app.py

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
import IPython


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 = """<h1 id="title">Plant Health Classification with ViT</h1>"""

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.HTML('<center><img src="file/images/Healthy.png" width=350px height=350px></center>')

    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=(450,450),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)