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main.py

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+from flask import Flask, request, jsonify
+import joblib
+import numpy as np
+
+# Load the scaler and models
+scaler = joblib.load('scaler.joblib')
+models = {target: joblib.load(f'svm_model_{target}.joblib') for target in ['processing', 'perception', 'input', 'understanding']}
+
+app = Flask(__name__)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json.get('user_input')
+    if not data:
+        return jsonify({"error": "No input provided"}), 400
+    
+    user_input_array = np.array(data).reshape(1, -1)
+    user_input_scaled = scaler.transform(user_input_array)
+
+    predictions = {target: model.predict(user_input_scaled)[0] for target, model in models.items()}
+    
+    return jsonify(predictions)
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

scaler.joblib

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+oid sha256:716237c8daa8b3ff6474304982f64e745ef2fd1f6159e1daac03fdf0604a5586
+size 1431

svm_model_input.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:31bf1c0b7ed4a190da8178ffd9bd4fa3ace472e89758f59f95cfe76fa1508f03
+size 34539

svm_model_perception.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11b96afabaa858fa31c09a79da2128775921a56e6401a21dbc107b6dd544dc65
+size 30139

svm_model_processing.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:95d16ec55a13538b6de43b771ccf226b0d60e3003da9e347bead01f74eb3dc12
+size 68651

svm_model_understanding.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6713ca9fcc952b6166d1e061e9f8fee1da652296ca2292950a607d6019cf441d
+size 53451