            "Dream company?",
+            "Which company do you want to work for?",
+            "What is your favourite company?",
+            "What companies do you like?",
+            "Where do you want to work?",
+            "Where do you want to be placed?",
+            "What is your dream job?"
+          ],
+          "response": "My favourite companies are Google, Nvidia and Tesla. I would like to be placed among them."
+        },
+        {
+          "intent": "dream",
+          "label": "artificial intelligence",
+          "examples": [
+            "What is your dream?",
+            "What do you want to achieve?",
+            "What is something you want to do?"
+          ],
+          "response": "My dream is to revolutionize the field of artificial intelligence and create industry standard applications."
+        }
+      ]
+    },
+    {
+      "intent": "work experience",
+      "label": "work",
+      "examples": [
+        "Do you have work experience?",
+        "Any jobs before?",
+        "Have you worked before?",
+        "Your job history?",
+        "Have you worked in a job before?",
+        "Experience",
+        "What is your experience?",
+        "About your experience"
+      ],
+      "response": "I haven't done any official jobs yet. Just learning and building stuff for now."
+    },
+    {
+      "intent": "internships",
+      "label": "internships",
+      "examples": [
+        "Have you done any internships?",
+        "Internships?",
+        "Have you interned anywhere?",
+        "Any internships?"
+      ],
+      "response": "I haven't done any internships yet. I'm learning stuff for now."
+    },
+    {
+      "intent": "competitions",
+      "label": "competitions",
+      "examples": [
+        "Have you participated in any competitions?",
+        "Competitions?",
+        "Have you competed in anything?",
+        "Any competitions?",
+        "Hackathons?",
+        "Have you participated in any hackathons?",
+        "Have you competed in any hackathons?",
+        "Any hackathons?",
+        "Have you qualified for a hackathon?"
+      ],
+      "response": "I have participated in a few hackathons, but I haven't won any yet. I'm still learning and improving."
+    },
+    {
+      "intent": "skills",
+      "label": "skills",
+      "examples": [
+        "What are your skills?",
+        "Skills?",
+        "What are you good at?",
+        "What are your strengths?",
+        "What are your abilities?",
+        "What can you do?"
+      ],
+      "response": "I am skilled in the languages Python, C++, Java, HTML, CSS, JavaScript."
+    },
+    {
+      "intent": "specialization",
+      "label": "specialization",
+      "examples": [
+        "What is your specialization?",
+        "What are your special skills?",
+        "Specialization?",
+        "What are you specialized in?",
+        "What is your major?"
+      ],
+      "response": "I am specialized in Python, with a focus on AI/ML and Rust, GPU rendering. I am also interested in low-level graphics programming."
+    },
+    {
+      "intent": "soft skills",
+      "label": "soft skills",
+      "examples": [
+        "What are your soft skills?",
+        "Soft skills?",
+        "What are your interpersonal skills?",
+        "What are your people skills?",
+        "What are your communication skills?"
+      ],
+      "response": "I am a good leader and a good team player. I am also a good communicator, and I can work well with people from different backgrounds and cultures, though I tend to be a bit shy."
+    },
+    {
+      "intent": "certifications",
+      "label": "certifications",
+      "examples": [
+        "Have you done any certifications?",
+        "Certifications?",
+        "Have you completed any courses?",
+        "Any certifications?",
+        "Have you done any online courses?"
+      ],
+      "response": "I have completed a few online courses, but I haven't received any certifications yet."
+    },
+    {
+      "intent": "languages",
+      "label": "languages",
+      "examples": [
+        "Languages?",
+        "What languages do you know?",
+        "How many languages do you know?",
+        "Can you speak language?",
+        "What languages can you speak?",
+        "What languages are you fluent in?"
+      ],
+      "response": "I am fluent in English, Tamil and Telugu, and somewhat fluent in Hindi and Malayalam."
+    },
+    {
+      "intent": "merits and demerits",
+      "label": "merits and demerits",
+      "examples": [
+        "What are your merits?",
+        "What are your demerits?",
+        "What are your merits and demerits?",
+        "What are your strengths and weaknesses?",
+        "What are your pros and cons?",
+        "What are your good and bad points?",
+        "What are your good and bad qualities?"
+      ],
+      "response": "I am a very curious person, and I love to learn new things. I am also very stubborn, and I don't give up easily. My demerit is that I am not very good at communicating with people, and I tend to be a bit shy.",
+      "children": [
+        {
+          "intent": "merits",
+          "label": "merits",
+          "examples": [
+            "What are your merits?",
