splitting up rest api functionality to make more readable

app.py

@@ -1,53 +1,51 @@
-from flask import Flask, request, jsonify, make_response, render_template
+from flask import Flask, request, jsonify, render_template
 from flask_cors import CORS
 
 from dataset.iris import iris
 from opts import options
 
+# using the iris data set for every algorithm
+X, y = iris()
+
 app = Flask(
     __name__,
     template_folder="templates",
 )
 
-CORS(app, origins="*")
-
-
-def not_valid(params: dict):
-    """
-    is_valid() simply checks if
-    an incoming response is valid
-    for this api or not. Key to
-    avoiding errors
-    """
-    if "algorithm" not in params:
-        return "User did not specify the algorithm parameter"
-
-    if params["algorithm"] not in options:
-        return f"Invalid algorithm '{params['algorithm']}' is invalid."
-    return False
+CORS(
+    app=app,
+    origins="*",
+)
 
 
-@app.route("/", methods=["POST", "GET"])
+@app.route("/", methods=["GET"])
 def index():
-    if request.method == "GET":
-        return render_template("index.html")
+    return render_template("index.html")
 
-    error_message = not_valid(params=request.json)
-    if error_message:
-        return make_response(error_message, 400)
 
+@app.route("/neural-network", methods=["POST", "GET"])
+def neural_network():
     # parse arguments
-    algorithm = options[request.json["algorithm"]]
+    algorithm = options["neural-network"]
     args = request.json["arguments"]
 
-    if "cluster" in request.json["algorithm"]:
-        args["algorithm"] = request.json["algorithm"]
+    result = algorithm(
+        X=X,
+        y=y,
+        args=args,
+    )
+    return jsonify(result)
+
+
+@app.route("/kmeans-clustering", methods=["POST"])
+def kmeans():
+    algorithm = options["kmeans-clustering"]
+    args = request.json["arguments"]
 
-    # using the iris data set for every algorithm
-    X, y = iris()
     result = algorithm(
         X=X,
         y=y,
+        clusterer="kmeans-clustering",
         args=args,
     )
     return jsonify(result)

cluster/main.py

@@ -9,12 +9,12 @@ from cluster.plot import plot
 def main(
     X: np.array,
     y: np.array,
+    clusterer: str,
     args: dict,
 ) -> dict:
-    cluster_func = args.pop("algorithm")
-    cluster_alg: Clusterer = clustering_methods[cluster_func]
+    cluster_alg: Clusterer = clustering_methods[clusterer]
 
-    args.update({"cluster_func": cluster_func})
+    args.update({"cluster_func": cluster_alg})
     alg = cluster_alg.from_dict(args)
 
     alg.build(X)