refactor imports

app.py

@@ -1,4 +1,7 @@
-import gradio as gr
+import os
+import re
+from datetime import datetime
+
 from PIL import Image
 import numpy as np
 import json
@@ -9,6 +12,9 @@ import torch
 import matplotlib.pyplot as plt
 import matplotlib.ticker as ticker
 from huggingface_hub import HfApi
+import gradio as gr
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
@@ -82,14 +88,14 @@ class Network(nn.Module):
 
         return F.log_softmax(x, dim=1)
     
-    def process_image(img_path):
-        """Scales, crops, and normalizes a PIL image for a PyTorch model,
-        returns a Numpy array
+def process_image(img_path):
+    """Scales, crops, and normalizes a PIL image for a PyTorch model,
+    returns a Numpy array
 
-        Arguments
-        ---------
-        image: path of the image to be processed
-        """
+    Arguments
+    ---------
+    image: path of the image to be processed
+    """
     inp = Image.open(img_path)
     exif = inp.getexif()
 
@@ -212,5 +218,5 @@ gr.Interface(
     predict,
     inputs=gr.inputs.Image(label="Upload a flower image", type="filepath"),
     outputs=gr.outputs.Label(num_top_classes=5),
-    title="What kind of flower is this?",
+    title="What kind of flower is this?"
 ).launch()