Update dependencies to latest versions and modernize TensorFlow APIs

[Copilot]

Updated all packages to latest stable versions for Python 3.12 compatibility and security compliance.

Package Updates

• streamlit: 0.79.0 → 1.39.0
• plotly: 4.14.3 → 5.24.1
• numpy: 1.19.5 → 1.26.4
• matplotlib: 3.3.4 → 3.9.2
• pandas: 1.1.5 → 2.2.3
• tensorflow: 2.4.1 → 2.18.0 (now explicit in requirements)
• Dockerfile base: tensorflow/tensorflow:2.4.1-gpu → 2.18.0-gpu

API Migrations

TensorFlow data pipeline APIs:

# Before
self.AUTOTUNE = tf.data.experimental.AUTOTUNE
dataset = dataset.apply(tf.data.experimental.ignore_errors())

# After  
self.AUTOTUNE = tf.data.AUTOTUNE
dataset = dataset.ignore_errors()

Optimizer initialization:

# Before - pre-instantiated with learning rate assignment
OPTIMIZERS = {"Adam": tf.keras.optimizers.Adam()}
OPTIMIZERS[name].learning_rate.assign(lr)

# After - factory pattern with constructor parameter
def get_optimizer(name, learning_rate):
    return optimizers_map[name](learning_rate=learning_rate)

Security

All packages scanned via GitHub Advisory Database and CodeQL - zero vulnerabilities found.

Original prompt

Analyze the codebase, and update the packages to the latest versions. Update the codebase as well as needed. Ensure security compliance.

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