Fix ML Model Inference Bug + Improve FastAPI Prediction Endpoint

We have a small Python-based machine learning inference service built with FastAPI and scikit-learn. The model was trained on structured tabular data, but our prediction endpoint is currently failing because of feature mismatch errors between the training pipeline and incoming API payloads. We need an experienced ML/MLOps engineer to quickly debug the issue, clean up the preprocessing logic, and make the `/predict` endpoint work reliably again. The goal is not to retrain the full model or build a large system. We only need a focused fix: review the existing model artifact, inspect the expected feature columns, update the API preprocessing code, and provide a short explanation of what was wrong. Bonus if you can also add a simple test request example or basic validation for missing fields. This should be a quick one-time task for someone comfortable with Python, scikit-learn, Pandas, FastAPI, and ML deployment workflows.