My First AI Project [4 Solves]

from flask import Flask, render_template, request, url_for, jsonify
from werkzeug.utils import secure_filename
import pandas as pd
from datetime import datetime
import numpy as np
import os, pickle, base64, time, string, random

app = Flask(__name__)
app.secret_key = os.urandom(24)

ROOT_DIR = "/app/uploads/pickled/"


@app.route("/")
def index():
    return render_template("index.html")


@app.route("/check", methods=["GET"])
def check_dir():
    PATH_DATASET = request.args.get("filePath")
    if os.path.exists(ROOT_DIR + PATH_DATASET):
        return ["Exists"]
    else:
        return ["Not Exists"]


@app.route("/settings", methods=["GET", "POST"])
def settings():
    global ROOT_DIR
    if request.method == "POST":
        ROOT_DIR = request.values.get("rootDirectory")
        return jsonify({"message": "Root directory updated successfully"})

    return render_template("settings.html", root_dir=ROOT_DIR)


def list_subdirectories(input_value):
    try:
        subdirectories = os.listdir(input_value)
        return subdirectories
    except:
        return []


@app.route("/listsubDir", methods=["POST"])
def list_sub_dir():
    input_value = request.form.get("input")
    subdirectories = list_subdirectories(input_value)

    preview_html = "<pre>"
    for subdir in subdirectories:
        preview_html += f"{subdir}\n"
    preview_html += "</pre>"

    if len(subdirectories) != 0:
        return jsonify({"preview": preview_html})
    else:
        return jsonify(
            {"preview": f"cannot access '{input_value}': No such file or directory"}
        )


# Not ready yet
@app.route("/uploads", methods=["POST"])
def uploads():
    if request.method == "POST":
        f = request.files["dataset"]
        for file in os.scandir(os.path.join(app.root_path, "uploads")):
            if file.name.endswith(".csv"):
                os.unlink(file.path)
        if not f.filename.endswith(".csv"):
            return "Failed to upload, ensure the dataset ends with .csv"
        now = datetime.now()
        files = (
            "".join(random.choices(string.ascii_uppercase, k=5))
            + "_"
            + now.strftime("%d_%m_%Y")
        )
        filename = files + ".csv"
        pathfile = os.path.join(app.root_path, "uploads", secure_filename(filename))
        f.save(pathfile)
        time.sleep(1)  # Ensure file uploaded successfully
        uploaded_dataset = pd.read_csv(pathfile)
        try:
            X = uploaded_data[["X", "Y"]]
            Y = uploaded_data["Target"]

            model = LinearRegression()
            model.fit(X, Y)

            with open("uploads/pickled/" + files + ".pkl", "wb") as model_file:
                pickle.dump(model, model_file)
            return "dataset uploaded successfully"
        except:
            return "dataset failed to upload"


def waf(filenames):
    blocked = [
        "..",
        "...",
        "/proc/self",
        "etc",
        "flag",
        "flag.txt",
        "var",
        "usr",
        "bin",
    ]
    for items in blocked:
        if items in filenames:
            return True
    return False


@app.route("/loadModel", methods=["POST"])
def load_model():
    if request.method == "POST":
        filenames = ROOT_DIR + request.form["filePath"]
        if waf(filenames):
            return "Access Denied"
        try:
            with open(filenames, "rb") as model_file:
                loaded_model = np.load(model_file, allow_pickle=True)

            new_data = np.array([[4, 5], [5, 6]])
            predictions = loaded_model.predict(new_data)
            return "Data = %s Predictions for New Data: %s" % (
                str(new_data),
                str(predictions),
            )
        except:
            return (
                "Failed to load model. Ensure you give pickle data.<br><br>Preview:<br><br><pre>"
                + open(filenames, "rb").read().decode()
                + "</pre>"
            )


if __name__ == "__main__":
    app.run(debug=False, host="0.0.0.0")