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| from flask import Flask, request, jsonify, render_template | |
| from flask_cors import CORS | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.linear_model import LogisticRegression | |
| app = Flask(__name__) | |
| app.static_folder = 'static' | |
| app.static_url_path = '/static' | |
| app.secret_key = "flask-nielit-2023" | |
| CORS(app) | |
| def iris(): | |
| return render_template("index.html") | |
| def page(): | |
| swidth=eval(request.form.get("swidth")) | |
| sheight=eval(request.form.get("sheight")) | |
| pwidth=eval(request.form.get("pwidth")) | |
| pheight=eval(request.form.get("pheight")) | |
| url="https://raw.githubusercontent.com/lovnishverma/datasets/main/iris.csv" | |
| data=pd.read_csv(url, header=None) | |
| flower=data.values | |
| #Split | |
| x=flower[:,:4] | |
| y=flower[:,-1] | |
| model=LogisticRegression() | |
| model.fit(x,y) | |
| arr=model.predict([[swidth,sheight,pwidth,pheight]]) | |
| return render_template("index.html", data=str(arr[0])) | |
| if __name__ == '__main__': | |
| app.run() | |