Looking for a fix on this python code, worked for 2+ years, now it doesnt

[20]

1. ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. tensorflow-text 2.16.1 requires tensorflow<2.17,>=2.16.1; platform_machine != "arm64" or platform_system != "Darwin", but you have tensorflow 2.15.0 which is incompatible.
   
2. [2:48 PM]
   ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.16.1 which is incompatible.
   
3. [2:48 PM]
   ImportError: cannot import name 'ops' from 'keras' (/usr/local/lib/python3.10/dist-packages/keras/init.py)
   

this is what I'm getting on my end

if anyone has suggestions or knows how to fix it, please let me know, can pay

[91]

requires tensorflow<2.17,>=2.16.1; platform_machine != "arm64" or platform_system != "Darwin", but you have tensorflow 2.15.0 which is incompatible

[20]

Sure, here's a concise version of your question, ready to send on GitHub:

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Title: Trouble Loading and Using a TensorFlow SavedModel with Custom Input Layer in Keras

Body:

Hi everyone,

I'm currently working on a project where I need to load a TensorFlow SavedModel and use it with a custom input layer in Keras. However, I'm running into issues when trying to perform inference with the model. Here’s what I’ve done so far:

1. Loaded the SavedModel as an inference-only layer:
       import tensorflow as tf
    from keras.layers import TFSMLayer
    from keras import Input
    from keras.models import Model

    base_model = TFSMLayer("path/to/savedmodel", call_endpoint='serving_default')

    input_layer = Input(shape=(32,), dtype='float64')

    output_layer = base_model(input_layer)

    model = Model(inputs=input_layer, outputs=output_layer)
    
2. Converted test data to the required float64 dtype:
       import pandas as pd

    test_data = pd.read_csv("path/to/testdata.csv")
    test_data_float64 = tf.cast(test_data.values, tf.float64)
    
3. Attempted to use the model for inference:
       predictions = model(test_data_float64)
    
However, I’m encountering issues with the input data type and shape compatibility. 

### My Questions:

1. Data Type Compatibility: How can I ensure that the input data is correctly formatted and compatible with the expected input dtype of the TFSMLayer?
2. Shape Issues: Are there any common pitfalls or best practices when dealing with custom input layers in Keras models that load TensorFlow SavedModels?
3. Inference with Custom Layers: Is there a better approach to modify the input layer of a pre-trained TensorFlow SavedModel for inference in Keras?

Any guidance or suggestions on how to resolve these issues would be greatly appreciated. Thank you!

---

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I requires tensorflow<2.17,>=2.16.1; platform_machine != "arm64" or platform_system != "Darwin", but you have tensorflow 2.15.0 which is incompatible

I believe that we need it to be at 2.15 for the data compile or whatever, once my dev responds to this in the morning i will relay his message over and quite possibly you might have a solution

[1.557]

This is why you're supposed to use virtual environments. Try running pipdeptree on your project to see if there are any conflicts.