from flask import Flask, request, render_template, redirect, url_for, session, flash
import seaborn as sns
from sklearn.linear_model import LogisticRegression
import sqlite3, os

app = Flask(__name__)
app.secret_key = "supersecretkey"  # change in production!

# ------------------- Database Setup -------------------
def init_db():
    if not os.path.exists("users.db"):
        conn = sqlite3.connect("users.db")
        c = conn.cursor()
        c.execute("""
            CREATE TABLE users (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                username TEXT UNIQUE NOT NULL,
                password TEXT NOT NULL
            )
        """)
        conn.commit()
        conn.close()

init_db()

# ------------------- ML Model -------------------
df = sns.load_dataset("iris")
X = df.iloc[:, :4].values
y = df.iloc[:, 4].values

model = LogisticRegression(max_iter=200, multi_class="auto")
model.fit(X, y)

# ------------------- Routes -------------------

@app.route("/")
def home():
    if "user" in session:
        return redirect(url_for("predict"))
    return redirect(url_for("login"))

@app.route("/signup", methods=["GET", "POST"])
def signup():
    if request.method == "POST":
        username = request.form["username"]
        password = request.form["password"]

        try:
            conn = sqlite3.connect("users.db")
            c = conn.cursor()
            c.execute("INSERT INTO users (username, password) VALUES (?, ?)", (username, password))
            conn.commit()
            conn.close()
            flash("Signup successful! Please login.", "success")
            return redirect(url_for("login"))
        except sqlite3.IntegrityError:
            flash("Username already taken!", "danger")

    return render_template("signup.html")

@app.route("/login", methods=["GET", "POST"])
def login():
    if request.method == "POST":
        username = request.form["username"]
        password = request.form["password"]

        conn = sqlite3.connect("users.db")
        c = conn.cursor()
        c.execute("SELECT * FROM users WHERE username=? AND password=?", (username, password))
        user = c.fetchone()
        conn.close()

        if user:
            session["user"] = username
            flash("Login successful!", "success")
            return redirect(url_for("predict"))
        else:
            flash("Invalid credentials!", "danger")

    return render_template("login.html")

@app.route("/predict", methods=["GET", "POST"])
def predict():
    if "user" not in session:
        return redirect(url_for("login"))

    prediction_text = ""
    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]

            prediction_text = f"Predicted Flower: {prediction}"
        except Exception as e:
            prediction_text = f"Error: {e}"

    return render_template("index.html", prediction_text=prediction_text)

@app.route("/logout")
def logout():
    session.pop("user", None)
    flash("Logged out successfully.", "info")
    return redirect(url_for("login"))

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
# url = "https://raw.githubusercontent.com/sarwansingh/Python/master/ClassExamples/data/iris.csv"
# df = pd.read_csv(url, header=None)
# 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)