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from flask import Flask, request, jsonify, Response |
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import os |
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import joblib |
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import pandas as pd |
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from typing import Any, Optional |
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app = Flask(__name__) |
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MODEL_PATH = os.getenv("MODEL_PATH", "best_model_random_forest.joblib") |
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_model: Optional[Any] = None |
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_pipe: Optional[Any] = None |
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_model_error: Optional[str] = None |
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def load_model_if_needed(): |
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"""Load the model lazily so the app can boot even if the model is missing.""" |
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global _model, _pipe, _model_error |
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if _pipe is not None or _model_error is not None: |
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return |
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try: |
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if not os.path.exists(MODEL_PATH): |
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_model_error = f"Model file not found at '{MODEL_PATH}'. Upload it or set MODEL_PATH." |
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return |
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_model = joblib.load(MODEL_PATH) |
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_pipe = _model["pipeline"] if isinstance(_model, dict) and "pipeline" in _model else _model |
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except Exception as e: |
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_model_error = f"Failed to load model from '{MODEL_PATH}': {e}" |
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@app.route("/", methods=["GET"]) |
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def root_html(): |
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return Response( |
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"<!doctype html><html><head><meta charset='utf-8'><title>Backend</title></head>" |
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"<body><h1>Backend running ✅</h1><p>See <code>/health</code> and <code>/predict</code>.</p></body></html>", |
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mimetype="text/html", |
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status=200, |
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) |
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@app.route("/__ping__", methods=["GET"]) |
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def ping_plain(): |
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return Response("ok", mimetype="text/plain", status=200) |
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@app.route("/health", methods=["GET"]) |
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def health(): |
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load_model_if_needed() |
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status = "ok" if _pipe is not None and _model_error is None else "degraded" |
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return jsonify({"status": status, "model_path": MODEL_PATH, "model_error": _model_error}) |
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@app.route("/predict", methods=["POST"]) |
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def predict(): |
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load_model_if_needed() |
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if _pipe is None: |
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return jsonify({"error": f"Model not available. Details: {_model_error}"}), 500 |
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data = request.get_json(force=True) |
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if isinstance(data, dict) and "records" in data: |
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df = pd.DataFrame(data["records"]) |
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elif isinstance(data, list): |
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df = pd.DataFrame(data) |
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elif isinstance(data, dict): |
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df = pd.DataFrame([data]) |
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else: |
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return jsonify({"error": "Unsupported payload format"}), 400 |
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try: |
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preds = _pipe.predict(df) |
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predictions = [float(p) for p in preds] |
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return jsonify({"predictions": predictions}) |
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except Exception as e: |
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return jsonify({"error": str(e)}), 400 |
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if __name__ == "__main__": |
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port = int(os.getenv("PORT", 7860)) |
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print(f"✅ Starting Flask on port {port} for Hugging Face Spaces") |
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app.run(host="0.0.0.0", port=port, debug=False) |
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