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app.py

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+from flask import Flask, request, jsonify
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+app = Flask(__name__)
+
+# 1. Create a simple dataset (X = Input, y = Output)
+# We are teaching the model that y is roughly 2 times X
+X = np.array([[1], [2], [3], [4], [5]])
+y = np.array([2, 4, 6, 8, 10])
+
+# 2. Train the model
+model = LinearRegression()
+model.fit(X, y)
+
+@app.route('/')
+def home():
+    return "ML API is running! Use the /predict endpoint."
+
+@app.route('/predict', methods=['GET'])
+def predict():
+    try:
+        # Get the 'value' parameter from the URL (e.g., /predict?value=10)
+        user_input = request.args.get('value')
+        
+        if user_input is None:
+            return jsonify({"error": "Please provide a 'value' parameter"}), 400
+            
+        prediction = model.predict([[float(user_input)]])
+        
+        return jsonify({
+            "input": user_input,
+            "prediction": prediction[0]
+        })
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

requirements.txt

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+flask
+scikit-learn
+numpy
+gunicorn