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app.py

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+import torch
+import gradio as gr
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+
+extractor = AutoFeatureExtractor.from_pretrained("susnato/plant_disease_detection-beans")
+model = AutoModelForImageClassification.from_pretrained("susnato/plant_disease_detection-beans")
+
+labels = ['angular_leaf_spot', 'rust', 'healthy']
+
+def classify(im):
+  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
+
+
+block = gr.Blocks(theme="JohnSmith9982/small_and_pretty")
+with block:
+    gr.HTML(
+        """
+        <h1 align="center">PLANT DISEASE DETECTION<h1>
+        """
+    )
+    with gr.Group():
+        with gr.Row():
+            gr.HTML(
+                """
+                <p style="color:black">
+                    <h4 style="font-color:powderblue;">
+                        <center>Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. <br><br>
+                        Using Computer Vision models in plant disease detection and diagnosis has the potential to revolutionize the way we approach agriculture. By providing real-time monitoring and accurate detection of plant diseases, A.I. can help farmers reduce costs and increase crop</center>
+                    </h4>
+                </p>
+                
+                <p align="center">
+                    <img src="https://huggingface.co/datasets/susnato/stock_images/resolve/main/merged.png">
+                </p>
+                """
+            )
+
+    with gr.Group():
+        with gr.Row():
+            gr.HTML(
+                """
+                <center><h3>Our Approach</h3></center>
+                
+                <p align="center">
+                    <img src="https://huggingface.co/datasets/susnato/stock_images/resolve/main/diagram2.png">
+                </p>
+                """
+            )
+
+    with gr.Group():
+        image = gr.Image(type='pil')
+        outputs = gr.Label()
+        button = gr.Button("Classify")
+
+        button.click(classify,
+                     inputs=[image],
+                     outputs=[outputs],
+                     )
+
+    with gr.Group():
+        gr.Examples(["ex3.jpg"],
+            fn=classify,
+            inputs=[image],
+            outputs=[outputs],
+            cache_examples=True
+        )
+
+block.launch(debug=False, share=False)

requirements.txt

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+torch
+datasets
+torchvision
+git+https://github.com/huggingface/transformers
+gradio