fix readme

README.md

@@ -1,57 +1,42 @@
-# Hugging Face Spaces Deployment
-
-This folder contains everything required to deploy the Predictive Maintenance API to Hugging Face Spaces as a Docker Space.
-
-## Contents
-
-- `api/`: FastAPI application (copied from `AC02-ML/api`)
-- `requirements.txt`: Python dependencies (now includes `huggingface_hub`)
-- `Dockerfile`: Container recipe used by Spaces
-
-## One-Time Setup
-
-1. Install the Hugging Face CLI and log in:
-   ```
-   pip install huggingface_hub
-   huggingface-cli login
-   ```
-2. (Optional) Enable Git LFS if the model artifacts exceed 10 MB:
-   ```
-   git lfs install
-   ```
-
-## Deploy Steps
-
-1. Initialize the Space repository (replace `<org>` and `<space>`):
-   ```
-   cd hf-space
-   git init
-   git remote add origin https://huggingface.co/spaces/<org>/<space>
-   ```
-2. Commit the deployment bundle:
-   ```
-   git add .
-   git commit -m "Initial Hugging Face Space"
-   git push origin main
-   ```
-3. In the Hugging Face UI:
-   - Set **Space SDK** to `Docker`.
-   - Pick hardware (`CPU Basic` is enough for CPU-only inference).
-   - Restart the Space if you tweak environment variables (e.g., secrets).
-
-Once the build succeeds, the FastAPI docs are accessible at `https://<space>.hf.space/docs` and the REST endpoints follow the paths defined in `api/main.py`.
-
-### Hosting model artifacts
-
-- Upload the serialized pipelines to the existing Hugging Face _model_ repo `deropxyz/AC02-ML`:
-  ```
-  hf upload --repo-id deropxyz/AC02-ML AC02-ML/model_pipeline_improved.joblib
-  hf upload --repo-id deropxyz/AC02-ML AC02-ML/model_pipeline.joblib
-  ```
-- `api/main.py` downloads the artifacts at startup using `HF_MODEL_REPO`, `HF_PRIMARY_MODEL`, and `HF_FALLBACK_MODEL` env vars (defaults target `deropxyz/AC02-ML`). If the model repo is private, add `HF_TOKEN` in the Space settings (Variables & secrets).
-- No large binaries are stored inside this repo; the runtime pulls them from the Hub cache.
-
-## Keeping the Space Updated
-
-- Copy any new code from `AC02-ML` into this folder, commit, and push.
-- When retraining, upload the new model artifact to the Hub model repo and update the env vars if filenames change.
+---
+title: AC02-ML
+emoji: ⚙️
+colorFrom: blue
+colorTo: green
+sdk: docker
+sdk_version: "latest"
+app_file: api/main.py
+pinned: false
+---
+
+# AC02-ML Predictive Maintenance API
+
+This Space deploys a Predictive Maintenance API using FastAPI and Docker.
+
+## API Endpoints
+
+- **POST `/predict`**: Get maintenance predictions for equipment
+- **POST `/predict_batch`**: Process multiple equipment data points
+- **GET `/docs`**: Interactive API documentation (FastAPI/Swagger)
+- **GET `/health`**: Health check endpoint
+
+## Model Information
+
+This API uses machine learning models trained for predictive maintenance:
+
+- Primary model: Enhanced pipeline with feature engineering
+- Fallback model: Basic model pipeline
+- Models are automatically downloaded from the Hugging Face Hub at startup
+
+## Usage
+
+Visit the `/docs` endpoint to interact with the API through the Swagger UI, or send POST requests to the prediction endpoints with equipment sensor data.
+
+## Technical Details
+
+- **Framework**: FastAPI
+- **Deployment**: Docker container
+- **Models**: Hosted on Hugging Face Model Hub
+- **Environment**: Python 3.9+
+
+For more information about deployment and setup, please refer to the project documentation.