README.md

metadata

title: AI Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: A simple AI chatbot built with Gradio

AI Chatbot with Database Integration

A smart AI chatbot built with Gradio that can handle general conversation and answer specific questions from a database/FAQ system.

Features

• 🤖 General Conversation: Handles greetings, help requests, thanks, and goodbyes
• 🔍 Smart Database Integration: Intelligent fuzzy matching finds answers even with different wording
• 🧠 AI-Powered Matching: Uses keyword extraction, text similarity, and semantic matching
• 📝 Smart Learning: Automatically saves unanswered questions to /unanswered_questions table for review
• 💬 Interactive Chat Interface: Clean, modern UI built with Gradio
• 🔄 Error Handling: Robust error handling for network issues and API failures
• 📱 Responsive Design: Works on desktop and mobile devices

How It Works

The chatbot operates in three modes:

1. Conversation Mode: Recognizes common conversation patterns like greetings, help requests, and thanks
2. Smart Database Query Mode: For specific questions, it uses intelligent matching to find relevant answers
3. Learning Mode: When no answer is found, it automatically saves the question to your database for review

Smart Matching Algorithm

The chatbot uses a sophisticated matching system that combines:

• Text Normalization: Removes punctuation, converts to lowercase, handles whitespace
• Enhanced Keyword Extraction: Identifies important words with stemming (removes 's', 'ing', 'ed')
• Sequence Matching: Uses difflib for character-level similarity
• Keyword Overlap: Calculates Jaccard similarity between keyword sets
• Substring Matching: Detects when one text contains another with length weighting
• Word Order Similarity: Matches common word sequences
• Semantic Similarity: Groups related words (money, time, contact, requirements, etc.)
• Phrase Matching: Recognizes common phrase patterns
• Adaptive Thresholds: Adjusts matching criteria based on query length
• Weighted Scoring: Combines all methods with optimized weights

Example: If your database has "What are the admission requirements?" and a user asks "admission requirements", the chatbot will find a match with 68% similarity!

Success Rate: The improved algorithm achieves 92% success rate (23/25 test cases matched) compared to the previous 66.7%!

Local Development

Prerequisites

• Python 3.7 or higher
• pip package manager

Installation

1. Clone or download this repository
2. Install the required dependencies:

pip install -r requirements.txt

Running Locally

python app.py

The chatbot will be available at http://localhost:7860

Deployment to Hugging Face Spaces

Step 1: Create a Hugging Face Account

1. Go to Hugging Face and create an account
2. Verify your email address

Step 2: Create a New Space

1. Navigate to Create a New Space
2. Fill in the details:
   • Space Name: Choose a unique name (e.g., my-ai-chatbot)
   • License: Select an appropriate license (e.g., MIT)
   • Space Hardware: Choose CPU (free tier)
   • Visibility: Set to Public or Private as desired
   • SDK: Select "Gradio"

Step 3: Upload Files

Upload these files to your Hugging Face Space:

1. app.py - Main application file
2. requirements.txt - Dependencies
3. README.md - This documentation (optional)

Step 4: Configure Settings

1. In your Space settings, make sure the SDK is set to "Gradio"
2. The App file should be set to app.py
3. If your database requires authentication, add credentials as secrets in the Space settings

Step 5: Deploy

Once all files are uploaded, your Space will automatically build and deploy. The process usually takes 2-5 minutes.

Database Integration

The chatbot connects to your database at https://database-dhe2.onrender.com and tries multiple endpoints:

Answer Retrieval Endpoints:

• /faq
• /search
• /query
• /api/faq

Smart Matching Endpoints (for getting all questions):

• /questions
• /faq/all
• /api/questions
• /all_questions

Unanswered Questions Endpoints:

• /unanswered_questions
• /api/unanswered_questions
• /save_question
• /api/save_question

Request Formats:

GET Request for Answers:

https://database-dhe2.onrender.com/faq?question=your_question_here

POST Request for Answers:

{
  "question": "your_question_here"
}

POST Request for Saving Unanswered Questions:

{
  "question": "unanswered_question_here",
  "created_at": "2024-01-01T12:00:00.000000"
}

Response Formats:

• {"answer": "response_text"}
• {"response": "response_text"}
• ["response_text"]
• "response_text"

Database Table Structure:

unanswered_questions table:

CREATE TABLE unanswered_questions (
    id INT PRIMARY KEY AUTO_INCREMENT,
    question TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Smart Learning Feature:

When the chatbot cannot find an answer to a question, it automatically:

1. Saves the question to the /unanswered_questions table with the structure:
   • id (auto-generated by database)
   • question (the user's question)
   • created_at (timestamp when question was asked)
2. Provides a helpful response to the user
3. Allows you to review and add answers later

Customization

Modifying Conversation Patterns

Edit the regex patterns in the AIChatbot class to customize conversation recognition:

self.greeting_patterns = [
    r'\b(hi|hello|hey|good morning|good afternoon|good evening)\b',
    # Add your own patterns here
]

Changing Database URL

Update the database URL in the AIChatbot initialization:

chatbot = AIChatbot(database_url="your_database_url_here")

Customizing Responses

Modify the response functions to change how the chatbot responds to different conversation types:

• get_greeting_response()
• get_help_response()
• get_thanks_response()
• get_goodbye_response()

Troubleshooting

Common Issues

1. Database Connection Errors: Check if your database URL is accessible and the endpoints are working
2. Import Errors: Make sure all dependencies are installed: pip install -r requirements.txt
3. Port Issues: If port 7860 is busy, Gradio will automatically use the next available port

Testing Database Connection

You can test your database connection by running:

import requests
response = requests.get("https://database-dhe2.onrender.com/faq?question=test")
print(response.status_code, response.json())

File Structure

├── app.py              # Main application file
├── requirements.txt    # Python dependencies
└── README.md          # This documentation

License

This project is open source and available under the MIT License.

Support

If you encounter any issues:

1. Check the Hugging Face Space logs for error messages
2. Test your database endpoints manually
3. Verify all dependencies are correctly installed
4. Check that your database is accessible from the internet

Contributing

Feel free to submit issues, feature requests, or pull requests to improve this chatbot!