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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 by uploading image", | |
| examples=["images/1.png","images/2.png", "images/3.png"]) | |
| demo.launch() |