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Name: geoinfer
Version: 0.0.1
Summary: Official Python SDK for the GeoInfer API
Project-URL: Homepage, https://geoinfer.com
Project-URL: Repository, https://github.com/geoinfer/geoinfer-sdk
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License-File: LICENSE
Keywords: coordinates,geoinfer,geolocation,image,prediction
Classifier: Development Status :: 4 - Beta
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Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
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Description-Content-Type: text/markdown

# GeoInfer Python SDK

The official Python client library for the [GeoInfer API](https://geoinfer.com) — AI-powered image geolocation without GPS, EXIF, or metadata.

[![PyPI version](https://img.shields.io/pypi/v/geoinfer)](https://pypi.org/project/geoinfer/)
[![Python](https://img.shields.io/pypi/pyversions/geoinfer)](https://pypi.org/project/geoinfer/)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue)](LICENSE)

---

## What is GeoInfer?

GeoInfer determines where a photo was taken by analysing its visual content alone — no GPS signal, no EXIF data, no metadata required. Results are delivered in under a second, with global coverage including remote regions, conflict zones, and GPS-denied environments.

The API exposes two model families:

| Model type | Output | Accuracy |
|---|---|---|
| **Global** | Geographic clusters with coordinates and radius | 1 km – 100 km, worldwide |
| **Accuracy** (regional) | Ranked coordinate predictions with confidence scores | Metre-level, within a specific region |

> **Note:** Accuracy models are region-specific and generally whitelisted — they are not available to the general public. Contact [geoinfer.com](https://geoinfer.com) to enquire about access.

---

## Requirements

- Python 3.9 or later
- A GeoInfer API key — get one at [geoinfer.com](https://geoinfer.com)

## Installation

```bash
pip install geoinfer
```

---

## Quick start

```python
from geoinfer import GeoInfer

client = GeoInfer(api_key="geo_...")

# List models your API key has access to
for m in client.predictions.models():
    print(m.model_id, m.model_type, f"{m.credits_per_use} credit(s)")

# Global model — returns geographic clusters
result = client.predictions.predict("photo.jpg", model_id="global_v0_1")
top = result.prediction.clusters[0]
print(top.location.name, top.center.latitude, top.center.longitude)

# Accuracy / regional model — returns ranked predictions
result = client.predictions.predict("photo.jpg", model_id="pais_vasco_v0_1")
top = result.prediction.top_prediction
if top:
    print(top.latitude, top.longitude, f"{top.confidence:.1%} confidence")
```

---

## Authentication

Pass your API key at client construction. It is sent as the `X-GeoInfer-Key` header on every request.

```python
client = GeoInfer(api_key="geo_...")
```

API keys can be managed from your dashboard at [geoinfer.com](https://geoinfer.com).

---

## Model access

Available models are determined by your API key — some models are public and some are whitelisted per account. `models()` returns only the models your key has access to:

```python
models = client.predictions.models()
for m in models:
    print(m.model_id, m.model_type, m.credits_per_use)
```

The model list is fetched on first use and cached for 5 minutes. Passing a `model_id` that is unknown to your account or disabled raises `InvalidModelError` immediately — before any prediction request is made.

---

## Async support

`AsyncGeoInfer` mirrors the synchronous client and is safe to use in any asyncio application:

```python
import asyncio
from geoinfer import AsyncGeoInfer

async def main():
    async with AsyncGeoInfer(api_key="geo_...") as client:
        result = await client.predictions.predict("photo.jpg", model_id="global_v0_1")
        top = result.prediction.clusters[0]
        print(top.location.name, top.center.latitude, top.center.longitude)

asyncio.run(main())
```

---

## File input

`predict()` accepts five input forms:

```python
from pathlib import Path

# URL — the SDK fetches the image and uploads it automatically
client.predictions.predict("https://example.com/photo.jpg", model_id="global_v0_1")

# Path string
client.predictions.predict("photo.jpg", model_id="global_v0_1")

# pathlib.Path
client.predictions.predict(Path("photo.jpg"), model_id="global_v0_1")

# Raw bytes (already loaded into memory)
with open("photo.jpg", "rb") as f:
    client.predictions.predict(f.read(), model_id="global_v0_1")

# Binary file-like object (streamed)
with open("photo.jpg", "rb") as f:
    client.predictions.predict(f, model_id="global_v0_1")
```

> URL inputs are fetched client-side and uploaded as bytes — the image is not stored or cached. Any `http://` or `https://` string is treated as a URL. File objects must be opened in binary mode (`"rb"`); passing a text-mode object raises `TypeError`.

