📌 What I tried

1. Using TensorFlow.js and related modules to import Keras h5 model in a TypeScript environment

Unable to use .h5 model and .pkl tokenizer in Node.js environment, so conversion to .json format is required.

import tensorflowjs as tfjs
from tensorflow.keras.models import load_model

model = load_model('rnn_model_v3.h5')
tfjs.converters.save_keras_model(model, './predict_emergency_level_model')

The above code converts the .h5 model into a .json file that can be recognized by tensorflow.js.

Issue #1

• Problem: Error encountered during the installation of the tensorflowjs module:

Collecting flax>=0.5.3 (from tensorflowjs)
  Using cached flax-0.5.3-py3-none-any.whl (202 kB)
ERROR: Cannot install flax because these package versions have conflicting dependencies.

The conflict is caused by:
    optax 0.1.5 depends on jaxlib>=0.1.37
    optax 0.1.4 depends on jaxlib>=0.1.37
    optax 0.1.3 depends on jaxlib>=0.1.37
    optax 0.1.2 depends on jaxlib>=0.1.37
    optax 0.1.1 depends on jaxlib>=0.1.37
    optax 0.1.0 depends on jaxlib>=0.1.37
    optax 0.0.91 depends on jaxlib>=0.1.37
    optax 0.0.9 depends on jaxlib>=0.1.37
    optax 0.0.8 depends on jaxlib>=0.1.37
    optax 0.0.6 depends on jaxlib>=0.1.37
    optax 0.0.5 depends on jaxlib>=0.1.37
    optax 0.0.3 depends on jaxlib>=0.1.37
    optax 0.0.2 depends on jaxlib>=0.1.37
    optax 0.0.1 depends on jaxlib>=0.1.37

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

• Solution: Referenced GitHub issue

• Conclusion: Installed with pip install --no-deps tensorflowjs command.

Issue #2

• Problem: Error encountered during the installation of the tensorflow_decision_forests module:

ERROR: Could not find a version that satisfies the requirement tensorflow-decision-forests>=1.3.0 (from tensorflowjs) (from versions: none)
ERROR: No matching distribution found for tensorflow-decision-forests>=1.3.0 (from tensorflowjs)

• Solution:

  1. Referenced TensorFlow official documentation

     Tried to install the problematic module with the command pip3 install tensorflow_decision_forests --upgrade, but encountered the above error. Decided to use the tensorflow/build Docker image.

  2. Executing Python files in the Docker container tensorflow/build

     2-1. Pulled the Docker image with the command docker pull tensorflow/build:latest-python3.8. 2-2. Executed the image to start the container with the command docker run -it tensorflow/build:latest-python3.8 bash. 2-3. Accessed the Docker container through the above command and set up the Python virtual environment. 2-4. Installed the module with the command pip install tensorflow_decision_forests inside the Docker container. (Also downgraded the numpy module version during the process.)

• Conclusion: Installed the tensorflow_decision_forests module in the tensorflow/build Docker container environment.

Issue #3

• Problem: Bringing local files into the Docker container and running them

• Solution:

1. Used the command docker cp [absolute path of the local file to be saved] [container_name]:[path to save in the container] (docker cp C:/Users/siwon/Desktop/Voyage99/projects/CodeBlue-AI-server/rnn_model_v3.h5 hungry_jones:/app ) to load the local files (.h5 model file and the py file to be executed) into the Docker container.

2. Additionally, installed tensorflowjs, tensorflow-hub, and jaxlib.

3. Executed the Python file with the command python [filename.py]. The "predict_emergency_level_model" folder was created as intended.

4. Used the following command to load the generated files back into our local environment from the container: docker cp [container_name]:[file path to be retrieved from the container] [absolute path to be saved in the local environment] (docker cp hungry_jones:/app/predict_emergency_level_model C:/Users/siwon/Desktop/Voyage99/projects/CodeBlue-AI-server ). The intended .bin and .json files were successfully loaded.

• Conclusion: Successfully used Docker commands to bring local files into the Docker container and run them.

