launch app

app.py

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+import datasets
+import torch
+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: gr.inputs.Image) -> dict:
+    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="image",
+                         outputs="label",
+                         title="Bean classifier",
+                         description="Web-application that can take in an image of a bean leaf and predict whether it is healthy or diseased",
+                         examples = ['./images/img_0.png'])
+interface.launch(debug=True)

requirements.txt

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+datasets==2.12.0
+gradio==3.28.1
+gradio_client==0.1.4
+huggingface-hub==0.14.1
+torch==2.0.0
+transformers==4.28.1

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:b9b1703e5b5b9c051f0fa9dae5e5550b9486a69e859faab86177d5b3071aa453
+size 343271789