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from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
from dataset.iris import iris
from opts import options
import os
# using the iris data set for every algorithm
X, y = iris()
app = Flask(
__name__,
template_folder="templates",
)
CORS(app, origins="*")
UPLOAD_FOLDER = os.getcwd() + "/plots"
@app.route("/", methods=["GET"])
def index():
return render_template("index.html")
@app.route("/plots/<plt_key>", methods=["GET"])
def get_plot(plt_key):
filename = f"{plt_key}.png"
filepath = os.path.join(UPLOAD_FOLDER, filename)
if os.path.isfile(filepath):
with open(filepath, "rb") as file:
plot_bytes = file.read()
return plot_bytes, 200, {"Content-Type": "image/png"}
else:
return "Plot not found", 404
@app.route("/neural-network", methods=["POST"])
def neural_network():
algorithm = options["neural-network"]
args = request.json["arguments"]
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"]
result = algorithm(
X=X,
y=y,
clusterer="kmeans-clustering",
args=args,
)
return jsonify(result)
if __name__ == "__main__":
app.run(debug=False)
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