init demo

app.py

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+import torch
+from transformers import ViTImageProcessor, AutoFeatureExtractor, AutoModelForImageClassification
+import gradio as gr
+
+image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
+
+extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("saved_model_files")
+
+labels = ['angular_leaf_spot', 'bean_rust', 'healthy'] 
+
+def classify(image):
+  features = image_processor(image, 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)} 
+  print(confidences)
+  return confidences
+
+
+theme = gr.themes.Soft(
+    primary_hue="green",
+    secondary_hue="green",
+    neutral_hue="green",
+).set(
+    block_background_fill_dark='*body_background_fill',
+    button_border_width='*block_label_border_width',
+    button_border_width_dark='*checkbox_label_border_width'
+)
+
+with gr.Blocks(theme=theme) as demo:
+
+    inference = gr.Interface(fn=classify, inputs="image", outputs="label",
+                        title="Plant leaves Classification",
+                    description="Classify the leaves b uploading their image",
+                    examples=["images/1.png","images/2.png", "images/3.png"])
+
+demo.launch(share=True)

requirements.txt

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+torch
+transformers==4.25.1
+gradio

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": "4.28.1"
+}

saved_model_files/preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

saved_model_files/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6fa2eede4696254ccbe431ab81f3a02bdf28702b1b8b6a33113181ba7567803
+size 343271789