This project is aimed at developing an end-to-end food delivery system with a chatbot interface. Leveraging Dialogflow for natural language understanding, FastAPI for efficient backend processing, and MySQL for database management, the system provides a seamless and user-friendly experience for ordering food.

• Features
• Tech Stack
• Installation
• Usage
• Directory Structure
• Running the FastAPI Backend
• Ngrok for HTTPS Tunneling
• Contributing
• License

• Conversational Interaction: Users can place orders, modify them, and inquire about order statuses using natural language queries.
• Efficient Backend Processing: FastAPI ensures rapid and asynchronous handling of incoming requests, contributing to a responsive backend.
• Database Management: MySQL stores and retrieves data related to user profiles, menu items, and order details.
• Order Processing Automation: Automation logic streamlines order processing, reducing manual intervention and improving operational efficiency.
• Real-Time Updates: Webhooks facilitate real-time communication, providing users with instant updates on changes in order status.

• Dialogflow: Natural language understanding for user interactions.
• FastAPI: Python web framework for efficient backend processing.
• MySQL: Database management for storing critical information.

1. Clone the repository:

   git clone https://github.com/your-username/food-delivery-chatbot.git
   cd food-delivery-chatbot
   

2. Install dependencies:

   pip install -r backend/requirements.txt
   

   OR

   pip install mysql-connector
   pip install "fastapi[all]"
   

3. Import the MySQL database dump from the db directory into your MySQL database using a tool like MySQL Workbench.

• Update the Dialogflow assets in the dialogflow_assets directory with your own training phrases and intents.
• Configure the FastAPI backend to connect to your MySQL database.
• Follow the instructions in Running the FastAPI Backend to start the server.
• Optionally, set up Ngrok for HTTPS tunneling as explained in Ngrok for HTTPS Tunneling.

food-delivery-chatbot/
|-- backend/
|-- db/
|-- dialogflow_assets/
|-- frontend/
|-- README.md
|-- .gitignore

• backend: Contains Python FastAPI backend code.
• db: Contains the dump of the database. Import this into your MySQL database.
• dialogflow_assets: Holds training phrases and intents for Dialogflow.
• frontend: Website code.

1. Go to the backend directory in your command prompt.

2. Run the following command:

   uvicorn main:app --reload
   

1. Install Ngrok by downloading it from [https://ngrok.com/download].

2. Extract the zip file and place ngrok.exe in a folder.

3. Open the command prompt, navigate to that folder, and run:

   ngrok http 8000
   

   Note: Ngrok may timeout; restart the session if you encounter a session expired message.

Contributions are welcome! Please follow the Contributing Guidelines.

This project is licensed under the MIT License.