feat: add FastAPI web interface for GIS classification

README.md

@@ -10,8 +10,10 @@ gis-classification/
 ├── requirements.txt     # Dependencies
 ├── data/                # Input data folder
 ├── output/              # Classification results
+├── static/              # Web frontend
 └── src/
     ├── classifier.py    # Main classification pipeline
+    ├── api.py           # FastAPI web server
     ├── data/
     │   └── loader.py    # Data loading (GeoTIFF, Shapefile)
     ├── strategies/
@@ -26,8 +28,15 @@ gis-classification/
 pip install -r requirements.txt
 ```
 
+Or with uv:
+```bash
+uv pip install -r requirements.txt
+```
+
 ## Usage
 
+### CLI Mode
+
 1. Place your input files in `data/`:
    - `landsat.tif` - GeoTIFF from Landsat
    - `polygons.shp` - Shapefile with class labels
@@ -42,6 +51,31 @@ pip install -r requirements.txt
 python main.py
 ```
 
+### Web Interface
+
+Start the web server:
+
+```bash
+uvicorn src.api:app --host 0.0.0.0 --port 8000 --reload
+```
+
+Then open http://localhost:8000 in your browser.
+
+**Features:**
+- Upload GeoTIFF raster and Shapefile training data
+- Select classification strategy (Random Forest, SVM, Logistic Regression, MLE)
+- View training metrics (Accuracy, Cohen's Kappa)
+- Interactive map visualization with Leaflet
+- Download classified GeoTIFF results
+
+**API Endpoints:**
+- `GET /` - Web interface
+- `POST /train` - Train classifier with uploaded files
+- `POST /predict` - Run classification
+- `GET /result/{session_id}` - Get result metadata
+- `GET /result/{session_id}/download` - Download classified GeoTIFF
+- `GET /docs` - Interactive API documentation (Swagger UI)
+
 ## Adding Custom Classification Strategy
 
 Create a new class implementing `ClassificationStrategy`:
@@ -54,17 +88,17 @@ class MyCustomStrategy(ClassificationStrategy):
     def train(self, X: np.ndarray, y: np.ndarray) -> None:
         # Your training logic
         pass
-    
+
     def predict(self, X: np.ndarray) -> np.ndarray:
         # Your prediction logic
         pass
-    
+
     def predict_proba(self, X: np.ndarray) -> np.ndarray:
         pass
-    
+
     def get_params(self) -> dict:
         pass
-    
+
     @property
     def name(self) -> str:
         return "MyCustom"
@@ -78,4 +112,11 @@ STRATEGY = MyCustomStrategy()
 ## Output
 
 - `output/classified.tif` - Classified raster (GeoTIFF)
-- Console output with accuracy metrics
+- Console output with accuracy metrics (Accuracy, Cohen's Kappa)
+- Web interface visualization with interactive map
+
+## Metrics
+
+The classifier reports:
+- **Accuracy**: Overall classification accuracy
+- **Cohen's Kappa**: Agreement statistic accounting for chance (values > 0.8 indicate excellent agreement)