add implementation/interface

app.py

@@ -2,21 +2,22 @@ import datasets
 import gradio as gr
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 
-def classify(im):
-  features = feature_extractor(im, return_tensors='pt')
-  logits = model(features['pixel_values'])[-1]
-  probability = torch.nn.functional.softmax(logits, dim=-1)
-  probs = probability[0].detach().numpy()
-  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 
-  return confidences
-
 dataset = datasets.load_dataset('beans', 'full_size')
 
+feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
 extractor = AutoFeatureExtractor.from_pretrained('saved_model_files')
 model = AutoModelForImageClassification.from_pretrained('saved_model_files')
 
 labels = dataset['train'].features['labels'].names
 
+def classify(im):
+  features = extractor(im, return_tensors='pt')
+  logits = model(features['pixel_values'])[-1]
+  probability = torch.nn.functional.softmax(logits, dim=-1)
+  probs = probability[0].detach().numpy()
+  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 
+  return confidences
+
 interface = gr.Interface(fn=classify, inputs=gr.Image(shape=(200, 200)),  outputs=gr.outputs.Label(num_top_classes=3),
              examples=['leaf1.png', 'leaf2.png'], title='Leaf Classification App', description='Check if the leaves of your plant are healthy!', flagging_dir='flagged_examples/')