Upload 2 files

app.py

@@ -24,7 +24,7 @@ REPO_DIR = Path(__file__).parent
 subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR)
 
 
-#subprocess.Popen(["uvicorn", "server:app", "--port", "3000"], cwd=REPO_DIR)
+# subprocess.Popen(["uvicorn", "server:app", "--port", "3000"], cwd=REPO_DIR)
 
 # if not exists, create a directory for the FHE keys called .fhe_keys
 if not os.path.exists(".fhe_keys"):
@@ -190,30 +190,25 @@ def process_pipeline(test_file):
     return eval_key, encodings, encrypted_quantized_encoding, encrypted_prediction
 
 if __name__ == "__main__":
-        
+
     with gr.Blocks() as demo:
         print("Starting the FHE Model")
 
+        fn = (process_pipeline,)
+        inputs = (
+            [
+                gr.File(label="Test File"),
+            ],
+        )
+        outputs = (
+            [
+                gr.Textbox(label="Evaluation Key"),
+                gr.Textbox(label="Encodings"),
+                gr.Textbox(label="Encrypted Quantized Encoding"),
+                gr.Textbox(label="Encrypted Prediction"),
+            ],
+        )
+        title = ("FHE Model",)
+        description = ("This is a FHE Model",)
 
-
-   
     demo.launch() #share=True)
-
-
-"""
-    app = gr.Interface(
-
-        fn=process_pipeline,
-        inputs=[
-            gr.File(label="Test File"),
-        ],
-        outputs=[
-            gr.Textbox(label="Evaluation Key"),
-            gr.Textbox(label="Encodings"),
-            gr.Textbox(label="Encrypted Quantized Encoding"),
-            gr.Textbox(label="Encrypted Prediction"),
-        ],
-        title="FHE Model",
-        description="This is a FHE Model",
-    )
-"""

server.py

@@ -9,11 +9,13 @@ import uvicorn
 
 
 current_dir = Path(__file__).parent
-
+print('1111', current_dir)
 # Load the model
 fhe_model = FHEModelServer(
     Path.joinpath(current_dir, "fhe_model")
 )
+print(Path.joinpath(current_dir, "fhe_model"))
+print(fhe_model)
 class PredictRequest(BaseModel):
     evaluation_key: str
     encrypted_encoding: str
@@ -39,5 +41,5 @@ def predict(query: PredictRequest):
     encoded_prediction = base64.b64encode(prediction).decode()
     return {"encrypted_prediction": encoded_prediction}
 
-#if __name__ == "__main__":
-#    uvicorn.run(app, host="0.0.0.0", port=3000)
+# if __name__ == "__main__":
+#    uvicorn.run(app, host="0.0.0.0", port=3000)