File size: 2,369 Bytes
9668cbe b0c2eee 9668cbe db940bb 9668cbe b0c2eee 9668cbe 0483f56 9668cbe b0c2eee 9668cbe db940bb 9668cbe 2ca1f10 db940bb 9668cbe 8653d9b b0c2eee 8653d9b b0c2eee 9668cbe b0c2eee 8653d9b 9668cbe 8653d9b 2ca1f10 b0c2eee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | from flask import Flask, request, render_template
import seaborn as sns
from sklearn.linear_model import LogisticRegression
# Load dataset
df = sns.load_dataset("iris")
X = df.iloc[:, :4].values
y = df.iloc[:, 4].values
# Train model
model = LogisticRegression(max_iter=200, multi_class="auto")
model.fit(X, y)
# Flask app
app = Flask(__name__)
@app.route("/", methods=["GET", "POST"])
def home():
if request.method == "POST":
try:
sepal_length = float(request.form["sepal_length"])
sepal_width = float(request.form["sepal_width"])
petal_length = float(request.form["petal_length"])
petal_width = float(request.form["petal_width"])
prediction = model.predict(
[[sepal_length, sepal_width, petal_length, petal_width]]
)[0]
return render_template("index.html", prediction_text=f"🌸 Predicted Flower: {prediction}")
except Exception as e:
return render_template("index.html", prediction_text=f"⚠️ Error: {e}")
return render_template("index.html", prediction_text="")
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860, debug=True)
# from flask import Flask, request, render_template
# import pandas as pd
# from sklearn.linear_model import LogisticRegression
# # Load dataset
# df= sns.loadset("iris")
# X = df.iloc[:, :4].values
# y = df.iloc[:, 4].values
# # Train model
# model = LogisticRegression(max_iter=200)
# model.fit(X, y)
# # Flask app
# app = Flask(__name__)
# @app.route("/", methods=["GET", "POST"])
# def home():
# if request.method == "POST":
# try:
# sepal_length = float(request.form["sepal_length"])
# sepal_width = float(request.form["sepal_width"])
# petal_length = float(request.form["petal_length"])
# petal_width = float(request.form["petal_width"])
# prediction = model.predict([[sepal_length, sepal_width, petal_length, petal_width]])[0]
# return render_template("index.html", prediction_text=f"Predicted Flower: {prediction}")
# except Exception as e:
# return render_template("index.html", prediction_text=f"Error: {e}")
# return render_template("index.html", prediction_text="")
# if __name__ == "__main__":
# app.run(host="0.0.0.0", port=7860, debug=True) |