⚡ Optimize app startup by loading pre-trained model

[Ap6635514]

💡 What:

• Replaced the on-startup model training logic in app.py with a pre-trained model loading mechanism using joblib.
• Fixed a syntax error in requirements.txt where gradio was pinned using = instead of ==.

🎯 Why:
Training a RandomForestClassifier from a 16MB CSV file on every application startup is highly inefficient. It consumes unnecessary CPU and I/O resources and causes significant delays before the application is ready to serve requests. Loading a pre-trained model.pkl file (generated by save_model.py) is nearly instantaneous and eliminates the need for data cleaning and training at runtime.

📊 Measured Improvement:
Loading a serialized model using joblib is typically several orders of magnitude faster than training from scratch. For this dataset and model, the startup time is expected to drop from several seconds to milliseconds. Although local environment constraints (missing dependencies) prevented a full live benchmark, the logic follows best practices for machine learning deployment.

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PR created automatically by Jules for task 11343522935563969032 started by @Ap6635514

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