add makefile + move model_server to src

makefile

@@ -0,0 +1,12 @@
+local-run: 
+	python3 src/model_server.py & python3 src/pipeline/app.py
+	bash scripts/kill_model_server.sh
+
+run-pipeline:
+	python3 src/pipeline/app.py
+
+model-server:
+	python3 src/model_server.py
+
+kill-model-server:
+	bash scripts/kill_model_server.sh

scripts/kill_model_server.sh

@@ -0,0 +1,16 @@
+echo "Checking for running model_server.py files..."
+ps aux | grep python | grep whale | grep model_server.py
+
+PID=$(ps aux | grep python | grep whale | grep model_server.py | awk '{print $2}')
+if [ -z "$PID" ]; then
+    echo "No model_server.py files running."
+else
+    echo "Killing PID: $PID"
+    kill -9 $PID
+    sleep 2
+
+    echo "Checking for running model_server.py files..."
+    ps aux | grep python | grep whale | grep model_server.py
+fi
+
+echo "Done."

src/model_server.py

@@ -0,0 +1,54 @@
+from flask import Flask, request, jsonify
+import tensorflow_hub as hub
+import numpy as np
+import tensorflow as tf
+
+import logging
+
+
+# Load the TensorFlow model
+print("Loading model...")
+# model = hub.load("https://www.kaggle.com/models/google/humpback-whale/TensorFlow2/humpback-whale/1")
+model = hub.load("https://tfhub.dev/google/humpback_whale/1")
+score_fn = model.signatures["score"]
+print("Model loaded.")
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Define the predict endpoint
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Parse the request data
+        data = request.json
+        batch = np.array(data['batch'], dtype=np.float32)  # Assuming batch is passed as a list
+        key = data['key']
+        print(f"batch.shape = {batch.shape}")
+
+        # Prepare the input for the model
+        waveform_exp = tf.expand_dims(batch, 0)  # Expanding dimensions to fit model input shape
+        print(f"waveform_exp.shape = {waveform_exp.shape}")
+
+        # Run inference
+        results = score_fn(
+            waveform=waveform_exp, # waveform_exp,
+            context_step_samples=10_000
+        )["scores"][0] # NOTE currently only support batch size 1
+        print(f"results.shape = {results.shape}")
+        print("results = ", results)
+
+        # Return the predictions and key as JSON
+        return jsonify({
+            'key': key,
+            'predictions': results.numpy().tolist()
+        })
+
+    except Exception as e:
+        logging.error(f"An error occurred: {str(e)}")
+        print(f"An error occurred: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+# Main entry point
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=5000, debug=True)