Revert "test without exec"

This reverts commit 7f467975c1508eb55afa74a02180964fb26ccd63.

app.py

@@ -317,7 +317,8 @@ def decrypt_output(user_id):
         user_id (int): The current user's ID.
 
     Returns:
-        str: The decrypted output message
+        bool: The decrypted output (True if seizure detected, False otherwise)
+
     """
     if user_id == "":
         raise gr.Error("Please generate the private key first.")
@@ -332,35 +333,13 @@ def decrypt_output(user_id):
     with encrypted_output_path.open("rb") as encrypted_output_file:
         encrypted_output = encrypted_output_file.read()
 
-    logger.debug(f"Encrypted output size: {len(encrypted_output)} bytes")
-    logger.debug(f"Encrypted output (first 100 bytes): {encrypted_output[:100].hex()}")
-
-    if not encrypted_output:
-        raise gr.Error("The encrypted output is empty. Please try running the FHE execution again.")
-
     # Retrieve the client API
     client = get_client(user_id)
 
     # Deserialize, decrypt and post-process the encrypted output
-    try:
-        decrypted_output = client.deserialize_decrypt_post_process(encrypted_output)
-        
-        # The decrypted output should be a 1D array with 2 elements
-        if isinstance(decrypted_output, np.ndarray) and decrypted_output.shape == (2,):
-            predicted_class = np.argmax(decrypted_output)
-            confidence = decrypted_output[predicted_class]
-            result = "Seizure detected" if predicted_class == 1 else "No seizure detected"
-            return f"{result} (Confidence: {confidence:.2f})"
-        else:
-            logger.error(f"Unexpected decrypted output format: {decrypted_output}")
-            raise ValueError("Unexpected output format from the model")
+    decrypted_output = client.deserialize_decrypt_post_process(encrypted_output)
 
-    except RuntimeError as e:
-        logger.error(f"Error during deserialization: {str(e)}")
-        raise gr.Error("Failed to deserialize the encrypted output. The data might be corrupted or in an unexpected format.")
-    except Exception as e:
-        logger.error(f"Unexpected error during decryption: {str(e)}")
-        raise gr.Error(f"An unexpected error occurred during decryption: {str(e)}")
+    return "Seizure detected" if decrypted_output else "No seizure detected"
 
 def resize_img(img, width=256, height=256):
     """Resize the image."""

client_server_interface.py

@@ -146,5 +146,6 @@ class FHEClient:
         output = self.client.decrypt(encrypted_output)
 
         # Post-process the output (if needed)
-        # Assuming the output is already in the correct format (2-element array)
-        return output
+        seizure_detected = self.seizure_detector.post_processing(output)
+
+        return seizure_detected

server.py

@@ -10,7 +10,6 @@ from fastapi.responses import JSONResponse, Response
 from fastapi.middleware.cors import CORSMiddleware
 
 from concrete.ml.deployment import FHEModelServer
-import numpy as np
 
 import gc
 
@@ -101,24 +100,17 @@ def run_fhe(user_id: str = Form()):
 
         # Run the FHE execution
         start = time.time()
-        # try:
-        #     encrypted_output = FHE_SERVER.run(encrypted_image, evaluation_key)
-        # except MemoryError:
-        #     logger.error("FHE execution failed due to insufficient memory")
-        #     raise HTTPException(status_code=503, detail="Insufficient memory during FHE execution")
-        # except Exception as e:
-        #     logger.error(f"FHE execution failed: {str(e)}")
-        #     raise HTTPException(status_code=500, detail="FHE execution failed")
-        # finally:
-        #     # Force garbage collection after FHE execution
-        #     gc.collect()
-
-        # Placeholder output
-        # Generate a random 2-element array with values between 0 and 1
-        placeholder_output = np.random.rand(2)
-        # Ensure the sum of the two elements is 1 (to mimic softmax output)
-        placeholder_output = placeholder_output / np.sum(placeholder_output)
-        encrypted_output = placeholder_output.tobytes()
+        try:
+            encrypted_output = FHE_SERVER.run(encrypted_image, evaluation_key)
+        except MemoryError:
+            logger.error("FHE execution failed due to insufficient memory")
+            raise HTTPException(status_code=503, detail="Insufficient memory during FHE execution")
+        except Exception as e:
+            logger.error(f"FHE execution failed: {str(e)}")
+            raise HTTPException(status_code=500, detail="FHE execution failed")
+        finally:
+            # Force garbage collection after FHE execution
+            gc.collect()
 
         fhe_execution_time = round(time.time() - start, 2)