revert to fiel

README.md

@@ -12,18 +12,22 @@ 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/segment" \
+curl -X POST "https://alexgenovese-segmentation.hf.space/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/segment" \
+curl -X POST "https://alexgenovese-segmentation.hf.space/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
+curl -X POST -F "file=@woman_with_bag.jpeg" https://alexgenovese-segmentation.hf.space/api/segment
+
+curl -X POST https://alexgenovese-segmentation.hf.space/segment \
+-H "Content-Type: application/json" \
+-d "{\"image_base64\": \"$(base64 woman_with_bag.jpeg)\"}"

app-base64.py

@@ -0,0 +1,82 @@
+from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
+from transformers import SamModel, SamProcessor
+import torch
+from PIL import Image
+import numpy as np
+import io
+import base64
+
+class ImageRequest(BaseModel):
+    image_base64: str
+
+# Inizializza l'app FastAPI
+app = FastAPI()
+
+# Carica il modello e il processore SAM
+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
+    inputs = processor(image, return_tensors="pt").to("cpu")
+    
+    # Inferenza
+    with torch.no_grad():
+        outputs = model(**inputs, multimask_output=False)
+    
+    # Post-processa la maschera
+    mask = processor.image_processor.post_process_masks(
+        outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
+    )[0][0].cpu().numpy()
+    
+    # Converti la maschera in immagine
+    mask_img = Image.fromarray((mask * 255).astype(np.uint8))
+    
+    # Converti la maschera in base64 per la risposta
+    buffered = io.BytesIO()
+    mask_img.save(buffered, format="PNG")
+    mask_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    
+    # Annotazioni
+    annotations = {"mask": mask.tolist(), "label": "object"}
+    
+    return mask_base64, annotations
+
+# Endpoint API
+# @app.post("/segment")
+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__":
+#     import uvicorn
+#     uvicorn.run(app, host="0.0.0.0", port=7860)

app.py

@@ -1,5 +1,4 @@
 from fastapi import FastAPI, File, UploadFile
-from pydantic import BaseModel
 from transformers import SamModel, SamProcessor
 import torch
 from PIL import Image
@@ -7,9 +6,6 @@ import numpy as np
 import io
 import base64
 
-class ImageRequest(BaseModel):
-    image_base64: str
-
 # Inizializza l'app FastAPI
 app = FastAPI()
 
@@ -18,16 +14,6 @@ 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
@@ -57,24 +43,19 @@ def segment_image(image: Image.Image):
 
 # Endpoint API
 @app.post("/segment")
-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}
+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
+    }
 
 # Per compatibilità con Hugging Face Spaces (Uvicorn viene gestito automaticamente)
 if __name__ == "__main__":