Update app.py and pipeline.py

app.py

@@ -1,9 +1,6 @@
 import gradio as gr
 from datasets import load_dataset, Image
 
-dataset = load_dataset("botmaster/mother-2-battle-sprites", split="train")
-gr.Interface.load("models/templates/text-to-image").launch()
-
 import torch
 import nltk
 import io
@@ -43,3 +40,6 @@ class PreTrainedPipeline():
         img = output[0]
         img = (img + 1) / 2.0
         img = transforms.ToPILImage()(img)
+
+dataset = load_dataset("botmaster/mother-2-battle-sprites", split="train")
+gr.Interface.load("models/templates/text-to-image").launch()

pipeline.py

@@ -1,39 +0,0 @@
-import torch
-import nltk
-import io
-import base64
-import shutil
-from torchvision import transforms
-
-from pytorch_pretrained_biggan import BigGAN, one_hot_from_names, truncated_noise_sample
-
-class PreTrainedPipeline():
-    def __init__(self, path=""):
-        """
-        Initialize model
-        """
-        nltk.download('wordnet')
-        self.model = BigGAN.from_pretrained(path)
-        self.truncation = 0.1
-
-    def __call__(self, inputs: str):
-        """
-        Args:
-            inputs (:obj:`str`):
-                a string containing some text
-        Return:
-            A :obj:`PIL.Image` with 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)
-
-        # Scale image
-        img = output[0]
-        img = (img + 1) / 2.0
-        img = transforms.ToPILImage()(img)