chore: add a dev.py file to allow the user to generate server.py and client.py by his own

dev.py

@@ -0,0 +1,48 @@
+import shutil
+
+from pathlib import Path
+
+import pandas as pd
+
+from concrete.ml.sklearn import LogisticRegression as ConcreteLogisticRegression
+from concrete.ml.deployment import FHEModelDev
+
+# Files location
+TRAINING_FILE_NAME = "./data/Training_preprocessed.csv"
+TESTING_FILE_NAME = "./data/Testing_preprocessed.csv"
+
+# Load data
+df_train = pd.read_csv(TRAINING_FILE_NAME)
+df_test = pd.read_csv(TESTING_FILE_NAME)
+
+print(df_train.shape)
+print(df_train.columns)
+# Split the data into X_train, y_train, X_test_, y_test sets
+TARGET_COLUMN = ["prognosis_encoded", "prognosis"]
+
+y_train = df_train[TARGET_COLUMN[0]].values.flatten()
+y_test = df_test[TARGET_COLUMN[0]].values.flatten()
+
+X_train = df_train.drop(TARGET_COLUMN, axis=1)
+X_test = df_test.drop(TARGET_COLUMN, axis=1)
+
+# Models parameters
+optimal_param = {"C": 0.9, "n_bits": 13, "solver": "sag", "multi_class": "auto"}
+
+# Concrete ML model
+clf = ConcreteLogisticRegression(**optimal_param)
+
+clf.fit(X_train, y_train)
+
+fhe_circuit = clf.compile(X_train)
+
+fhe_circuit.client.keygen(force=False)
+
+path_to_model = Path("./deployment_logit/").resolve()
+
+if path_to_model.exists():
+    shutil.rmtree(path_to_model)
+
+dev = FHEModelDev(path_to_model, clf)
+
+dev.save(via_mlir=True)

requirements.txt

@@ -1,4 +1,4 @@
 concrete-ml==1.4.0
-gradio==3.35.2
+gradio==3.40.1
 uvicorn>=0.21.0
 fastapi>=0.93.0