Deploy FastAPI to Hugging Face

README.md

@@ -1,168 +1,10 @@
-# 📱 Telecom Anomaly Detection System
-
-A production-ready anomaly detection system for telecommunications Call Data Records (CDR). This system identifies unusual patterns in subscriber behavior, ranging from potential fraud to technical malfunctions.
-
-## 🚀 How it Works
-
-The system operates as an automated pipeline that transforms raw CDR data into actionable security insights:
-
-1.  **Data Ingestion**: Reads CSV files from the `data/input` directory or direct CLI arguments.
-2.  **Validation & Cleaning**: Filters out invalid records, parses dates/times, and categorizes calls (e.g., voice vs. SMS, service vs. subscriber).
-3.  **Multi-Layered Detection**: Parallel analysis of each record across multiple specialized detector modules.
-4.  **Anomaly Aggregation**: Combines findings per subscriber and per record, calculating confidence scores and eliminating duplicates.
-5.  **Reporting**: Generates multiple output formats for different stakeholders (analysts, systems, executives).
-
 ---
-
-## 🔍 Core Detection Modules
-
-The system uses 6 primary detection strategies:
-
-| Detector | Logic | Use Case |
-| :--- | :--- | :--- |
-| **Duration** | Monitors for "infinite calls" or "flash calls" | Fraud, billing errors |
-| **Frequency** | Tracks calls per minute/hour/day | Automated dialing, SMS flooding |
-| **SMS** | Specifically analyzes SMS volume and rate | Spam, bulk-SMS marketing |
-| **Location** | Calculates velocity between consecutive activities | Account sharing, travel anomalies |
-| **IMEI** | Tracks hardware-to-subscriber changes | SIM swapping, device theft |
-| **Time Pattern** | Identifies activity during unusual (night) hours | Targeted attacks, outlier behavior |
-
+title: Teleprocessinggh
+emoji: 🏢
+colorFrom: pink
+colorTo: yellow
+sdk: docker
+pinned: false
 ---
 
-## 🛠 Architecture
-
-The project follows a modular orchestrator-based architecture:
-
--   **`TelecomAnomalyDetector` (Core)**: The central brain that coordinates the reader, validator, and all detector modules.
--   **`TelecomDataReader`**: Handles high-performance batch reading of large CSV files.
--   **`DataValidator`**: Ensures MSISDN formats, geographical coordinates, and timestamps are valid.
--   **`ResultExporter`**: Formats findings into JSON (for APIs), CSV (for Excel), and Markdown (for reports).
-
----
-
-## 📊 Data Requirements
-
-The system expects CSV files with the following core columns:
-
--   `msisdn`: Subscriber identification (e.g., `233XXXXXXXXX`)
--   `usage_type`: `VOICE` or `SMS`
--   `duration`: Call length in seconds
--   `latitude` / `longitude`: Geographical coordinates of the activity
--   `date` / `time`: Activity timestamps (`YYYY-MM-DD` and `HH:MM:SS`)
--   `imei`: Device hardware identifier
-
----
-
-## ⚙️ Configuration
-
-Detection sensitivity can be tuned in `config/settings.py` or via `TelecomConfig` overrides:
-
-```python
-DEFAULT_THRESHOLDS = {
-    'extreme_call_threshold': 10800.0,  # 3 hours
-    'max_calls_per_hour': 60,           # >1 call/minute
-    'max_location_change_speed': 1000.0,# km/h (impossible travel)
-    'night_hours_start': 23,            # Start of "unusual" hours
-}
-```
-
----
-
-## 💻 Installation & Usage
-
-### Setup with uv
-The project uses [uv](https://github.com/astral-sh/uv) for high-performance dependency management.
-
-```bash
-# Clone and enter the repository
-git clone https://github.com/yourusername/telecom-anomaly-detection.git
-cd telecom-anomaly-detection
-
-# Sync dependencies and create virtual environment
-uv sync
-```
-
-### Running the Analysis
-Place your CSV files in `data/input/` and run using `uv run`:
-```bash
-uv run python -m telecom_anomaly.main
-```
-
-Or specify files directly:
-```bash
-uv run python -m telecom_anomaly.main data/input/records_2023_10.csv
-```
-
-### Development
-```bash
-# Run tests
-uv run pytest
-
-# Format code
-uv run black .
-
-# Lint code
-uv run flake8 .
-```
-
-## 🌐 API (FastAPI)
-
-The project includes a FastAPI-based REST API for programmatic access to the anomaly detection engine.
-
-### Running the API
-```bash
-uv run python -m uvicorn telecom_anomaly.api:app --reload
-```
-The API will be available at `http://127.0.0.1:8000`.
-
-### API Documentation
-Interactive documentation is available at:
-- Swagger UI: `http://127.0.0.1:8000/docs`
-- ReDoc: `http://127.0.0.1:8000/redoc`
-
-### Key Endpoints
-
-#### `POST /analyze`
-Upload a CDR CSV file for real-time analysis.
-
-**Example using curl:**
-```bash
-curl -X 'POST' \
-  'http://127.0.0.1:8000/analyze' \
-  -H 'accept: application/json' \
-  -H 'Content-Type: multipart/form-data' \
-  -F 'file=@data/input/sample_cdr.csv'
-```
-
-**Response Example:**
-```json
-{
-  "filename": "sample_cdr.csv",
-  "statistics": {
-    "total_rows": 1000,
-    "rows_with_anomalies": 15,
-    "total_anomalies_detected": 22,
-    "anomaly_rate": 1.5,
-    "unique_subscribers": 12,
-    "anomaly_types": {
-      "short_duration": 10,
-      "impossible_speed": 5
-    }
-  },
-  "anomalies": [
-    {
-      "row_index": 42,
-      "column_name": "duration",
-      "value": 1.2,
-      "detection_method": "short_duration",
-      "score": 2.5,
-      "threshold": 3.0,
-      "confidence": 0.75,
-      "msisdn": "233123456789"
-    }
-  ]
-}
-```
-
-#### `GET /health`
-Returns the health status of the API.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

READ_ME.md

@@ -1,10 +0,0 @@
----
-title: Teleprocessinggh
-emoji: 🏢
-colorFrom: pink
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference