saitejamalyala

# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

saitejamalyala

import math
from typing import Dict, Tuple, Optional, List

class SparseMatrix:
    """Sparse Matrix class
    Args:
        data: A dictionary of (i, j) -> value
    """

    def __init__(self, data: Dict[Tuple[int, int], float] = None):

saitejamalyala

from tensorflow.keras import backend as K
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
import tensorflow as tf
from tensorflow.keras.regularizers import l1,l1_l2,l2
from tensorflow.python.keras.regularizers import L1
from rosbag2numpy.config import params

def euclidean_distance_loss(y_true, y_pred):

saitejamalyala

#! /usr/bin/env python

# Copyright (c) 2019 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

saitejamalyala

import tensorflow as tf


@tf.keras.utils.register_keras_serializable()
class CustomLayer(tf.keras.layers.Layer):
    def __init__(self, k, **kwargs):
        self.k = k
        super(CustomLayer, self).__init__(**kwargs)

    def get_config(self):

saitejamalyala

import wandb
from wandb.keras import WandbCallback
import tensorflow as tf

class cd_wandb_custom(WandbCallback):
    def __init__(
        self,
        # newly added
        ds_test,
        np_test_dataset:Dict[str,Union[ndarray,List]],

saitejamalyala

from tensorflow.keras import layers
from tensorflow.keras import models
import tensorflow as tf

class CustomMaskLayer(layers.Layer):
    """Layer that masks tensor at specific locations as mentioned in binary tensor 

    Args:
        layers (layers.Layer): keras.layers baseclass
    """    

saitejamalyala

import tensorflow as tf

class CustomLayer(tf.keras.layers.Layer):
    def __init__(self, k, name=None, **kwargs):
        super(CustomLayer, self).__init__(name=name)
        self.k = k
        super(CustomLayer, self).__init__(**kwargs)

    def get_config(self):
        config = super(CustomLayer, self).get_config()