app.py

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)