28 lines
785 B
Python
28 lines
785 B
Python
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from flask import Flask, request, jsonify
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import joblib # or pickle if you're using that
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import sklearn # just to ensure compatibility if needed
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import numpy as np
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# Load your trained model and vectorizer
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model = joblib.load("model/category_model.pkl")
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vectorizer = joblib.load("model/vectorizer.pkl") # if used
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app = Flask(__name__)
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@app.route("/api/predict", methods=["POST"])
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def predict():
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data = request.json
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description = data.get("description", "")
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if not description:
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return jsonify({"error": "Description is required"}), 400
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# Preprocess and predict
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X = vectorizer.transform([description])
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prediction = model.predict(X)[0]
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return jsonify({"category": prediction})
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000)
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