Update pipeline.py

pipeline.py

@@ -1,37 +1,25 @@
 from typing import Dict, List, Any
 from fastai.learner import load_learner
-import fastai
-from fastbook import *
 from PIL import Image
 import os
 import json
 import numpy as np
 
-dls = DataBlock(
-    blocks=(ImageBlock, CategoryBlock), 
-    get_items=get_image_files, 
-    splitter=RandomSplitter(valid_pct=0.2, seed=42),
-    get_y=parent_label,
-    item_tfms=[Resize(192, method='squish')]
-).dataloaders(path, bs=32)
-
-print('PIPELINE')
-
 class ImageClassificationPipeline:
-    def __init__(self, path=""):
-        # IMPLEMENT_THIS
-        # Preload all the elements you are going to need at inference.
-        # For instance your model, processors, tokenizer that might be needed.
-        # This function is only called once, so do all the heavy processing I/O here"""
-        print('init')
-        self.model = load_learner(os.path.join(path, "model.pkl"))
-        with open(os.path.join(path, "config.json")) as config:
-            config = json.load(config)
-        self.labels = config["labels"]
 
-    def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
-        print('call')
-        """
+  def __init__(self, path=""):
+    # IMPLEMENT_THIS
+    # Preload all the elements you are going to need at inference.
+    # For instance your model, processors, tokenizer that might be needed.
+    # This function is only called once, so do all the heavy processing I/O here"""
+    self.model = load_learner(os.path.join(path, "model.pkl"))
+    with open(os.path.join(path, "config.json")) as config:
+      config = json.load(config)
+    self.labels = config["labels"]
+
+  def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
+    print('call')
+    """
         Args:
             inputs (:obj:`PIL.Image`):
                 The raw image representation as PIL.
@@ -40,8 +28,11 @@ class ImageClassificationPipeline:
             A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
                 It is preferred if the returned list is in decreasing `score` order
         """
-        # IMPLEMENT_THIS
-        # FastAI expects a np array, not a PIL Image.
-        _, _, preds = self.model.predict(np.array(inputs))
-        preds = preds.tolist()
-        return [{"label": label, "score": preds[idx]} for idx, label in enumerate(self.labels)]
+    # IMPLEMENT_THIS
+    # FastAI expects a np array, not a PIL Image.
+    _, _, preds = self.model.predict(np.array(inputs))
+    preds = preds.tolist()
+    return [{
+      "label": label,
+      "score": preds[idx]
+    } for idx, label in enumerate(self.labels)]