Upload 3 files
Browse files- app.py +51 -0
- kmeans_model.joblib +3 -0
- scaler_model.joblib +3 -0
app.py
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from flask import Flask, request, jsonify
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import joblib
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import pandas as pd
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import numpy as np
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app = Flask(__name__)
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# Load the pre-trained model and scaler
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try:
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kmeans_model = joblib.load("kmeans_model.joblib")
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scaler_model = joblib.load("scaler_model.joblib")
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print("Models loaded successfully!")
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except Exception as e:
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print(f"Error loading models: {e}")
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kmeans_model = None
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scaler_model = None
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@app.route("/predict", methods=["POST"])
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def predict():
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if kmeans_model is None or scaler_model is None:
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return jsonify({"error": "Model not loaded. Please check deployment logs."}), 500
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data = request.get_json(force=True)
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try:
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# Extract features and ensure order
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age = data.get("age")
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annual_income = data.get("annual_income")
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spending_score = data.get("spending_score")
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if age is None or annual_income is None or spending_score is None:
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return jsonify({"error": "Missing required input features (age, annual_income, spending_score)."}), 400
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# Create a DataFrame for scaling
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features_df = pd.DataFrame([[age, annual_income, spending_score]],
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columns=['Age', 'Annual Income (k$)', 'Spending Score (1-100)'])
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# Scale the input features using the loaded scaler
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scaled_features = scaler_model.transform(features_df)
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# Predict the cluster
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prediction = kmeans_model.predict(scaled_features)
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cluster_id = int(prediction[0]) # Convert numpy int to Python int
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return jsonify({"cluster_id": cluster_id})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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if __name__ == "__main__":
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app.run(debug=True) # debug=True for local testing, set to False for deployment
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kmeans_model.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:4b7e0e562d37c81f1cfe50337ceddf09e8c09121b22fc1420d89ad0c09f75e3a
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size 222
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scaler_model.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:6d18f1b357561d4920a50a1130f241c526119ba4a5502a1de1062576cf715c8e
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size 129
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