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monish563/NU-KIOSK-API

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App README

Northwestern CS Kiosk API

REST API backend for the Northwestern CS Department Kiosk. This is a stripped-down version optimized for integration with external systems (e.g., speech-to-text/text-to-speech).

Quick Start

1. Install Dependencies

bash

pip install -r requirements.txt

2. Configure Environment

bash

cp .env.example .env
# Edit .env and add your API key

3. Run the Server

bash

python -m backend.main

The API will be available at http://0.0.0.0:8000

Deploy to Hugging Face Spaces

Deploy this API as a public endpoint so your manager (or STT/TTS systems) can send requests from anywhere.

1. Create a new Space

1.Go to huggingface.co/spaces
2.Click Create new Space
3.Choose Docker SDK, Blank template
4.Name it (e.g. monish563/NU-Kiosk-API)
5.Create, then push this kiosk-api folder to the Space repo

2. Add secrets (Settings → Variables and secrets → Secrets Private)

Secret	Required	Description
`ANTHROPIC_API_KEY`	Yes*	Anthropic API key (starts with `sk-ant-api03-...`)
`KIOSK_LLM_PROVIDER`	No	Default: `anthropic`
`KIOSK_LLM_MODEL`	No	Default: `claude-haiku-4-5`
`KIOSK_LLM_SYSTEM_PROMPT`	No	Custom system prompt for the receptionist
`KIOSK_LLM_STYLE`	No	Style guidelines for TTS-friendly responses
`OPENAI_API_KEY`	No	If using `provider: "openai"`
`GEMINI_API_KEY`	No	If using `provider: "gemini"`
`KIOSK_HF_DATASET_REPO`	No	HF dataset for persistence (e.g. `monish563/kiosk-api-metrics`)
`KIOSK_HF_TOKEN`	No*	HF token with write access (required if dataset repo is set)

*At least one LLM API key is required. KIOSK_HF_TOKEN is required if KIOSK_HF_DATASET_REPO is set.

3. Endpoint URL for your manager

Once the Space is built and running, the base URL will be:

https://<your-username>-<space-name>.hf.space

Main endpoint (for STT → TTS flow):

POST https://<your-username>-<space-name>.hf.space/api/query
Content-Type: application/json

{"question": "Where is Professor Hammond's office?"}

Response: {"answer": "...", ...} — send answer to your TTS system.

API Reference

Health Check

GET /

Response:

json

{
  "status": "ok",
  "service": "Northwestern CS Kiosk API"
}

Query (Main Endpoint)

POST /api/query

This is the primary endpoint for speech integration.

Request Body:

json

{
  "question": "Where is Professor Hammond's office?",
  "session_id": "optional-session-id",
  "provider": "anthropic"
}

Field	Type	Required	Description
`question`	string	Yes	The user's question (from speech-to-text)
`session_id`	string	No	Session ID for conversation continuity (default: "default")
`provider`	string	No	LLM provider: `anthropic`, `openai`, `gemini`

Response:

json

{
  "session_id": "default",
  "session_title": "Chat – Jan 23, 10:30 AM",
  "question": "Where is Professor Hammond's office?",
  "answer": "Professor Kristian Hammond's office is located in Mudd 3225.",
  "blueprint": "location",
  "facts": [...],
  "notes": [],
  "usage": {
    "provider": "anthropic",
    "model": "claude-haiku-4-5",
    "tokens": 512
  },
  "action": {
    "type": "lookup_location",
    "arguments": { "name": "Kristian Hammond" }
  }
}

Key Fields:

—answer - The response text (send to text-to-speech)
—question - Echo of the input question
—blueprint - Which tool was used internally
—facts - Structured data retrieved
—usage - Token/model metadata

List Providers

GET /api/providers

Returns available LLM providers and their configuration status.

Response:

json

{
  "providers": {
    "claude": {
      "name": "Claude",
      "configured": true,
      "default_model": "claude-haiku-4-5"
    },
    "gpt": {
      "name": "GPT",
      "configured": false,
      "note": "Set OPENAI_API_KEY before using this provider."
    }
  },
  "default_provider": "claude"
}

Get History

GET /api/history?session_id=default

Returns conversation history for a session.

Response:

json

{
  "session_id": "default",
  "title": "Chat – Jan 23, 10:30 AM",
  "history": [
    {
      "timestamp": 1706012345.123,
      "question": "Who is Kristian Hammond?",
      "answer": "Professor Kristian Hammond is...",
      "blueprint": "person_lookup"
    }
  ]
}

List Sessions

GET /api/sessions

Returns all conversation sessions.

Response:

json

{
  "sessions": [
    {
      "session_id": "default",
      "title": "Chat – Jan 23, 10:30 AM",
      "created_at": 1706012345.123,
      "updated_at": 1706012400.456
    }
  ]
}

Integration Example

cURL

bash

curl -X POST "http://localhost:8000/api/query" \
  -H "Content-Type: application/json" \
  -d '{"question": "Where is Professor Hammond?"}'

Python

python

import requests

response = requests.post(
    "http://localhost:8000/api/query",
    json={"question": "Where is Professor Hammond?"}
)
data = response.json()
answer = data["answer"]  # Send this to text-to-speech

JavaScript

javascript

const response = await fetch("http://localhost:8000/api/query", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ question: "Where is Professor Hammond?" })
});
const data = await response.json();
const answer = data.answer;  // Send this to text-to-speech

Speech Integration Flow

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Microphone │ ──▶ │   STT API   │ ──▶ │ Kiosk API   │ ──▶ │   TTS API   │
│             │     │ (Speech to  │     │ /api/query  │     │ (Text to    │
│             │     │   Text)     │     │             │     │  Speech)    │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
                           │                   │                   │
                           ▼                   ▼                   ▼
                      "Where is           {"answer":          [Audio]
                       Prof X?"           "Prof X is           🔊
                                          in Mudd..."}

Available Query Types

Query Type	Example Questions
Person lookup	"Who is Kristian Hammond?", "Tell me about Katie Winters"
Location	"Where is Professor X's office?", "Where does student Y sit?"
Research topics	"Who researches AI?", "Faculty working on machine learning?"
Advisors	"Who advises student X?", "Who does Prof Y advise?"
Centers	"Who leads the Center for Deep Learning?"
Staff support	"Who handles reimbursements?", "Academic advising contact?"
Office hours	"When are CS 211 office hours?"
Events	"Any upcoming AI events?"

Environment Variables

Variable	Required	Default	Description
`ANTHROPIC_API_KEY`	Yes*	-	Anthropic API key
`OPENAI_API_KEY`	Yes*	-	OpenAI API key
`GEMINI_API_KEY`	Yes*	-	Google Gemini API key
`KIOSK_LLM_PROVIDER`	No	`anthropic`	Default LLM provider
`KIOSK_HOST`	No	`0.0.0.0`	Server host
`KIOSK_PORT`	No	`8000`	Server port
`KIOSK_LLM_TIMEOUT`	No	`60`	LLM timeout (seconds)

*At least one API key is required.

Project Structure

kiosk-api/
├── Archive/              # Data files (CSV)
├── backend/
│   ├── data/             # Data loading utilities
│   ├── mcp/              # LLM planner & tool execution
│   ├── providers/        # LLM provider implementations
│   ├── tools/            # Query blueprints
│   ├── main.py           # FastAPI application
│   └── responders.py     # Response generation
├── .env.example          # Environment template
├── requirements.txt      # Python dependencies
└── README.md             # This file