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| from flask import Flask, request, jsonify | |
| import joblib | |
| import numpy as np | |
| import os | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| model_path = hf_hub_download( | |
| repo_id="affanthinks/superkart", | |
| filename="AGreatLearning/tuned_bagging_model.pkl", # include directory | |
| revision="main", # ensures correct branch | |
| token=os.getenv("HF_TOKEN") # authentication | |
| ) | |
| model = joblib.load(model_path) | |
| print("✅ Model loaded successfully from", model_path) | |
| # Initialize app | |
| app = Flask("predict_revenue") | |
| def home(): | |
| return jsonify({"message": "Supermarket Revenue Prediction API is running!"}) | |
| def predict(): | |
| try: | |
| # Get JSON input | |
| data = request.get_json(force=True) | |
| features = np.array(data["features"]).reshape(1, -1) | |
| # Predict | |
| prediction = model.predict(features)[0] | |
| return jsonify({"predicted_revenue": float(prediction)}) | |
| except Exception as e: | |
| return jsonify({"error": str(e)}) | |
| if "predict_revenue" == "__main__": | |
| app.run(host="0.0.0.0", port=7860, debug=True) | |