update with base64

README.md

@@ -12,13 +12,18 @@ pinned: false
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
-curl -X POST "https://alexgenovese-segmentation.hf.space/api/predict" \
+curl -X POST "https://alexgenovese-segmentation.hf.space/api/segment" \
   -F "data=[{\"type\": \"image\", \"value\": null}]" \
   -F "data=@woman_with_bag.jpeg" \
   -H "Content-Type: multipart/form-data" \
   -o response.json
 
 
-  curl -X POST "https://alexgenovese-segmentation.hf.space/api/predict" \
-  -F "data=[{\"type\": \"image\", \"value\": null}]" \
-  -F "data=@woman_with_bag.jpeg" \
+curl -X POST "https://alexgenovese-segmentation.hf.space/api/segment" \
+  -H "accept: application/json" \
+  -H "Content-Type: multipart/form-data" \
+  -F "file=@woman_with_bag.jpeg"
+
+
+
+curl -X POST -F "file=@woman_with_bag.jpeg" https://alexgenovese-segmentation.hf.space/api/segment

app.py

@@ -1,4 +1,5 @@
 from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
 from transformers import SamModel, SamProcessor
 import torch
 from PIL import Image
@@ -6,6 +7,9 @@ import numpy as np
 import io
 import base64
 
+class ImageRequest(BaseModel):
+    image_base64: str
+
 # Inizializza l'app FastAPI
 app = FastAPI()
 
@@ -14,6 +18,16 @@ model = SamModel.from_pretrained("facebook/sam-vit-base")
 processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
 model.to("cpu")  # Usa CPU per il free tier
 
+@app.get("/health")
+async def health_check():
+    return {"status": "ok"}
+
+def preprocess_image(image: Image.Image, size=(320, 320)):
+    """Ridimensiona l'immagine per velocizzare l'inferenza"""
+    img = image.convert("RGB")
+    img = img.resize(size, Image.LANCZOS)  # 320x320 è veloce su CPU
+    return img
+
 # Funzione per segmentare l'immagine
 def segment_image(image: Image.Image):
     # Prepara l'input per SAM
@@ -43,19 +57,24 @@ def segment_image(image: Image.Image):
 
 # Endpoint API
 @app.post("/segment")
-async def segment_endpoint(file: UploadFile = File(...)):
-    # Leggi l'immagine caricata
-    image_data = await file.read()
-    image = Image.open(io.BytesIO(image_data)).convert("RGB")
-    
-    # Segmenta l'immagine
-    mask_base64, annotations = segment_image(image)
-    
-    # Restituisci la risposta
-    return {
-        "mask": f"data:image/png;base64,{mask_base64}",
-        "annotations": annotations
-    }
+async def segment_endpoint(file: ImageRequest):
+    try:
+        # Decodifica la stringa Base64
+        image_data = base64.b64decode(file.image_base64)
+        image = Image.open(io.BytesIO(image_data))
+        image = preprocess_image(image)
+        
+        # Segmenta l'immagine
+        mask_base64, annotations = segment_image(image)
+        
+        # Restituisci la risposta
+        return {
+            "mask": f"data:image/png;base64,{mask_base64}",
+            "annotations": annotations
+        }
+    except Exception as e:
+        # In caso di errore (es. Base64 non valido), restituisci False
+        return {"output": False, "error": str(e), "debug": file}
 
 # Per compatibilità con Hugging Face Spaces (Uvicorn viene gestito automaticamente)
 if __name__ == "__main__":