adding app

app.py

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+import datasets
+import torch
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+description = Upload a picture of your bean plant to determine if they are healthy or diseased
+title = Bean Plant Disease Identifier
+
+dataset =  datasets.load_dataset('beans')
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(saved_model_files)
+model = AutoModelForImageClassification.from_pretrained(saved_model_files)
+
+labels = dataset['train'].features['labels'].names
+
+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
+
+import gradio as gr
+
+interface = gr.Interface(
+    classify,
+    inputs='image',
+    outputs='label',
+    title=title,
+    description=description
+    )
+
+interface.launch(debug=True)

config.json

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+{
+  _name_or_path: google/vit-base-patch16-224,
+  architectures: [
+    ViTForImageClassification
+  ],
+  attention_probs_dropout_prob: 0.0,
+  encoder_stride: 16,
+  hidden_act: gelu,
+  hidden_dropout_prob: 0.0,
+  hidden_size: 768,
+  id2label: {
+    0: angular_leaf_spot,
+    1: bean_rust,
+    2: healthy
+  },
+  image_size: 224,
+  initializer_range: 0.02,
+  intermediate_size: 3072,
+  label2id: {
+    angular_leaf_spot: 0,
+    bean_rust: 1,
+    healthy: 2
+  },
+  layer_norm_eps: 1e-12,
+  model_type: vit,
+  num_attention_heads: 12,
+  num_channels: 3,
+  num_hidden_layers: 12,
+  patch_size: 16,
+  problem_type: single_label_classification,
+  qkv_bias: true,
+  torch_dtype: float32,
+  transformers_version: 4.28.1
+}

requirements.txt

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+torch
+transformers