Do batch prediction.

Do batch prediction inside the `predict` method instead of calling
`predict` once for each image.

autotagger/autotagger.py

@@ -1,5 +1,5 @@
 from fastbook import *
-from pandas import read_csv
+from pandas import DataFrame, read_csv
 import timm
 
 class Autotagger:
@@ -26,10 +26,13 @@ class Autotagger:
 
         return learn
 
-    def predict(self, path, threshold=0.01, limit=50):
+    def predict(self, images, threshold=0.01, limit=50, bs=64):
         with self.learn.no_bar(), self.learn.no_logging():
-            pred = self.learn.predict(path)
-            scores = [score.item() for score in pred[2]]
-            results = { tag : score for (tag, score) in zip(self.learn.dls.vocab, scores) if score >= threshold }
-            results = sorted(results.items(), key = lambda x: -x[1])
-            return dict(results[:limit])
+            dl = self.learn.dls.test_dl(images, bs=bs)
+            batch, _ = self.learn.get_preds(dl=dl)
+
+            for scores in batch:
+                df = DataFrame({ "tag": self.learn.dls.vocab, "score": scores })
+                df = df[df.score >= threshold].sort_values("score", ascending=False).head(limit)
+                tags = dict(zip(df.tag, df.score))
+                yield tags