Updated from colab

app.py

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+import datasets
+import gradio as gr
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+dataset = datasets.load_dataset("beans")
+
+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
+
+interface = gr.Interface(fn = classify, 
+                         inputs="image",
+                         outputs = "label",
+                         title = "Plant Leaf Disease Classifier",
+                         description = """Below is a simple app to detect Angular Leaf Spot and Bean Rust diseases on leaves.
+                          Data was annotated by experts from the National Crops Resources Research Institute (NaCRRI) 
+                          in Uganda and collected by the Makerere AI research lab.""",
+                         examples = example_imgs)
+
+interface.launch(debug=True)

requirements.txt

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+torch
+transformers

saved_model_files/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": "4.22.1"
+}

saved_model_files/preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "size": 224
+}

saved_model_files/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9733559e22b0843348b12a1c16e5b7708d16af1a77a2f8f2921fd6cb92dddf9
+size 343270065

saved_model_files/training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19660b89e8bce9dbc2d42536b1ea72ce8f25433773a9620349bcf86562844668
+size 3375