README.md

metadata

title: Apartment
emoji: 🏠
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 6.13.0
app_file: app.py
pinned: false

Apartment Predictor (Numeric Model + LLM)

This Space demonstrates the Week 2 AI Applications pattern:

• natural language apartment wishes
• structured extraction (rooms, area_m2, town)
• reuse of an existing pickled random forest model
• LLM explanation of the result

Because the data is Swiss, students should write prompts in German so town names like Zürich match the dataset more reliably.

Student workflow

• Build logic in notebook (week2/ai_applications_exercise2.ipynb)
• Reuse the provided saved model file random_forest_regression.pkl
• Implement TODOs in app_student.py (any LLM provider is allowed)
• Promote finished code to app.py for deployment
• Deploy the app to Hugging Face Spaces
• Complete documentation.md

What To Submit

Your submission for this exercise should include:

• a working deployed app on Hugging Face Spaces
• your finished code files
• a completed documentation.md

In documentation.md, document what you built, how your prompts work, how you tested the app, and what happened during deployment.

You must also include 2 screenshots from your app:

• 2 different example inputs
• visible extracted JSON
• visible prediction
• visible final explanation text

LLM policy in this exercise

• LLM usage is mandatory.
• No fallback path is allowed for extraction/explanation.
• Errors should stay visible so issues can be debugged.

Reference solution details

• app.py is an OpenAI-based reference implementation.
• It expects OPENAI_API_KEY (and optional OPENAI_MODEL).

Required files

• app.py
• app_student.py
• requirements.txt
• random_forest_regression.pkl
• bfs_municipality_and_tax_data.csv

See NOTEBOOK_TO_APP.md for the transfer checklist.