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README.md

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----
-title: SAM
-emoji: 📉
-colorFrom: red
-colorTo: pink
-sdk: docker
-pinned: false
-short_description: SAM
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SAM FastAPI for Hugging Face Space
+
+This is a FastAPI wrapper for Meta's Segment Anything Model (SAM), ready to deploy on Hugging Face Spaces.
+
+## Setup
+
+1. Upload the `sam_vit_b.pth` model checkpoint to the root of your Space manually.
+2. Hugging Face will automatically install dependencies and run the app.
+3. Use the `/segment` endpoint to perform segmentation.
+

main.py

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+from fastapi import FastAPI, File, UploadFile
+from segment_anything import sam_model_registry, SamPredictor
+from PIL import Image
+import numpy as np
+import torch
+import io
+
+app = FastAPI()
+
+# Load SAM Model
+sam_checkpoint = "sam_vit_b.pth"  # Add the weights file manually in the Space
+model_type = "vit_b"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device)
+predictor = SamPredictor(sam)
+
+@app.post("/segment")
+async def segment_image(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image_np = np.array(image)
+
+    predictor.set_image(image_np)
+
+    input_point = np.array([[100, 100]])
+    input_label = np.array([1])
+
+    masks, scores, _ = predictor.predict(
+        point_coords=input_point,
+        point_labels=input_label,
+        multimask_output=False
+    )
+
+    return {
+        "score": float(scores[0]),
+        "mask": masks[0].tolist()
+    }

requirements.txt

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+fastapi
+uvicorn
+opencv-python
+numpy
+torch
+torchvision
+git+https://github.com/facebookresearch/segment-anything.git
+pillow

sam_vit_b.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec2df62732614e57411cdcf32a23ffdf28910380d03139ee0f4fcbe91eb8c912
+size 375042383