Create app.py

app.py

@@ -0,0 +1,37 @@
+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)
+