Intelligent Conversational Engine (Web Edition)

Live Demo: intelligent-conversational-engine-jkym-4x6higb97.vercel.app

A data-structure–driven conversational text engine that provides real-time auto-completion, next-word prediction, and semantic word relationship mapping, now with a modern Web Interface.

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📌 Overview

The Intelligent Conversational Engine has been transformed from a terminal-based tool into a full-stack web application. It features a FastAPI backend for high-performance prediction and a React (Vite) frontend for a premium user experience.

The system analyzes text patterns using optimized data structures: Tries for autocomplete, Bigram models for next-word prediction, Red-Black Trees for frequency management, and Graphs for semantic mapping.

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📂 Project Structure

intelligent-conversational-engine/
│
├── backend/                # FastAPI Application
│   ├── api/                # API Endpoints
│   │   └── index.py        # Main FastAPI app
│   ├── engine/             # Core Logic (Trie, Bigram, RBT, Graph)
│   │   └── intelligent_engine.py
│   └── requirements.txt    # Python dependencies
│
├── frontend/               # React (Vite) Application
│   ├── src/                # UI Components & Logic
│   │   ├── App.jsx         # Main Chat UI
│   │   └── App.css         # Premium Styles
│   └── .env                # Environment configuration
│
├── README.md               # You are here
└── intelligent_engine.py   # Original terminal-based logic (legacy)

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🚀 Rapid Setup

1. Backend (FastAPI)

cd backend
pip install -r requirements.txt
uvicorn api.index:app --host 0.0.0.0 --port 10000

• API will be live at http://localhost:10000
• Docs: http://localhost:10000/docs

2. Frontend (React)

cd frontend
npm install
npm run dev

• UI will be live at http://localhost:5173

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✨ Web Features

• Vibrant UI: Sleek dark mode with glassmorphism effects.
• Top Words: Real-time frequency analysis.
• Auto-complete: Fast prefix matching via Trie.
• Next-Word Prediction: Probabilistic next-word suggestions.
• Semantic Mapping: Visual insights into word relationships.
• Memory Persistence: The engine maintains state as long as the backend runs.

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🛠 Tech Stack

• Backend: Python 3.x, FastAPI, NetworkX, Uvicorn.
• Frontend: React 18/19, Vite, CSS3 (Vanilla).
• Deployment: Prepared for Render (Backend) and Vercel (Frontend).

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🎯 Implementation Details

• Trie: Used for O(L) time complexity prefix search where L is word length.
• Bigram Model: Stores word couples to calculate "P(Word_B | Word_A)".
• Red-Black Tree: Ensures balanced word storage and fast lookup.
• Word Graph: Maps successors in sentences to discover semantic links.

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👨‍💻 Author

Yaswanth Jallipalli (Web conversion by Antigravity AI)