import io import gradio as gr import requests, validators import torch import pathlib from PIL import Image import datasets from transformers import AutoFeatureExtractor, AutoModelForImageClassification import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" feature_extractor = AutoFeatureExtractor.from_pretrained("saved_model_files") model = AutoModelForImageClassification.from_pretrained("saved_model_files") labels = ['angular_leaf_spot', 'bean_rust', 'healthy'] def classify(im): '''FUnction for classifying plant health status''' features = feature_extractor(im, return_tensors='pt') with torch.no_grad(): logits = model(**features).logits 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 def get_original_image(url_input): '''Get image from URL''' if validators.url(url_input): image = Image.open(requests.get(url_input, stream=True).raw) return image def detect_plant_health(url_input,image_input,webcam_input): if validators.url(url_input): image = Image.open(requests.get(url_input, stream=True).raw) elif image_input: image = image_input elif webcam_input: image = webcam_input #Make prediction label_probs = classify(image) return label_probs def set_example_image(example: list) -> dict: return gr.Image.update(value=example[0]) def set_example_url(example: list) -> dict: return gr.Textbox.update(value=example[0]), gr.Image.update(value=get_original_image(example[0])) title = """

Plant Health Classification with ViT

""" gr.Image('images/Healthy.png',label = 'Healthy Plant') gr.Image('images/sickie.png',label = 'Infected Plant') description = """ This Plant Health classifier app was built to detect the health of plants using images of leaves by fine-tuning a Vision Transformer (ViT) [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [Beans](https://huggingface.co/datasets/beans) dataset. The finetuned model has an accuracy of 98.4% on the test (unseen) dataset and 100% on the validation dataset. How to use the app: - Upload an image via 3 options, uploading the image from local device, using a URL (image from the web) or a webcam - The app will take a few seconds to generate a prediction with the following labels: - *angular_leaf_spot* - *bean_rust* - *healthy* - Feel free to click the image examples as well. """ urls = ["https://www.healthbenefitstimes.com/green-beans/","https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg", "https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg"] images = [[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.p*g'))] twitter_link = """ [![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi) """ css = ''' h1#title { text-align: center; } ''' demo = gr.Blocks(css=css) with demo: gr.Markdown(title) gr.Markdown(description) gr.Markdown(twitter_link) with gr.Tabs(): with gr.TabItem('Image Upload'): with gr.Row(): with gr.Column(): img_input = gr.Image(type='pil',shape=(450,450)) label_from_upload= gr.Label(num_top_classes=3) with gr.Row(): example_images = gr.Examples(examples=images,inputs=[img_input]) img_but = gr.Button('Classify') with gr.TabItem('Image URL'): with gr.Row(): with gr.Column(): url_input = gr.Textbox(lines=2,label='Enter valid image URL here..') original_image = gr.Image(shape=(450,450)) url_input.change(get_original_image, url_input, original_image) with gr.Column(): label_from_url = gr.Label(num_top_classes=3) with gr.Row(): example_url = gr.Examples(examples=urls,inputs=[url_input]) url_but = gr.Button('Classify') with gr.TabItem('WebCam'): with gr.Row(): with gr.Column(): web_input = gr.Image(source='webcam',type='pil',shape=(750,750),streaming=True) with gr.Column(): label_from_webcam= gr.Label(num_top_classes=3) cam_but = gr.Button('Classify') url_but.click(detect_plant_health,inputs=[url_input,img_input,web_input],outputs=[label_from_url],queue=True) img_but.click(detect_plant_health,inputs=[url_input,img_input,web_input],outputs=[label_from_upload],queue=True) cam_but.click(detect_plant_health,inputs=[url_input,img_input,web_input],outputs=[label_from_webcam],queue=True) gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-plant-health)") demo.launch(debug=True,enable_queue=True)