input image base64 e update della risposta

README.md

@@ -10,3 +10,19 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Test the endpoint with Curl 
+```
+curl -X POST -F "file=@test_image.jpg" https://tuo-username-singlefacedetector.hf.space/detect_single_face
+```
+
+# Test the endpoint with python 
+
+```
+import requests
+
+url = "https://tuo-username-singlefacedetector.hf.space/detect_single_face"
+with open("test_image.jpg", "rb") as f:
+    response = requests.post(url, files={"file": f})
+print(response.json())
+```

app.py

@@ -1,13 +1,19 @@
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 from PIL import Image
 from ultralytics import YOLO
 import io
+import base64
 import torch
 
 app = FastAPI()
 
 # Carica il modello all'avvio (singleton per efficienza)
-model = YOLO("yolov8n.pt")  # Sostituisci con "yolov8n-face.pt" se disponibile
+model = YOLO("YoloV8-FaceDetection.pt")  # Sostituisci con "yolov8n-face.pt" se disponibile
+
+# Modello Pydantic per validare l'input Base64
+class ImageRequest(BaseModel):
+    image_base64: str
 
 def preprocess_image(image: Image.Image, size=(320, 320)):
     """Ridimensiona l'immagine per velocizzare l'inferenza"""
@@ -16,11 +22,11 @@ def preprocess_image(image: Image.Image, size=(320, 320)):
     return img
 
 @app.post("/detect_single_face")
-async def detect_single_face(file: UploadFile = File(...)):
+async def detect_single_face(request: ImageRequest):
     try:
-        # Leggi e preprocessa l'immagine
-        image_bytes = await file.read()
-        image = Image.open(io.BytesIO(image_bytes))
+        # Decodifica la stringa Base64
+        image_data = base64.b64decode(request.image_base64)
+        image = Image.open(io.BytesIO(image_data))
         image = preprocess_image(image)
 
         # Esegui l'inferenza con YOLOv8
@@ -35,10 +41,17 @@ async def detect_single_face(file: UploadFile = File(...)):
             if int(box.cls) == 0:  # "person" in yolov8n
                 face_count += 1
 
-        # Restituisci True solo se c'è esattamente 1 volto
-        return {"has_single_face": face_count == 1}
+        # Logica di risposta
+        if face_count == 1:
+            return {"has_single_face": True}
+        elif face_count > 1:
+            return {"has_single_face": False, "face_count": face_count}
+        else:
+            return {"has_single_face": False}
+
     except Exception as e:
-        return {"error": str(e)}
+        # In caso di errore (es. Base64 non valido), restituisci False
+        return {"has_single_face": False, "error": str(e)}
 
 # Avvio del server (Hugging Face lo gestisce automaticamente)
 if __name__ == "__main__":