🤖 AI Tech Support Chatbot (NLP)

An intelligent chatbot designed to automate Level 1 technical support. Built using Natural Language Processing (NLP) techniques and a Hybrid Classification Approach (TF-IDF + Keyword Safety Net) to accurately diagnose common computer issues.

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📖 Project Overview

This project was developed to assist users with common technical problems such as internet connectivity issues, system crashes, and hardware malfunctions. It utilizes a Logistic Regression model trained on TF-IDF vectors for general intent recognition, reinforced by a Keyword Safety Net to handle specific technical terms with high precision.

Key Capabilities:

• Diagnose Issues: Identifies 20+ common PC problems (e.g., Blue Screen, Slow WiFi, No Audio).
• Smart Fallback: Detects when it doesn't know an answer and guides the user to human support.
• Context Awareness: Distinguishes between "My internet is bad" (Internet Issue) and "My internet is good" (Game Lag).
• Interactive UI: A modern, dark-themed interface built with Streamlit.

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🛠️ Tech Stack

• Language: Python
• Frontend: Streamlit (Web Interface)
• NLP & ML:
  • NLTK: Tokenization & Lemmatization
  • Scikit-Learn: TF-IDF Vectorization & Logistic Regression
• Data Handling: JSON (Intent dataset), Pickle (Model serialization)

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🚀 Installation & Setup

1. Clone the Repository

git clone [https://github.com/yourusername/ai-tech-support-bot.git](https://github.com/yourusername/ai-tech-support-bot.git)
cd ai-tech-support-bot

2. Install Dependencies

Ensure you have Python installed. Then run:

pip install -r requirements.txt

3. Train the Model

Before running the bot, you must generate the model files (chatbot_model.pkl, etc.).

python train_model.py

You should see a message: "Training complete! Model is now smarter and more confident."

4. Run the Application

Launch the chatbot interface:

streamlit run app.py

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📂 Project Structure

ai-tech-support-bot/
│
├── app.py                # Main application file (Streamlit UI & Logic)
├── train_model.py        # Script to train the ML model
├── intents.json          # Dataset containing patterns and responses
├── requirements.txt      # List of Python dependencies
├── README.md             # Project documentation
│
└── (Generated Artifacts)
    ├── chatbot_model.pkl # Trained Logistic Regression model
    ├── vectorizer.pkl    # TF-IDF Vectorizer
    └── responses.pkl     # Response dictionary

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💡 How It Works

1. Preprocessing: User input is tokenized and lemmatized (e.g., "running" -> "run") using NLTK.
2. Vectorization: The text is converted into numbers using TF-IDF (Term Frequency-Inverse Document Frequency).
3. Prediction (Layer 1): The Logistic Regression model predicts the intent (e.g., wifi_issue) and assigns a confidence score.
4. Keyword Safety Net (Layer 2): If the model's confidence is low (< 0.5), the system scans for specific keywords (e.g., "blue screen", "HDMI"). If a match is found, it overrides the model to ensure accurate support.
5. Context Check: The bot checks for "Anti-Keywords" (e.g., if a user says "Internet is good", it ignores the internet_issue tag).

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🧪 Example Queries

Try asking the bot:

• "My computer is not turning on"
• "I have a blue screen error"
• "My wifi is connected but no internet"
• "Who made you?"
• "My FPS is dropping in games"

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👨‍💻 Author

Dev Pandey

• Role: Software Engineer

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📝 License

This project is open-source and available for educational purposes.

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