""" Integration Example: How to replace SAM predictor calls with HF Space calls This shows you how to modify your app.py to use the HuggingFace Space """ import requests import json import base64 from io import BytesIO from PIL import Image import numpy as np from typing import Tuple, Optional # Your Space URL (update after deployment) MEDSAM_SPACE_URL = "https://YOUR_USERNAME-medsam-inference.hf.space/api/predict" class MedSAMSpacePredictor: """ Drop-in replacement for SamPredictor that calls HuggingFace Space Usage: # OLD CODE: from segment_anything import SamPredictor predictor = SamPredictor(sam) predictor.set_image(image_array) masks, scores, _ = predictor.predict(point_coords=..., point_labels=...) # NEW CODE: predictor = MedSAMSpacePredictor(MEDSAM_SPACE_URL) predictor.set_image(image_array) masks, scores, _ = predictor.predict(point_coords=..., point_labels=...) """ def __init__(self, space_url: str): self.space_url = space_url self.image_array = None print(f"✓ MedSAM Space Predictor initialized: {space_url}") def set_image(self, image: np.ndarray): """Set the image for segmentation (matches SAM interface)""" self.image_array = image def predict( self, point_coords: np.ndarray, point_labels: np.ndarray, multimask_output: bool = True, return_logits: bool = False ) -> Tuple[np.ndarray, np.ndarray, Optional[np.ndarray]]: """ Predict masks using HuggingFace Space (matches SAM interface) Args: point_coords: nx2 array of point coordinates [[x, y], ...] point_labels: n array of point labels [1, 0, ...] multimask_output: whether to return multiple masks return_logits: (ignored) kept for compatibility Returns: masks: (N, H, W) array of boolean masks scores: (N,) array of confidence scores logits: None (not supported via API) """ if self.image_array is None: raise ValueError("Must call set_image() before predict()") try: # Convert numpy array to base64 image = Image.fromarray(self.image_array) buffered = BytesIO() image.save(buffered, format="PNG") img_base64 = base64.b64encode(buffered.getvalue()).decode() # Prepare points JSON points_json = json.dumps({ "coords": point_coords.tolist(), "labels": point_labels.tolist(), "multimask_output": multimask_output }) # Call Space API response = requests.post( self.space_url, json={ "data": [ f"data:image/png;base64,{img_base64}", points_json ] }, timeout=120 # 2 minute timeout ) if response.status_code != 200: raise Exception(f"API returned status {response.status_code}: {response.text}") # Parse result result = response.json() # Gradio wraps output in data array if "data" in result and len(result["data"]) > 0: output_json = result["data"][0] else: raise Exception("Unexpected API response format") output = json.loads(output_json) if not output.get('success', False): raise Exception(output.get('error', 'Unknown error')) # Convert masks back to numpy arrays masks = [] for mask_data in output['masks']: mask = np.array(mask_data['mask_data'], dtype=bool) masks.append(mask) masks = np.array(masks) scores = np.array(output['scores']) return masks, scores, None except requests.exceptions.Timeout: raise Exception("MedSAM Space API timeout (>120s)") except requests.exceptions.RequestException as e: raise Exception(f"MedSAM Space API request failed: {str(e)}") except Exception as e: raise Exception(f"MedSAM Space API error: {str(e)}") # ============================================================================ # Example: How to modify your app.py # ============================================================================ def example_modification(): """ Shows how to modify your segment endpoint in app.py """ # At the top of app.py, add: print("# Add to imports:") print("from integration_example import MedSAMSpacePredictor") print() # Replace SAM initialization: print("# Replace SAM initialization:") print(""" # OLD: sam = sam_model_registry["vit_b"](checkpoint="models/sam_vit_h_4b8939.pth") sam.to(device=device) sam_predictor = SamPredictor(sam) # NEW: sam_predictor = MedSAMSpacePredictor( "https://YOUR_USERNAME-medsam-inference.hf.space/api/predict" ) """) print() # Usage in endpoint: print("# Usage in endpoint (NO CHANGES NEEDED!):") print(""" @app.route('/api/segment', methods=['POST']) def segment_with_sam(): # ... your existing code ... # This works exactly the same! sam_predictor.set_image(image_array) masks, scores, _ = sam_predictor.predict( point_coords=np.array([[x, y]]), point_labels=np.array([1]), multimask_output=True ) # Get the best mask best_mask = masks[np.argmax(scores)] # ... rest of your code ... """) # ============================================================================ # Complete integration example # ============================================================================ def integrate_with_your_backend(space_url: str): """ Complete code snippet to add to your app.py Save this as: backend/medsam_space_client.py Then import in app.py """ code = f''' # File: backend/medsam_space_client.py """Client for MedSAM HuggingFace Space""" import requests import json import base64 from io import BytesIO from PIL import Image import numpy as np MEDSAM_SPACE_URL = "{space_url}" class MedSAMSpacePredictor: """Drop-in replacement for SAM predictor using HF Space""" def __init__(self, space_url): self.space_url = space_url self.image_array = None def set_image(self, image): self.image_array = image def predict(self, point_coords, point_labels, multimask_output=True, **kwargs): if self.image_array is None: raise ValueError("Must call set_image() first") # Convert to base64 img = Image.fromarray(self.image_array) buf = BytesIO() img.save(buf, format="PNG") img_b64 = base64.b64encode(buf.getvalue()).decode() # Call API points_json = json.dumps({{ "coords": point_coords.tolist(), "labels": point_labels.tolist(), "multimask_output": multimask_output }}) resp = requests.post( self.space_url, json={{"data": [f"data:image/png;base64,{{img_b64}}", points_json]}}, timeout=120 ) result = json.loads(resp.json()["data"][0]) if not result["success"]: raise Exception(result.get("error", "API error")) # Convert back to numpy masks = np.array([np.array(m["mask_data"], dtype=bool) for m in result["masks"]]) scores = np.array(result["scores"]) return masks, scores, None # Usage in app.py: # ---------------- # from medsam_space_client import MedSAMSpacePredictor # # # Replace: # # sam_predictor = SamPredictor(sam) # # With: # sam_predictor = MedSAMSpacePredictor(MEDSAM_SPACE_URL) # # # Everything else stays the same! ''' print(code) return code if __name__ == "__main__": print("=" * 80) print("MedSAM HuggingFace Space Integration Guide") print("=" * 80) print() example_modification() print() print("=" * 80) print("Complete Integration Code") print("=" * 80) print() integrate_with_your_backend("https://YOUR_USERNAME-medsam-inference.hf.space/api/predict")