Update app.py

app.py

@@ -1,21 +1,90 @@
-from flask import Flask, request, render_template
+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
 
-# Load dataset
+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
 
-# Train model
 model = LogisticRegression(max_iter=200, multi_class="auto")
 model.fit(X, y)
 
-# Flask app
-app = Flask(__name__)
+# ------------------- Routes -------------------
 
-@app.route("/", methods=["GET", "POST"])
+@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"])
@@ -27,11 +96,57 @@ def home():
                 [[sepal_length, sepal_width, petal_length, petal_width]]
             )[0]
 
-            return render_template("index.html", prediction_text=f"🌸 Predicted Flower: {prediction}")
+            prediction_text = f"Predicted Flower: {prediction}"
         except Exception as e:
-            return render_template("index.html", prediction_text=f"⚠️ Error: {e}")
+            prediction_text = f"Error: {e}"
+
+    return render_template("index.html", prediction_text=prediction_text)
 
-    return render_template("index.html", 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)
+    app.run(host="0.0.0.0", port=7860, debug=True)
+
+    
+    
+# 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)