Create app.py

app.py

@@ -0,0 +1,66 @@
+import os
+import requests
+import cv2
+import numpy as np
+import torch
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from detectron2 import model_zoo
+from PIL import Image
+import io
+
+app = FastAPI(title="Roof Segmentation API")
+
+# -----------------------------
+# 1. Download model from Google Drive
+# -----------------------------
+MODEL_URL = "https://drive.google.com/uc?export=download&id=1bazIVYG0CYMubDLoMu5pgH6ArC1sayzg"
+MODEL_PATH = "model_final.pth"
+
+if not os.path.exists(MODEL_PATH):
+    print("⏬ Downloading model...")
+    r = requests.get(MODEL_URL, allow_redirects=True)
+    open(MODEL_PATH, 'wb').write(r.content)
+    print("✅ Model downloaded!")
+
+# -----------------------------
+# 2. Configure Detectron2
+# -----------------------------
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
+cfg.MODEL.WEIGHTS = MODEL_PATH
+cfg.MODEL.DEVICE = "cpu"   # Hugging Face Spaces often has no GPU
+
+predictor = DefaultPredictor(cfg)
+
+# -----------------------------
+# 3. API Endpoint
+# -----------------------------
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    # Read image
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents)).convert("RGB")
+    image = np.array(image)[:, :, ::-1]  # to BGR for OpenCV/Detectron2
+
+    # Run inference
+    outputs = predictor(image)
+    instances = outputs["instances"].to("cpu")
+
+    results = []
+    if instances.has("pred_masks"):
+        masks = instances.pred_masks.numpy()
+        boxes = instances.pred_boxes.tensor.numpy()
+        scores = instances.scores.numpy()
+
+        for i in range(len(masks)):
+            results.append({
+                "box": boxes[i].tolist(),
+                "score": float(scores[i])
+            })
+
+    return JSONResponse({"predictions": results})