Remove object oriented programming from prediction

Author: Patrick Kuehn

autofocus/predict/app/app.py

@@ -3,11 +3,11 @@
 from flask import Flask, jsonify, request
 
 from .models.File import File
-from .models.Predictor import Predictor
 from .models.ZipArchive import ZipArchive
 from .validation.predict import validate_predict_request
 from .validation.predict_zip import validate_predict_zip_request
 from .validation.validation import abort_with_errors
+from .prediction.prediction import predict, predict_multiple
 
 
 # We are going to upload the files to the server as part of the request, so set tmp folder here.
@@ -27,16 +27,8 @@ def classify_single():
     # Get File object
     file = File(request.files["file"], app.config["UPLOAD_FOLDER"])
 
-    # Predict probabilities
-    app.logger.info("Classifying image %s" % (file.getPath()))
-    t = time.time()
-    predictor = Predictor()
-    predictor.predict(file)
-    dt = time.time() - t
-    app.logger.info("Execution time: %0.2f" % (dt * 1000.0))
-
     # Return ziped probabilities
-    return jsonify(predictor.getProbabilities())
+    return jsonify(predict(file))
 
 
 @app.route("/predict_zip", methods=["POST"])
@@ -56,8 +48,7 @@ def classify_zip():
     files = file.extractAll(app.config["UPLOAD_FOLDER"], file.listAllImages())
 
     # Make prediction
-    predictor = Predictor()
-    return jsonify(predictor.predict_multiple(files))
+    return jsonify(predict_multiple(files))
 
 
 @app.route("/hello")

autofocus/predict/app/models/Predictor.py

@@ -0,0 +1,46 @@
+from pathlib import Path
+
+from fastai.vision import load_learner, open_image
+
+
+MODEL_DIR = Path(__file__).resolve().parents[2] / "models"
+MODEL_NAME = "multilabel_model_20190407.pkl"
+model = load_learner(MODEL_DIR, MODEL_NAME)
+CLASSES = model.data.classes
+
+
+def predict_multiple(files):
+    """
+    Predict probabilities of multiple files
+
+    Parameters:
+        files: Dict with File objects of image file
+
+    Returns:
+        dict: Dictionary of probabilities for each file in files
+    """
+    predictions = {}
+    for key in files:
+        predictions[key] = predict(files[key])
+    return predictions
+
+def predict(file):
+    """
+    Predict probabilities of single file
+
+    Parameters:
+        file: File object of image file
+    """
+    image = open_image(file.getPath())
+    # Get the predictions (output of the softmax) for this image
+    pred_classes, preds, probs = model.predict(image)
+    return getProbabilities([prob.item() for prob in probs])
+
+def getProbabilities(probabilities):
+    """
+    Return formated Probabilities
+
+    Returns:
+        dict: A dictionary of classes to probabilities
+    """
+    return dict(zip(CLASSES, probabilities))