Issue #4

• Problem: Converting .pkl tokenizer to .json format

• Solution:

import pickle
import json

# Load tokenizer from the pickle file
with open('tokenizer.pkl', 'rb') as f:
    tokenizer = pickle.load(f)

# Save the tokenizer's word_index as a json file
with open('tokenizer.json', 'w') as f:
    json.dump(tokenizer.word_index, f)

• Conclusion: Executed the Python file locally to resolve the issue.

Issue #5

• Problem: Running the model and tokenizer in our Nest project using Flask

• Solution: To verify whether the code works correctly, I tested it with JavaScript:

const tf = require("@tensorflow/tfjs");
const fs = require("fs");

// Preprocessing constants
const MAX_LEN = 18;
const STOPWORDS = ["의","로","을","가","이"];

// Load the existing model and tokenizer
const modelPath =
  "file://C:/Users/siwon/Desktop/Voyage99/projects/CodeBlue-AI-server/predict_emergency_level_model/model.json";
const tokenizerPath =
  "C:/Users/siwon/Desktop/Voyage99/projects/CodeBlue-AI-server/tokenizer.json";

async function loadModel(modelPath) {
  const model = await tf.loadLayersModel(modelPath);
  return model;
}

// Load the file
let tokenizer;
fs.readFile(tokenizerPath, "utf8", (err, data) => {
  if (err) {
    console.error("Unable to read the file:", err);
    return;
  }

  tokenizer = JSON.parse(data);
});

// Sentence prediction
async function emergencyLevelPrediction(sampleSentence) {
  // Load the model
  const model = await loadModel(modelPath);

  // Preprocess the sample sentence (tokenization, remove stopwords)
  let sampleSentenceArr = sampleSentence.split(" ");
  sampleSentenceArr = sampleSentenceArr.filter(
    (word) => !STOPWORDS.includes(word)
  );
  // Tokenize and pad the sample sentence
  const encodedSample = sampleSentenceArr.map(
    (word) => tokenizer.word_index[word] || 0
  );
  const paddedSample = padSequences([encodedSample], MAX_LEN, "post");
  // Predict emergency level for the sample sentence
  const prediction = model.predict(tf.tensor2d(paddedSample));
  let emergencyLevel;
  let confidence;

  if (!Array.isArray(prediction)) {
    emergencyLevel = prediction.argMax(-1).dataSync()[0] + 1;
    confidence = prediction.dataSync()[emergencyLevel - 1];
  }
  console.log(`Emergency level: ${emergencyLevel}, Confidence: ${confidence * 100.0}%`);
}

function padSequences(
  sequences,
  maxLen,
  padding = "post",
  truncating = "post",
  value = 0
) {
  return sequences.map((seq) => {
    // Truncate
    if (seq.length > maxLen) {
      if (truncating === "pre") {
        seq.splice(0, seq.length - maxLen);
      } else {
        seq.splice(maxLen, seq.length - maxLen);
      }
    }
    // Pad
    if (seq.length < maxLen) {
      const pad = [];
      for (let i = 0; i < maxLen - seq.length; i++) {
        pad.push(value);
      }
      if (padding === "pre") {
        seq = pad.concat(seq);
      } else {
        seq = seq.concat(pad);
      }
    }
    return seq;
  });
}

// Example sentence
emergencyLevelPrediction(
  "응급환자는 심장마비로 인해 의식을 잃고 쓰러졌습니다. 호흡 곤란 상태입니다."
); // Expected: 1

At this point, I encountered an error related to absolute file paths. I tried modifying the model path from "C:/" to "file://C:/" to address the error, but it was not resolved.

• Conclusion: Unable to resolve the error related to absolute file paths.

---

🤚 Stop here

I continued attempting to fix the error at this point, but it was time for my mentoring session with the senior coach. During the session, I asked how we could connect the AI model to our project in the desired way, and the coach mentioned that the approach I was trying would introduce dependencies, which should be avoided. Instead, it was suggested to create a separate server for running the model and make it part of the MSA (Microservices Architecture) of our project.

2. Building a Python Flask server to run the AI model, and connecting the model to the Nest project using the Flask server

This is the task to be done.