ChatBot Project

This project is a chatbot application that integrates Google Sheets data with Google's Gemini AI to provide conversational responses based on spreadsheet content. It consists of a Flask backend API and a PHP frontend interface.

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Prerequisites

• Python 3.7 or higher
• PHP 7.4 or higher
• Google Cloud service account with access to Google Sheets API
• Gemini API key from Google AI Generative Language

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Installation

1. Clone the repository or download the project files.

2. Create and activate a virtual environment:

   macOS / Linux

   python3 -m venv venv
   source venv/bin/activate

   Windows (PowerShell)

   python -m venv venv
   .\venv\Scripts\Activate

   💡 You should now see (venv) at the start of your terminal prompt.

3. Install Python dependencies inside the activated environment:

   pip install -r requirements.txt

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Configuration

1. Obtain a Google Cloud service account JSON key with permissions to read Google Sheets.

2. Get your Gemini API key from Google AI Generative Language.

3. Prepare your Google Spreadsheet and note its Spreadsheet ID.

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Running the Project

Start the Flask Backend

Run the Flask backend server on port 5001:

python app.py

Start the PHP Frontend Server

Run the PHP built-in server on port 8000:

php -S localhost:8000 index.php

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Usage

1. Open your browser and navigate to http://localhost:8000.

2. Sign up or log in with a username and password.

3. Fill in the chatbot configuration form:

   • Chatbot Name
   • Generate or enter a Chatbot ID
   • Gemini API Key
   • Gemini Model (default: gemini-2.0-flash)
   • Google Spreadsheet ID
   • Paste the Service Account JSON key

4. Click Connect to authorize and list available sheets.

5. Select one or more sheets to load.

6. Click Load to Chat to start chatting with the bot based on your spreadsheet data.

7. Save your chatbot configuration for later use if desired.

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Database

The project uses a SQLite database (chatbots.db) to store user credentials and chatbot configurations.

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Notes

• Ensure your Google service account has read access to the specified spreadsheet.
• The Flask backend runs on port 8080 and the PHP frontend on port 8000—make sure these ports are free.
• The chatbot uses Gemini AI to generate responses based on spreadsheet data.

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Docker Setup

This project includes comprehensive Docker support for easy deployment and development.

Quick Start with Docker

1. Clone and navigate to the project:

   git clone <repository-url>
   cd ChatBot

2. Start the application:

   # Using docker-compose (recommended)
   docker-compose up -d
   
   # Or using make
   make up

3. Access the application:

   • Frontend: http://localhost:8000
   • Backend API: http://localhost:5001

Docker Commands

Development

# Start development environment
make dev

# View logs
make logs

# Open shell in container
make shell

# Stop development environment
make down

Production

# Build production image
make build-prod

# Start production environment
make prod

# Scale application
make scale

Maintenance

# Clean up containers and images
make clean

# Backup database
make backup

# Check status
make status

Manual Docker Commands

# Build the image
docker build -t chatbot:latest .

# Run the container
docker run -d \
  --name chatbot \
  -p 8000:8000 \
  -p 5001:5001 \
  -v $(pwd)/data:/app/data \
  chatbot:latest

## To build and run the docker

docker build -t chatbot:latest .
docker run -d --name chatbot -p 8080:8080 chatbot:latest

# Check running containers
docker ps

# Check all containers (including stopped)
docker ps -a

# View container logs
docker logs -f chatbot

# Execute commands in running container
docker exec -it chatbot bash

# Stop container
docker stop chatbot

# Start stopped container
docker start chatbot

# Remove container
docker rm chatbot

Production Deployment

1. Build for production:

   docker buildx build --platform linux/amd64 -t shivamnishad/chatbot:latest .

2. Push to registry:

   docker push shivamnishad/chatbot:latest

3. Run in production:

   docker-compose -f docker-compose.prod.yml up -d

Environment Variables

Create a .env file based on .env.example:

cp .env.example .env

Key variables:

• GEMINI_API_KEY: Your Google Gemini API key
• GEMINI_MODEL: AI model to use (default: gemini-2.0-flash)
• FLASK_ENV: Set to 'production' for production deployment

Database Persistence

The SQLite database is automatically persisted in the data/ directory. For production, consider using PostgreSQL:

# Uncomment PostgreSQL service in docker-compose.prod.yml
# Update DATABASE_URL in .env file

Troubleshooting

1. Port conflicts: Change ports in docker-compose.yml if needed
2. Permission issues: Ensure data directory has proper permissions
3. Database errors: Check database file permissions and location
4. Memory issues: Adjust resource limits in docker-compose.prod.yml