Food Ordering Assistant (Dialogflow + FastAPI)

Overview

This project is a conversational food ordering assistant built using Dialogflow for natural language understanding and FastAPI as the backend server.
Users can add, remove, view, and complete their food orders through conversational commands.

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Features

• Natural language interaction using Dialogflow
• Add items to order (with quantity and variants)
• Remove items from order (handles case insensitive matching)
• View current order status
• Complete order and reset session
• Session management with FastAPI backend

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Tech Stack

• Dialogflow CX / ES (for building conversational agents)
• FastAPI (Python framework for REST API backend)
• Uvicorn (ASGI server to run FastAPI)
• Ngrok (optional, for local tunneling to expose your server to Dialogflow)
• Python 3.9+

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Getting Started

Prerequisites

• Python 3.9 or higher installed
• Dialogflow agent created in Google Cloud Console
• Ngrok installed (optional for local development)

Installation

1. Clone the repo:

   git clone https://github.com/yourusername/FoodChatBot-AI-assistant.git
   cd FoodChatBot-AI-assistant

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Running the Server

uvicorn main:app --reload

• The FastAPI server will start on http://127.0.0.1:8000.
• Use ngrok to expose this endpoint to Dialogflow:

  ngrok http 8000

• Copy the public HTTPS URL from ngrok and set it as the webhook URL in your Dialogflow fulfillment.

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Dialogflow Setup

• Create intents for adding, removing, viewing, and completing orders.
• Set up webhook fulfillment pointing to your FastAPI server URL.
• Use session IDs to maintain order state per user.
• Map user expressions to respective intent actions.

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Usage

• Start a conversation in Dialogflow simulator or integrated platform.
• Add food items by saying:
  “Add 2 masala dosas”
• Remove items by saying:
  “Remove dosa” (case insensitive handling)
• Check your order status:
  “What’s in my order?”
• Complete your order:
  “Complete order”

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Troubleshooting

• Make sure your webhook URL is HTTPS and reachable from Dialogflow.
• Use consistent session IDs to track user orders.
• Handle case insensitive matching to improve user experience.
• Review Dialogflow logs for webhook errors.

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License

MIT License