trying to move forwards with the iris dataset

app.py

@@ -1,6 +1,7 @@
 from flask import Flask, request, jsonify, make_response, render_template
 
 from dataset.random import random_dataset
+from dataset.iris import iris
 from opts import options
 
 app = Flask(
@@ -29,23 +30,28 @@ def index():
     if request.method == "GET":
         return render_template("index.html")
 
-    params = request.json
-    error_message = not_valid(params=params)
+    error_message = not_valid(params=request.json)
     if error_message:
         return make_response(error_message, 400)
 
     # parse arguments
-    algorithm = options[params["algorithm"]]
-    args = params["arguments"]
+    algorithm = options[request.json["algorithm"]]
+    args = request.json["arguments"]
 
     # in the future instead of a random data set
     # we should do a more real one like palmer penguins
-    X, y = random_dataset(100, 10)
-    model = algorithm(X, y, args)
-    return jsonify(model)
+
+    X_train, X_test, y_train, y_test = iris()
+    return jsonify(
+        algorithm(
+            X_train=X_train,
+            y_train=y_train,
+            X_test=X_test,
+            y_test=y_test,
+            args=args,
+        )
+    )
 
 
 if __name__ == "__main__":
-    app.run(
-        debug=False,
-    )
+    app.run(debug=False)

dataset/iris.py

@@ -0,0 +1,29 @@
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+
+def iris() -> tuple[np.array]:
+    """
+    returns a tuple of numpy arrays containing the
+    iris dataset split into training and testing sets
+    after being normalized and one-hot encoded 
+    """
+    iris = load_iris()
+    X_train, X_test, y_train, y_test = train_test_split(
+        iris.data,
+        iris.target,
+        test_size=0.3,
+        random_state=8675309,
+    )
+    scaler = StandardScaler()
+    X_train, X_test = scaler.fit_transform(
+        X_train
+    ), scaler.fit_transform(
+        X_test
+    )
+
+    y_train = OneHotEncoder().fit_transform(y_train.reshape(-1, 1)).toarray()
+    y_test = OneHotEncoder().fit_transform(y_test.reshape(-1, 1)).toarray()
+    return X_train, X_test, y_train, y_test

neural_network/main.py

@@ -22,21 +22,16 @@ def init(
 
 
 def main( 
-    X: np.array,
-    y: np.array,
+    X_train: np.array,
+    y_train: np.array,
+    X_test: np.array,
+    y_test: np.array,
     args,
 ) -> None:
-    wb = init(X, args["hidden_size"])
+    wb = init(X_train, args["hidden_size"])
     act = activation[args["activation_func"]]
     args["activation_func"] = act["main"]
     args["func_prime"] = act["prime"]
-
-    X_train, X_test, y_train, y_test = train_test_split(
-        X,
-        y,
-        test_size=0.3,
-        random_state=8675309
-    )
     model = bp(X_train, y_train, wb, args)
 
     # evaluate the model and return final results