TIF_E41253368/.github/copilot-instructions.md

GitHub Copilot Instructions — Prediksi Harga Cabai

Project

This repository is the FastAPI deployment backend for the D4 thesis: "Analisis Model Long Short-Term Memory dalam Sistem Prediksi Harga Cabai dengan Visualisasi Peta Digital Interaktif di Kabupaten Jember"

The API serves one-day-ahead LSTM predictions for cabai rawit at:

• Pasar_Tanjung
• Pasar_Kreongan
• Pasar_Tegal_Besar
• Pasar_Mangli
• Pasar_Wirolegi

Environment

Use the existing Anaconda environment:

• Conda: tf_gpu39
• Python: 3.9.23
• Interpreter: C:\Users\alfia\anaconda3\envs\tf_gpu39\python.exe

Do not create another Conda environment unless explicitly requested.

ML artifacts

The models are already trained and deployed. Do NOT retrain them in the API.

Each market has:

• model.h5
• scaler.pkl
• config.json

Final deployment configuration:

• neurons: 64
• activation: tanh
• learning_rate: 0.05
• batch_size: 32
• epochs: 50
• optimizer: Adam
• time_step: 7
• seed: 42
• shuffle: true

These values are reference metadata. Do not hard-code predictions.

Inference rules

For each request:

1. Accept exactly 7 recent prices.
2. Convert to numeric shape (7, 1).
3. Use the saved market-specific scaler.
4. Transform the 7 values.
5. Reshape to (1, 7, 1).
6. Run the saved .h5 model once.
7. Inverse-transform the output.
8. Return the predicted next price.

The model unit is thousand rupiah:

• 22.0 means approximately Rp22,000.

Do not fit a new scaler on request data.

Model loading

Load all five model/scaler/config artifacts once during FastAPI startup/lifespan and keep them in memory.

Do NOT:

• load models on every request
• call model.fit()
• retrain on startup
• recreate a different LSTM architecture when the saved model can be loaded directly

For inference, loading Keras .h5 with compile=False is preferred.

Suggested project structure

C:\PrediksiHargaCabe
├── api/ │ ├── main.py │ ├── requirements.txt │ └── models/ │ └── rawit/ │ ├── pasar_tanjung/ │ │ ├── model.h5 │ │ ├── scaler.pkl │ │ └── config.json │ ├── pasar_kreongan/ │ ├── pasar_tegal_besar/ │ ├── pasar_mangli/ │ └── pasar_wirolegi/ ├── website/ └── .github/ └── copilot-instructions.md

Inspect the workspace first and reuse existing files instead of creating duplicates.

Required API

Implement at least:

GET / Basic status response.

GET /api/markets Return Rawit and the five supported markets.

GET /api/health Report API status and whether all five model artifacts loaded successfully.

POST /api/prediction/rawit/{market} Request example: { "prices": [18, 16, 19, 19, 22, 22, 22] }

Response should include:

• chili
• market
• time_step
• input_prices
• predicted_price_thousand
• predicted_price_rupiah

Use Pydantic validation.

Reject:

• unknown markets
• fewer/more than 7 prices
• non-numeric values
• NaN/infinite values
• negative prices

CORS

Prepare CORS so a React/React Native frontend can call the API during development. Keep allowed origins configurable and avoid recommending unrestricted wildcard CORS for production.

Testing

Before declaring the API complete:

1. Start from the api directory with: python -m uvicorn main:app --reload
2. Verify /.
3. Verify /api/markets.
4. Verify /api/health.
5. Verify /docs.
6. Test prediction for all five markets.
7. Compare at least one known API prediction with the validated notebook inference, allowing normal floating-point differences.

Thesis safety

Do not silently:

• change hyperparameters
• change time_step
• change the scaler method
• retrain models
• introduce another ML model
• modify the validated ML methodology

Never call the final configuration a "global optimum". Use wording such as: "konfigurasi terbaik yang diperoleh dari skema eksperimen yang diterapkan pada dataset penelitian."

Agent workflow

Before editing:

1. Inspect the workspace.
2. Inspect existing files and model paths.
3. Reuse correct code.
4. Avoid duplicate entry points or model directories.

When editing:

1. Make small coherent changes.
2. Preserve validated ML inference logic.
3. Use clear Python/FastAPI structure.
4. Run a practical validation after each important change.
5. If a command fails, inspect and fix the actual error; do not fake success.

The immediate priority is Rawit only:

1. Build the FastAPI backend.
2. Load the five saved artifacts.
3. Verify startup.
4. Implement /api/markets, /api/health, and /api/prediction/rawit/{market}.
5. Test the API.
6. Prepare it for connection to the thesis frontend.

Communication preference: Explain important decisions briefly in Indonesian, give exact commands, state which files are changed, and avoid unrelated technologies.