README.md

---
title: My Fastapi Endpoint
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: docker
pinned: false
license: mit
---

# Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

title: My FastAPI App
emoji: 🌍
colorFrom: red
colorTo: red
sdk: docker
pinned: false
license: mit
short_description: This is a taxi predictor!

---

# FastAPI Taxi Trip Duration Prediction API

This project provides a FastAPI-based REST API for predicting taxi trip durations in different cities using pre-trained Ridge regression models.

## How to Use

### 1. Running the API

- **Locally:**  
  Start the server with:
  ```sh
  uvicorn main:app --host 0.0.0.0 --port 7860
  ```
- **On Hugging Face Spaces:**  
  The API will be available at  
  `https://<your-username>-<your-space-name>.hf.space/predict`

### 2. Sending a Prediction Request

You can send a POST request to the `/predict` endpoint using any HTTP client (such as `curl`, Postman, or Python's `requests` library).

#### Example Python snippet

```python
import requests

API_URL = "https://<your-username>-<your-space-name>.hf.space/predict"
# If your Space is private, uncomment and set your token:
# HF_TOKEN = "hf_xxx..."
# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
headers = {}

data = {
    "vendor_id": "Bogotá UberX",
    "dist_meters": 18.976,
    "wait_sec": 1640,
    "geodetic_dist": 15.439039,
    "mean_velocity": 17.172851,
    "is_rush_hour": False,
    "model_name": "bog"
}

response = requests.post(API_URL, json=data, headers=headers)
print(response.json())
```

### 3. Datapoint Format

The API expects a JSON object with the following fields:

| Field         | Type   | Example Value  | Description                                      |
| ------------- | ------ | -------------- | ------------------------------------------------ |
| vendor_id     | string | "Bogotá UberX" | The taxi vendor or service name                  |
| dist_meters   | float  | 18.976         | Distance of the trip in meters                   |
| wait_sec      | float  | 1640           | Waiting time in seconds                          |
| geodetic_dist | float  | 15.439039      | Geodetic (straight-line) distance                |
| mean_velocity | float  | 17.172851      | Mean velocity during the trip                    |
| is_rush_hour  | bool   | false          | Whether the trip occurred during rush hour       |
| model_name    | string | "bog"          | Which model to use: `"bog"`, `"mex"`, or `"uio"` |

**Example JSON datapoint:**

```json
{
  "vendor_id": "Bogotá UberX",
  "dist_meters": 18.976,
  "wait_sec": 1640,
  "geodetic_dist": 15.439039,
  "mean_velocity": 17.172851,
  "is_rush_hour": false,
  "model_name": "bog"
}
```

### 4. Response Format

The API will return a JSON response like:

```json
{
  "trip_duration": 123.45,
  "model_used": "bog",
  "message": "Inference successful using BOG model."
}
```

---

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