Create app.py

app.py

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+from flask import Flask
+import seaborn as sns
+from sklearn.linear_model import LogisticRegression
+
+app = Flask(__name__)
+
+# Train model once at startup (faster than retraining every request)
+iris1 = sns.load_dataset("iris")
+modlog = LogisticRegression(max_iter=300)
+
+irisarr = iris1.values
+X = irisarr[:, 0:4]
+Y = irisarr[:, 4]
+modlog.fit(X, Y)
+
+
+@app.route('/')
+def hello_world():
+    # Test sample (setosa)
+    sl, sw, pl, pw = 5.1, 3.5, 1.4, 0.2
+    res = modlog.predict([[sl, sw, pl, pw]])
+    return "Hello, AIML July 25 batch — Predicted: " + str(res[0])
+
+
+if __name__ == "__main__":
+    # Debug mode for development
+    app.run(debug=True, host="0.0.0.0", port=5000)