+            "What are your strengths?",
+            "What are your good points?",
+            "What are your good qualities?"
+          ],
+          "response": "I am a very curious person, and I love to learn new things. I am also very stubborn, and I don't give up easily."
+        },
+        {
+          "intent": "demerits",
+          "label": "demerits",
+          "examples": [
+            "What are your demerits?",
+            "What are your weaknesses?",
+            "What are your bad points?",
+            "What are your bad qualities?"
+          ],
+          "response": "My demerit is that I am not very good at communicating with people, and I tend to be a bit shy."
+        }
+      ]
+    },
+    {
+      "intent": "research papers",
+      "label": "research papers",
+      "examples": [
+        "Have you published any research papers?",
+        "Research papers?",
+        "Have you written any research papers?",
+        "Any research papers?",
+        "Have you written any papers?",
+        "Are you researching on something?",
+        "Are you doing any research?",
+        "Are you working on any research?"
+      ],
+      "response": "I have not published any research papers yet, but I am working on a few."
+    },
+    {
+      "intent": "projects",
+      "label": "projects",
+      "examples": [
+        "What projects are you working on?",
+        "What are your projects?",
+        "What are you working on?",
+        "What is your current project?",
+        "What is your latest project?",
+        "What are your current projects?",
+        "What are your latest projects?",
+        "How do you even work?",
+        "How do you function?",
+        "How are you able to chat?",
+        "What is your technology?",
+        "What is your tech stack?",
+        "What is your architecture?",
+        "How are you built?",
+        "How are you made?",
+        "What is your framework?",
+        "About your tech stack",
+        "About your technology",
+        "About your working",
+        "About Dwarakesh AI",
+        "About Dwarakesh",
+        "About you",
+        "How does Dwarakesh work?",
+        "How does Dwarakesh function?"
+      ],
+      "response": "I am currently working on a routing semantic engine. ",
+      "children": [
+        {
+          "intent": "Dwarakesh AI",
+          "label": "Dwarakesh AI",
+          "examples": [
+            "How do you even work?",
+            "How do you function?",
+            "How are you able to chat?",
+            "What is you technology?",
+            "What is your tech stack?",
+            "What is your architecture?",
+            "How are you built?",
+            "How are you made?",
+            "What is your framework?",
+            "About your tech stack",
+            "About your technology",
+            "About you working",
+            "About you",
+            "About Dwarakesh",
+            "How does Dwarakesh work?",
+            "How does Dwarakesh function?"
+          ],
+          "response": "I am a semantic engine created by Dwarakesh. For details on how I work, check out my source code and documentation on Hugging face using files in the same porfolio site."
+        }
+      ]
+    },
+    {
+      "intent": "contact",
+      "label": "contact",
+      "examples": [
+        "How can I contact you?",
+        "How can I reach you?",
+        "How can I get in touch with you?",
+        "How can I connect with you?",
+        "How to contact you?",
+        "Contact",
+        "Contact",
+        "Can I colab with you?",
+        "Can I collaborate with you?",
+        "Can I work with you?",
+        "Contact information",
+        "Gmail",
+        "Email",
+        "Github",
+        "LinkedIn",
+        "Social media",
+        "Social links",
+        "Social media accounts",
+        "What is your email?",
+        "What is your github?",
+        "What is your linkedin?",
+        "What is your social media?"
+      ],
+      "response": "You can contact me through Github on <a href = 'https://www.github.com/Dwarakesh-V'> github.com/Dwarakesh-V </a>, Email on <email link>, or LinkedIn on <a href = 'https://www.linkedin.com/in/vdwarakesh/'> linkedin.com/in/vdwarakesh </a>.",
+      "children": [
+        {
+          "intent": "email",
+          "label": "email",
+          "examples": [
+            "What is your email?",
+            "What is your email address?",
+            "Email",
+            "Gmail"
+          ],
+          "response": "My email is <a href='mailto:dwarakesh.2005.4@gmail.com'>dwarakesh.2005.4@gmail.com</a>."
+        },
+        {
+          "intent": "github",
+          "label": "github",
+          "examples": [
+            "What is your github?",
+            "Github",
+            "Github link"
+          ],
+          "response": "My github is <a href = 'https://www.github.com/Dwarakesh-V'> github.com/Dwarakesh-V </a>."
+        },
+        {
+          "intent": "linkedin",
+          "label": "linkedin",
+          "examples": [
+            "What is your linkedin?",
+            "LinkedIn",
+            "LinkedIn link"
+          ],
+          "response": "My linkedin is <a href = 'https://www.linkedin.com/in/vdwarakesh/'> linkedin.com/in/vdwarakesh </a>."
