DataBased-chatbot

#DataBased WhatsApp Chatbot

This repository contains a scaffold for a premium BSNL WhatsApp chatbot: FastAPI backend, SQLite DB models, WhatsApp webhook, OTP auth, and stubs for AI, voice, payments, and Fiber flows.

Quick start

1. Create a virtual env and install:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

1. Copy environment file and fill keys:

cp .env.example .env
# edit .env to add TWILIO / OPENAI / UPI values

1. Run the app:

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

WhatsApp webhook

Configure your WhatsApp provider (Twilio / Meta Cloud API) to forward incoming messages to /webhook.

Mermaid workflow (paste into GitHub or Mermaid live editor):

flowchart TD
  User-->WhatsAppChatbot
  WhatsAppChatbot-->Menu[Main Menu]
  Menu-->OTP[OTP Verification]
  OTP-->FastAPI
  FastAPI-->DB[Database / Services]
  DB-->FastAPI
  FastAPI-->WhatsAppChatbot
  WhatsAppChatbot-->User
  subgraph Escalation
    UserIssue-->Chatbot
    Chatbot-->TicketCreated
    TicketCreated-->HumanAgent
  end

Files added

• app/main.py — FastAPI app + webhook and sample endpoints
• app/db.py — SQLModel/SQLite engine
• app/models.py — core models (User, Ticket, Transaction)
• app/services.py — business logic stubs
• app/utils.py — OTP + helpers
• requirements.txt — Python deps
• .env.example — env vars template

Next steps

• Wire actual WhatsApp provider credentials
• Implement payment gateway webhook (UPI)
• Integrate OpenAI (or other LLM) for AI chat
• Add voice transcription (Whisper) and multilingual support

If you want, I can now implement the WhatsApp webhook routing for menu flows and OTP end-to-end.