working on making it just a flask app, got rid of the main.py stuff

app.py

@@ -1,12 +1,44 @@
-from flask import Flask, render_template
+from flask import Flask, render_template, request
+import numpy as np
+
+from opts import options
+
+app = Flask(
+    __name__,
+    static_folder="static",
+    template_folder="templates",
+)
+
+
+def random_dataset(rows: int, features: int):
+    """
+    Initializes a training and a testing dataset in the form of numpy arrays
+    """
+    rng = np.random.default_rng()
+    X = rng.normal(size=(rows, features))
+    y = rng.integers(5, size=(rows, 1))
+    return X, y
 
-app = Flask(__name__)
 
 @app.route("/")
-@app.route("/index")
 def index():
     return render_template("index.html")
 
+
+@app.route("/process_algorithm", methods=["GET", "POST"])
+def process_algorithm():
+    alg = request.form.get('model-select')
+    func = options[alg]
+
+    # have a form for options based on the algorithm the user chose
+    # and set it as the args variable, make a 'go' button for this funcitonality
+    # to start the algorithm
+    args = 0
+    if args:
+        # create random numpy array dataset
+        X, y = random_dataset(100, 3)
+        func(X, y, args)
+
+
 if __name__ == '__main__':
     app.run(debug=True)
-

build_dev.sh

@@ -0,0 +1,2 @@
+#!/bin/bash
+npx tailwindcss -i ./static/src/input.css -o ./static/dist/css/output.css --watch

main.py

@@ -1,27 +0,0 @@
-from opts import options
-import numpy as np
-
-
-def random_dataset(rows: int, features: int):
-    """
-    Initializes a training and a testing dataset in the form of numpy arrays
-    """
-    rng = np.random.default_rng()
-    X = rng.normal(size=(rows, features))
-    y = rng.integers(5, size=(rows, 1))
-    return X, y
-
-
-def main():
-    method = input("\nChoose a method to test: ").lower()
-    try:
-        func = options[method]
-    except KeyError:
-        raise f"Invalid method \"{method}\". Try one of these\n{list(options.keys())}"
-
-    X, y = random_dataset(rows=1000, features=10)
-    func(X, y)
-
-
-if __name__ == "__main__":
-    main()

neural_network/main.py

@@ -1,25 +1,10 @@
 from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
 import numpy as np
 
 from neural_network.opts import activation
 from neural_network.backprop import bp
 from neural_network.model import Network
-from neural_network.plot import loss_history_plt
-
-
-def get_args() -> dict:
-    """
-    returns a dictionary containing
-    the arguments to be passed to
-    the main function
-    """
-    return {
-        "epochs": int(input("Enter the number of epochs: ")),
-        "hidden_size": int(input("Enter the number of hidden nodes: ")),
-        "learning_rate": float(input("Enter the learning rate: ")),
-        "activation_func": input("Enter the activation function: "),
-    }
+from neural_network.plot import loss_history_plt, save_plt
 
 
 def init(X: np.array, hidden_size: int) -> dict:
@@ -38,8 +23,8 @@ def init(X: np.array, hidden_size: int) -> dict:
 def main(
         X: np.array,
         y: np.array,
+        args,
 ) -> None:
-    args = get_args()
     wb = init(X, args["hidden_size"])
     X_train, X_test, y_train, y_test = train_test_split(
         X,
@@ -65,5 +50,4 @@ def main(
     # plot predicted versus actual
     # also plot the training loss over epochs
     animated_loss_plt = loss_history_plt(loss_history)
-    # eventually we will save this plot
-    plt.show()
+    save_plt(animated_loss_plt, "plt.svg", animated=True, fps=30)

opts.py

@@ -2,6 +2,7 @@ from neural_network.main import main as nn
 from cluster.main import main as clust
 
 options = {
-    "nn": nn,
-    "cluster": clust,
+    "neural-network": nn,
+    "kmeans-clustering": clust,
+    "kmedoid-clustering": clust,
 }

run.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+npx tailwindcss -i ./static/src/input.css -o ./static/dist/css/output.css
+gunicorn --bind :3000 --workers 1 --threads 8 --timeout 0 app:app

run_dev.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-
-# Function to handle SIGINT signal
-function cleanup {
-  echo "Cleaning up and exiting..."
-  kill $TAILWIND_PID
-  kill $FLASK_PID
-  exit 0
-}
-
-# Register cleanup function to be called when SIGINT is received
-trap cleanup SIGINT
-
-# Start tailwindcss watch process in the background
-npx tailwindcss -i ./static/src/input.css -o ./static/dist/css/output.css --watch &
-TAILWIND_PID=$!
-
-# Wait for the CSS files to be generated before starting the Flask server
-sleep 2
-
-# Start the Flask server in the background
-python app.py &
-FLASK_PID=$!
-
-# Wait for both processes to finish
-wait $TAILWIND_PID $FLASK_PID
-
-# When the wait command completes, kill both processes
-kill $TAILWIND_PID
-kill $FLASK_PID

templates/index.html

@@ -1,18 +1,34 @@
 <!DOCTYPE html>
 <html lang="en">
-  <head>
+<head>
     <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Flowbite Flask</title>
-    <link rel="stylesheet" href="{{url_for('static',filename='dist/css/output.css')}}">
-  </head>
-  <body>
-    <div class="flex items-center h-screen">
-      <div class="mx-auto">
-        <h1 class="text-3xl font-bold">Centered Text</h1>
-      </div>
+    <title>Data Mining From Scratch</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='dist/css/output.css') }}">
+    <!-- Add Tailwind CSS -->
+    <link rel="stylesheet" href="https://cdn.tailwindcss.com/css/tailwind.min.css">
+</head>
+<body class="bg-gray-100">
+<div class="container mx-auto flex flex-col items-center justify-center h-screen">
+    <div class="text-center mb-8">
+        <h1 class="text-4xl font-bold">Data Mining From Scratch</h1>
+        <p class="text-gray-500">By: Jensen Holm</p>
     </div>
-  </body>
+    <div class="w-full md:w-1/3">
+        <form method="POST" action="{{ url_for('process_algorithm') }}">
+            <label class="block font-medium text-gray-700 mb-2" for="model-select">
+                Select Algorithm
+            </label>
+            <select id="model-select" name="model-select"
+                    class="form-select block w-full mt-1 rounded-md shadow-sm border-gray-300 focus:border-indigo-300 focus:ring focus:ring-indigo-200 focus:ring-opacity-50">
+                <option value="neural-network">Neural Network</option>
+                <option value="kmeans-clustering">Kmeans Clustering</option>
+                <option value="kmedoids-clustering">Kmedoids Clustering</option>
+            </select>
+            <button type="submit" class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded mt-4">
+                Go
+            </button>
+        </form>
+    </div>
+</div>
+</body>
 </html>
-