+        }
+      ]
+    },
+    {
+      "intent": "favorite color",
+      "label": "favorite color",
+      "examples": [
+        "What is your favorite color?",
+        "Favorite color?",
+        "What color do you like?",
+        "Do you have a favorite color?",
+        "What colors do you like?"
+      ],
+      "response": "My favourite color is green, tied with black."
+    },
+    {
+      "intent": "favorite food",
+      "label": "favorite food",
+      "examples": [
+        "What is your favorite food?",
+        "Favorite food?",
+        "What food do you like?",
+        "Do you have a favorite food?",
+        "What foods do you like?"
+      ],
+      "response": "I like South Indian food in general, but I'm not very picky."
+    },
+    {
+      "intent": "favorite movie",
+      "label": "favorite movie",
+      "examples": [
+        "What is your favorite movie?",
+        "Favorite movie?",
+        "What movie do you like?",
+        "Do you have a favorite movie?",
+        "What movies do you like?"
+      ],
+      "response": "I am not a big fan of movies."
+    },
+    {
+      "intent": "parents",
+      "label": "parents",
+      "examples": [
+        "What are your parents doing?",
+        "What is you father doing?",
+        "What is your mother doing?",
+        "What is your father's job?",
+        "What is your mother's job?",
+        "What do your parents do?",
+        "What do your parents work as?",
+        "What do your parents do for a living?"
+      ],
+      "response": "My father is a buisnessman, and my mother is a government employee."
+    },
+    {
+      "intent": "siblings",
+      "label": "siblings",
+      "examples": [
+        "Do you have any siblings?",
+        "Are you an only child?",
+        "Do you have a brother or sister?",
+        "How many siblings do you have?",
+        "What are your siblings doing?"
+      ],
+      "response": "I have no siblings. I am an only child."
+    },
+    {
+      "intent": "maritial status",
+      "label": "maritial status",
+      "examples": [
+        "Are you married?",
+        "Are you single?",
+        "Do you have a partner?",
+        "Are you in a relationship?",
+        "Do you have a girlfriend?",
+        "Are you dating?"
+      ],
+      "response": [
+        "I might have a partner. I might be single. Good luck finding out."
+      ]
+    },
+    {
+      "intent": "interests",
+      "label": "interests",
+      "examples": [
+        "Is there someone you like?",
+        "Are you interested in anyone?",
+        "Do you have a crush?",
+        "Whom do you like?",
+        "Who is your crush?"
+      ],
+      "response": "There could be someone I like. Maybe I'm lying. Good luck finding out."
+    },
+    {
+      "intent": "age",
+      "label": "age",
+      "examples": [
+        "How old are you?",
+        "What is your age?",
+        "When were you born?",
+        "When is your birthday?",
+        "What year were you born?"
+      ],
+      "response": "I was born on November 2, 2005. Which means I am 19 years old.",
+      "children": [
+        {
+          "intent": "birth date",
+          "label": "birth date",
+          "examples": [
+            "When is your birthday?",
+            "What is your birth date?",
+            "When were you born?",
+            "What year were you born?"
+          ],
+          "response": "I was born on November 2, 2005."
+        },
+        {
+          "intent": "current age",
+          "label": "current age",
+          "examples": [
+            "How old are you?",
+            "What is your age?",
+            "How many years old are you?"
+          ],
+          "response": "I am 19 years old."
+        }
+      ]
+    },
+    {
+      "intent": "resume",
+      "label": "resume",
+      "examples": [
+        "Resume",
+        "Can I see your resume?",
+        "Show me your resume",
+        "Send me your resume"
+      ],
+      "response": "Here is my resume.<br><img src = '/static/resume_sample.webp' onclick = 'requestFullscreen(this)'>"
+    }
+  ]
+}

tree_data/portfolio_tree_data.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e79abe36e19a7a9a41b7ac4e5979e41ad94aae0c92cdee2da2f465a0ba467d6c
+size 707759

tsconfig.app.json

@@ -0,0 +1,28 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+    "target": "ES2022",
+    "useDefineForClassFields": true,
+    "lib": ["ES2022", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "types": ["vite/client"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["frontend/src"]
+}

tsconfig.json

@@ -0,0 +1,7 @@
+{
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.node.json" }
+  ]
+}

tsconfig.node.json

@@ -0,0 +1,26 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+    "target": "ES2023",
+    "lib": ["ES2023"],
+    "module": "ESNext",
+    "types": ["node"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["vite.config.ts"]
+}

vite.config.ts

@@ -0,0 +1,12 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import tailwindcss from '@tailwindcss/vite'
+
+// https://vite.dev/config/
+export default defineConfig({
+  root: "frontend",
+  plugins: [
+    react(),
+    tailwindcss(),
+  ],
+})