🧠 Jarvis-Like AI Assistant using Python

Inspired by Iron Man's iconic AI, this project is a fully functional, voice-controlled AI assistant built using Python. It integrates real-time data handling, voice recognition, image generation, and automation to perform diverse tasks — from answering queries to executing system commands.

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🚀 Features

• 🎙 Voice Recognition & Interaction
  Converts voice to text and understands WH-type questions for responsive interaction.

• 🧠 AI-Powered Decision Making
  A custom model classifies incoming queries into:

  • General (static knowledge)
  • Real-time (internet data fetch)
  • Automation (system/application tasks)

• 🌐 Real-Time Query Processing
  Fetches up-to-date information like current events or stock prices using integrated search APIs.

• 🖼️ Image Generation
  Uses Hugging Face API to generate AI-powered images from user-defined text prompts (e.g., “Tony Stark in a futuristic suit”)

• 💬 Persistent Chat Memory
  Remembers conversations and user-provided data across sessions (like name, age, preferences).

• 🖥️ System Automation
  Executes commands like opening/closing apps, playing music, generating leave letters, etc.

• 🔊 Text-to-Speech & Custom Voice
  Responds using customizable speech output and supports voice selection.

• 🧵 Threaded Execution
  Runs frontend and backend in parallel using threading for seamless interaction.

• 💡 UI Integration
  Chat interface for real-time user input, assistant responses, and voice status indicators.

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📁 Project Structure

AI-CHATBOT_ASSISTANT/
├── .venv/                       # Python virtual environment
│
├── Backend/                     # Core AI logic and modules
│   ├── __pycache__/             # Compiled Python files
│   ├── Automation.py            # Handles system automation tasks
│   ├── Chatbot.py               # Conversation handling logic
│   ├── ImageGeneration.py       # Text-to-image generation using AI
│   ├── Model.py                 # Query classification and decision making
│   ├── RealTimeSearchEngine.py  # Live web search integration
│   ├── SpeechToText.py          # Voice input handling
│   ├── TextToSpeech.py          # Voice output using TTS
│   └── tempCodeRunnerFile.py    # Temporary dev file (optional)
│
├── Data/                        # Runtime and static assistant data
│   ├── ChatLog.json             # Persistent chat memory
│   ├── speech.mp3               # Temporary TTS output
│   ├── Titiksha_Didi2.jpg       # Sample image file
│   └── Voice.html               # Voice control template (UI)
│
├── Frontend/                    # Assistant interface and settings
│   ├── __pycache__/             # Compiled Python files
│   ├── Files/
│       ├── Database.data        # Local data or Q&A base
│       ├── ImageGeneration.data # Image generation configuration
│       ├── Mic.data             # Microphone status/settings
│       ├── Response.data        # Assistant response templates
│       └── Status.data          # Runtime assistant/system status
│   ├──Graphics/                 # GUI & graphic images
│   └── Gul.py                   # GUI layout and visuals
│
├── .env                         # API keys and environment configs
├── Main.py                      # Entry point of the application
└── Requirements.txt             # Python dependencies list

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Details

🔧 Installation

1. Clone the repository

   • 🔗 GitHub Repository

   • cd AI-ChatBot-Voice-Assistant

2. Create a virtual environment

   • python -m venv venv

   • source venv/bin/activate # or venv\Scripts\activate on Windows

3. Install dependencies

   • pip install -r requirements.txt

4. Set up your API keys

   • Get keys from:

     • 🔑 Hugging Face
     • 🧬 Cohere API-Key
     • 🌐 Google Cloud
     • ☁️ Groq Cloud
     • 🔍 Serp API-Key

   • Place your Hugging Face API key, Google Search API key, etc., in environment variables or config files as per instructions in Model.py and ImageGeneration.py.

5. Run the assistant

   • python Main.py

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🧠 How It Works

• The assistant listens to voice commands.

• It classifies the query type (general, real-time, or automation) using a decision-making model.

• Based on the classification:

  • It responds using LLMs (like ChatGPT-style for general queries),

  • Fetches live data for real-time queries,

  • Executes automation tasks (like opening apps, playing media, etc.)

• It also retains memory across sessions using Chatlog.json.

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💻 Usage Examples

Here are some real-world voice commands you can try with your assistant:

🧠 Command Type	🎤 Example Voice Command	💡 What It Does
Real-Time Query	"What's the current Bitcoin price?"	Fetches live crypto data using search APIs
General Knowledge	"Who is the Prime Minister of India?"	Uses LLM to respond with general facts
Automation	"Open Spotify and play LoFi music"	Launches Spotify and plays music via automation
File Handling	"Create a leave application for sick leave"	Auto-generates and saves a leave letter
Image Generation	"Draw a cyberpunk city at night"	Generates an AI image using Hugging Face API
Personal Info Recall	"What's my favorite food?"	Responds using data remembered from past sessions

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🔄 Version Comparison

🔢 Version	✨ Features Added
v1.0	Basic voice interaction, simple automation
v1.5	Image generation, chat memory
v2.0	Real-time queries, threaded UI, enhanced NLP
v2.5 (Upcoming)	Emotion tone control, web dashboard, multi-language

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🔮 Future Enhancements

• 🎭 Emotion-based tone switching in TTS

• 🌐 Web dashboard for history, control, and settings

• 🧬 Custom AI model training from user interactions

• 🌍 Multi-language interaction support

• 🙋 Personal user profiles for adaptive behavior

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🤝 Contribute or Hire

I welcome contributions and ideas. If you have blog suggestions or freelance opportunities, feel free to contact me via DM on socials.