Maximum file size: **10 MB**.

---

## Credits

Each prediction consumes credits according to the model used. Check your balance at any time:

```python
summary = client.credits.summary()
print(summary.summary.total_available)    # total credits available now
print(summary.subscription.remaining)     # subscription allowance remaining
print(summary.summary.topup_credits)      # one-time top-up balance
```

Credits consumed are reported on every `PredictionResponse`:

```python
result = client.predictions.predict("photo.jpg", model_id="global_v0_1")
print(f"Used {result.credits_consumed} credit(s). ID: {result.prediction_id}")
```

---

## Rate limits

Rate-limit metadata is attached to every `PredictionResponse`:

```python
result = client.predictions.predict("photo.jpg", model_id="global_v0_1")
rl = result.rate_limit
print(f"{rl.remaining} requests remaining, window resets in {rl.reset}s")
```

When the limit is exceeded `RateLimitError` is raised with a `retry_after` attribute:

```python
import time
from geoinfer import RateLimitError

try:
    result = client.predictions.predict("photo.jpg", model_id="global_v0_1")
except RateLimitError as e:
    if e.retry_after:
        time.sleep(e.retry_after)
```

---

## Error handling

All SDK exceptions inherit from `GeoInferError`.

| Exception | HTTP status | When raised |
|---|---|---|
| `AuthenticationError` | 401 | Missing or invalid API key |
| `InsufficientCreditsError` | 402 | Account has insufficient credits |
| `ForbiddenError` | 403 | Access denied to this resource |
| `FileTooLargeError` | 413 | Image exceeds the 10 MB limit |
| `InvalidFileTypeError` | 422 | File format is not supported |
| `RateLimitError` | 429 | Request rate limit exceeded |
| `InvalidModelError` | — | `model_id` is not in your account's model list or is disabled |
| `APIError` | other | Unexpected non-2xx response |

```python
from geoinfer import (
    GeoInferError,
    AuthenticationError,
    InsufficientCreditsError,
    InvalidModelError,
    RateLimitError,
)

try:
    result = client.predictions.predict("photo.jpg", model_id="global_v0_1")
except InvalidModelError as e:
    print(e.message)                  # includes a list of your available model IDs
except RateLimitError as e:
    if e.retry_after:
        time.sleep(e.retry_after)
except InsufficientCreditsError:
    print("Top up credits at geoinfer.com")
except AuthenticationError:
    print("Check your API key at geoinfer.com")
except GeoInferError as e:
    print(e.message_code, e.message)
```

---

## Context managers

Both clients support context managers for deterministic connection cleanup:

```python
# Sync
with GeoInfer(api_key="geo_...") as client:
    result = client.predictions.predict("photo.jpg", model_id="global_v0_1")

# Async
async with AsyncGeoInfer(api_key="geo_...") as client:
    result = await client.predictions.predict("photo.jpg", model_id="global_v0_1")
```

---

## Configuration

| Parameter | Default | Description |
|---|---|---|
| `api_key` | *(required)* | Your GeoInfer API key |
| `base_url` | `https://api.geoinfer.com` | Override the API base URL |
| `timeout` | `60` | HTTP request timeout in seconds |
| `model_cache_ttl` | `300` | Seconds to cache the model list before re-fetching |

---

## Type safety

All response objects are immutable frozen dataclasses. Narrow on `result.prediction.result_type` to get the specific prediction type:

```python
from geoinfer.types import CoordinatePredictionResult, AccuracyPredictionResult

result = client.predictions.predict("photo.jpg", model_id="global_v0_1")
if isinstance(result.prediction, CoordinatePredictionResult):
    print(result.prediction.clusters[0].location.name)
elif isinstance(result.prediction, AccuracyPredictionResult):
    print(result.prediction.top_prediction.confidence)
```

---

## License

Apache 2.0 — see [LICENSE](LICENSE).
