Update pipeline.py

pipeline.py

@@ -16,29 +16,26 @@ class PreTrainedPipeline():
         self.model = BigGAN.from_pretrained(path)
         self.truncation = 0.1
 
-
-    def __call__(self, inputs: str) -> str:
+    def __call__(self, inputs: str):
         """
         Args:
             inputs (:obj:`str`):
                 a string containing some text
         Return:
-            A :obj:`np.array`. A np.array containing the image information.
+            A :obj:`PIL.Image`. The raw image representation as PIL.
         """
         class_vector = one_hot_from_names([inputs], batch_size=1)
         if type(class_vector) == type(None):
             raise ValueError("Input is not in ImageNet")
-
         noise_vector = truncated_noise_sample(truncation=self.truncation, batch_size=1)
-
         noise_vector = torch.from_numpy(noise_vector)
         class_vector = torch.from_numpy(class_vector)
-
         with torch.no_grad():
-            output = self.model(noise_vector, class_vector, self.truncation)
+            output = self.model(noise_vector, class_vector, truncation)
 
         img = transforms.ToPILImage()(output[0])
-        buf = io.BytesIO()
-        img.save(buf, format="JPEG")
+        buffer = BytesIO()
+        img.save(buffer, format="JPEG")
+        img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
 
-        return base64.encodebytes(buf.getvalue()).decode('utf-8')
+        return img_str