Update app.py

app.py

@@ -1,31 +1,37 @@
-from fastapi import FastAPI, File, UploadFile
-from ultralytics import YOLO
-from huggingface_hub import hf_hub_download
-import io
-from PIL import Image
-
-app = FastAPI()
-
-# Download your YOLO model from your model repo
-model_path = hf_hub_download("devsdevline/soccer-homography-yolo", "best.pt")
-model = YOLO(model_path)
-
-@app.get("/")
-def root():
-    return {"status": "API running", "model": "soccer-homography-yolo"}
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    # Read uploaded image
-    image = Image.open(io.BytesIO(await file.read())).convert("RGB")
-    results = model(image)
-
-    detections = []
-    for box in results[0].boxes:
-        detections.append({
-            "class_id": int(box.cls[0]),
-            "confidence": float(box.conf[0]),
-            "bbox": box.xyxy[0].tolist()
-        })
-
-    return {"detections": detections}
+from fastapi import FastAPI, UploadFile, File
+from ultralytics import YOLO
+from PIL import Image
+import io
+import torch
+
+app = FastAPI(title="YOLO API - Football Model")
+
+# Load model from local file
+try:
+    model = YOLO("best.pt")
+    print("✅ Loaded best.pt model")
+except Exception as e:
+    print("⚠️ best.pt not found, trying last.pt:", e)
+    model = YOLO("last.pt")
+
+@app.get("/")
+def home():
+    return {"message": "YOLO Football Model API is running!"}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents)).convert("RGB")
+
+    # Run inference
+    results = model(image)
+
+    detections = []
+    for box in results[0].boxes:
+        detections.append({
+            "class": int(box.cls),
+            "confidence": float(box.conf),
+            "bbox": box.xyxy[0].tolist()
+        })
+
+    return {"detections": detections}