add implementation/interface

app.py

@@ -0,0 +1,23 @@
+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')
+
+extractor = AutoFeatureExtractor.from_pretrained('saved_model_files')
+model = AutoModelForImageClassification.from_pretrained('saved_model_files')
+
+labels = dataset['train'].features['labels'].names
+
+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/')
+
+interface.launch(debug=True)

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+transformers
+torch