feat: add psagentai agent with deployment and configuration

Initial project setup including the main agent script, configuration files, profile JSON state, systemd service unit, startup script, deployment documentation, and Python dependencies. Includes .env, venv/, and __pycache__/ which should likely be excluded via .gitignore to avoid committing secrets and artifacts.
2026-10-25 17:05:13 -03:00
commit 6b3b6541a7
3792 changed files with 805563 additions and 0 deletions
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+from copy import copy
+from functools import partial
+
+from .auto import tqdm as tqdm_auto
+
+try:
+    import keras
+except (ImportError, AttributeError) as e:
+    try:
+        from tensorflow import keras
+    except ImportError:
+        raise e
+__author__ = {"github.com/": ["casperdcl"]}
+__all__ = ['TqdmCallback']
+
+
+class TqdmCallback(keras.callbacks.Callback):
+    """Keras callback for epoch and batch progress."""
+    @staticmethod
+    def bar2callback(bar, pop=None, delta=(lambda logs: 1)):
+        def callback(_, logs=None):
+            n = delta(logs)
+            if logs:
+                if pop:
+                    logs = copy(logs)
+                    for i in pop:
+                        logs.pop(i, 0)
+                bar.set_postfix(logs, refresh=False)
+            bar.update(n)
+
+        return callback
+
+    def __init__(self, epochs=None, data_size=None, batch_size=None, verbose=1,
+                 tqdm_class=tqdm_auto, **tqdm_kwargs):
+        """
+        Parameters
+        ----------
+        epochs  : int, optional
+        data_size  : int, optional
+            Number of training pairs.
+        batch_size  : int, optional
+            Number of training pairs per batch.
+        verbose  : int
+            0: epoch, 1: batch (transient), 2: batch. [default: 1].
+            Will be set to `0` unless both `data_size` and `batch_size`
+            are given.
+        tqdm_class  : optional
+            `tqdm` class to use for bars [default: `tqdm.auto.tqdm`].
+        tqdm_kwargs  : optional
+            Any other arguments used for all bars.
+        """
+        if tqdm_kwargs:
+            tqdm_class = partial(tqdm_class, **tqdm_kwargs)
+        self.tqdm_class = tqdm_class
+        self.epoch_bar = tqdm_class(total=epochs, unit='epoch')
+        self.on_epoch_end = self.bar2callback(self.epoch_bar)
+        if data_size and batch_size:
+            self.batches = batches = (data_size + batch_size - 1) // batch_size
+        else:
+            self.batches = batches = None
+        self.verbose = verbose
+        if verbose == 1:
+            self.batch_bar = tqdm_class(total=batches, unit='batch', leave=False)
+            self.on_batch_end = self.bar2callback(
+                self.batch_bar, pop=['batch', 'size'],
+                delta=lambda logs: logs.get('size', 1))
+
+    def on_train_begin(self, *_, **__):
+        params = self.params.get
+        auto_total = params('epochs', params('nb_epoch', None))
+        if auto_total is not None and auto_total != self.epoch_bar.total:
+            self.epoch_bar.reset(total=auto_total)
+
+    def on_epoch_begin(self, epoch, *_, **__):
+        if self.epoch_bar.n < epoch:
+            ebar = self.epoch_bar
+            ebar.n = ebar.last_print_n = ebar.initial = epoch
+        if self.verbose:
+            params = self.params.get
+            total = params('samples', params(
+                'nb_sample', params('steps', None))) or self.batches
+            if self.verbose == 2:
+                if hasattr(self, 'batch_bar'):
+                    self.batch_bar.close()
+                self.batch_bar = self.tqdm_class(
+                    total=total, unit='batch', leave=True,
+                    unit_scale=1 / (params('batch_size', 1) or 1))
+                self.on_batch_end = self.bar2callback(
+                    self.batch_bar, pop=['batch', 'size'],
+                    delta=lambda logs: logs.get('size', 1))
+            elif self.verbose == 1:
+                self.batch_bar.unit_scale = 1 / (params('batch_size', 1) or 1)
+                self.batch_bar.reset(total=total)
+            else:
+                raise KeyError('Unknown verbosity')
+
+    def on_train_end(self, *_, **__):
+        if hasattr(self, 'batch_bar'):
+            self.batch_bar.close()
+        self.epoch_bar.close()
+
+    def display(self):
+        """Displays in the current cell in Notebooks."""
+        container = getattr(self.epoch_bar, 'container', None)
+        if container is None:
+            return
+        from .notebook import display
+        display(container)
+        batch_bar = getattr(self, 'batch_bar', None)
+        if batch_bar is not None:
+            display(batch_bar.container)
+
+    @staticmethod
+    def _implements_train_batch_hooks():
+        return True
+
+    @staticmethod
+    def _implements_test_batch_hooks():
+        return True
+
+    @staticmethod
+    def _implements_predict_batch_hooks():
+        return True