# main.py from huggingface_hub import hf_hub_download from fastapi import FastAPI, HTTPException, Request from fastapi.responses import JSONResponse, HTMLResponse import tensorflow as tf import numpy as np import base64 import logging import os import sys import time from datetime import datetime from logging.handlers import RotatingFileHandler from inference.forest import predict_forest, build_input_tensor from schemas import PredictRequest, PredictResponse # ============================================================================= # LOGGING CONFIGURATION # ============================================================================= os.makedirs("logs", exist_ok=True) logger = logging.getLogger("forest_segmentation") logger.setLevel(logging.DEBUG) console_handler = logging.StreamHandler(sys.stdout) console_handler.setLevel(logging.DEBUG) console_handler.setFormatter( logging.Formatter( "%(asctime)s | %(levelname)-8s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S" ) ) file_handler = RotatingFileHandler( "logs/server.log", maxBytes=10_000_000, backupCount=5, encoding="utf-8" ) file_handler.setFormatter(console_handler.formatter) logger.addHandler(console_handler) logger.addHandler(file_handler) logger.info("=" * 80) logger.info("FOREST SEGMENTATION SERVER STARTING") logger.info("=" * 80) # ============================================================================= # INVERSION DETECTION # ============================================================================= def detect_inversion(image_stack, confidence_map, ndvi_threshold=0.3): """ Detect if model output is inverted using NDVI correlation. image_stack: (H, W, 9) confidence_map: (H, W) """ ndvi = image_stack[:, :, 6] # NDVI channel vegetation_mask = ndvi > ndvi_threshold veg_conf = ( confidence_map[vegetation_mask].mean() if vegetation_mask.any() else 0.5 ) non_veg_conf = ( confidence_map[~vegetation_mask].mean() if (~vegetation_mask).any() else 0.5 ) is_inverted = non_veg_conf > veg_conf correlation = veg_conf - non_veg_conf return bool(is_inverted), float(correlation) # ============================================================================= # FASTAPI APP # ============================================================================= app = FastAPI( title="Forest Segmentation API", description="Landsat 8 Forest Segmentation", version="1.0.0" ) IMG_SIZE = 256 LANDSAT_BANDS = [ "Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "NDVI", "NDWI", "NBR" ] # ============================================================================= # MIDDLEWARE # ============================================================================= @app.middleware("http") async def log_requests(request: Request, call_next): start = time.time() response = await call_next(request) duration = time.time() - start logger.info( f"{request.method} {request.url.path} | " f"{response.status_code} | {duration:.3f}s" ) return response # ============================================================================= # ROOT ENDPOINT # ============================================================================= @app.get("/", response_class=HTMLResponse) def root(): """Serve a simple HTML page with API info.""" return """ Forest Segmentation API

🌲 Forest Segmentation API

Landsat 8 Forest Segmentation Model

API Endpoints

Health Check
GET /health
Returns API status

Predict
POST /predict
Send Landsat bands for forest segmentation

API Docs
GET /docs
Interactive Swagger UI

Status

✓ API is running

""" # ============================================================================= # HEALTH # ============================================================================= @app.get("/health") def health(): return { "status": "healthy", "timestamp": datetime.utcnow().isoformat() } # ============================================================================= # PREDICT ENDPOINT (FIXED - CONTINUOUS VALUES) # ============================================================================= @app.post("/predict", response_model=PredictResponse) def predict(payload: PredictRequest): try: logger.info("[PREDICT] Request received") if not payload.bands: raise ValueError("No bands provided") # --------------------------------------------------------------------- # Decode bands # --------------------------------------------------------------------- decoded_bands = {} for band, data in payload.bands.items(): if isinstance(data, str): raw = base64.b64decode(data) arr = np.frombuffer(raw, dtype=np.float32) side = int(np.sqrt(arr.size)) decoded_bands[band] = arr.reshape((side, side)) else: decoded_bands[band] = np.array(data, dtype=np.float32) logger.info(f"[PREDICT] Decoded {len(decoded_bands)} bands") # --------------------------------------------------------------------- # Build input tensor # --------------------------------------------------------------------- input_tensor = build_input_tensor(decoded_bands) # (1, H, W, 9) input_stack = input_tensor[0] # (H, W, 9) # --------------------------------------------------------------------- # Run model (raw confidence) # --------------------------------------------------------------------- MODEL_REPO = "prshntdxt/Forest_Segmentation_Best" MODEL_FILE = "Forest_Segmentation_Best.keras" MODEL_PATH = hf_hub_download( repo_id=MODEL_REPO, filename=MODEL_FILE, ) model = tf.keras.models.load_model( MODEL_PATH, compile=False ) confidence_map = model.predict( input_tensor, verbose=0 )[0, :, :, 0] # Log raw model output stats logger.info( f"[MODEL OUTPUT] Raw confidence: min={confidence_map.min():.4f}, " f"max={confidence_map.max():.4f}, mean={confidence_map.mean():.4f}" ) # --------------------------------------------------------------------- # Inversion detection & correction # --------------------------------------------------------------------- is_inverted, corr = detect_inversion( input_stack, confidence_map ) if is_inverted: logger.warning( f"[INVERSION] Detected | NDVI correlation={corr:.4f} | FIX APPLIED" ) corrected_conf = 1.0 - confidence_map else: logger.info( f"[INVERSION] Not detected | NDVI correlation={corr:.4f}" ) corrected_conf = confidence_map # --------------------------------------------------------------------- # Create masks (CONTINUOUS values for density visualization) # --------------------------------------------------------------------- # Use continuous confidence scaled to 0-255 (NOT binary!) mask_255 = (corrected_conf * 255).astype(np.uint8) inverted_mask_255 = (255 - mask_255).astype(np.uint8) # Calculate stats using threshold for percentage forest_percentage = float((corrected_conf > 0.5).sum() / corrected_conf.size * 100) forest_confidence = float(corrected_conf.mean()) # Log mask stats to verify continuous values logger.info( f"[MASK] Range: [{mask_255.min()}, {mask_255.max()}] | " f"Unique values: {len(np.unique(mask_255))}" ) logger.info( f"[PREDICT] Forest={forest_percentage:.2f}% | " f"Confidence={forest_confidence:.4f}" ) # --------------------------------------------------------------------- # Response # --------------------------------------------------------------------- return { "mask": mask_255.flatten().tolist(), "inverted_mask": inverted_mask_255.flatten().tolist(), "forest_percentage": forest_percentage, "forest_confidence": forest_confidence, "mean_prediction": forest_confidence, "classes": {"forest": 1, "non_forest": 0}, "model_info": { "name": "Forest_Segmentation_Best", "bands": LANDSAT_BANDS }, "debug": { "was_inverted": is_inverted, "inversion_correlation": corr, "mask_min": int(mask_255.min()), "mask_max": int(mask_255.max()), "unique_values": int(len(np.unique(mask_255))) } } except ValueError as e: logger.error(f"[PREDICT] Validation error: {e}") raise HTTPException(status_code=400, detail=str(e)) except Exception as e: logger.exception("[PREDICT] Inference failed") raise HTTPException(status_code=500, detail=str(e)) # ============================================================================= # STARTUP / SHUTDOWN # ============================================================================= @app.on_event("startup") async def startup(): logger.info("=" * 80) logger.info("SERVER READY") logger.info("=" * 80) @app.on_event("shutdown") async def shutdown(): logger.info("=" * 80) logger.info("SERVER SHUTDOWN") logger.info("=" * 80)