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app.py

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+import io
+import gradio as gr
+import matplotlib.pyplot as plt
+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("/content/drive/MyDrive/Week 1 Project/saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("/content/drive/MyDrive/Week 1 Project/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('*.j*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=(750,750))
+                  label_from_upload= gr.Label()
+                
+            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=(750,750))
+                    url_input.change(get_original_image, url_input, original_image)
+                with gr.Column():
+                    label_from_url = gr.Label()
+                
+            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()
+
+            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)