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Upload 5 files
Browse files- app.py +74 -0
- ex1.jpg +0 -0
- ex2.jpeg +0 -0
- ex3.jpg +0 -0
- requirements.txt +5 -0
app.py
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import torch
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import gradio as gr
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification
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extractor = AutoFeatureExtractor.from_pretrained("susnato/plant_disease_detection-beans")
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model = AutoModelForImageClassification.from_pretrained("susnato/plant_disease_detection-beans")
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labels = ['angular_leaf_spot', 'rust', 'healthy']
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def classify(im):
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features = extractor(im, return_tensors='pt')
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logits = model(features["pixel_values"])[-1]
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probability = torch.nn.functional.softmax(logits, dim=-1)
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probs = probability[0].detach().numpy()
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confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
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return confidences
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block = gr.Blocks(theme="JohnSmith9982/small_and_pretty")
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with block:
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gr.HTML(
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"""
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<h1 align="center">PLANT DISEASE DETECTION<h1>
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"""
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)
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with gr.Group():
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with gr.Row():
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gr.HTML(
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"""
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<p style="color:black">
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<h4 style="font-color:powderblue;">
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<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>
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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>
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</h4>
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</p>
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<p align="center">
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<img src="https://huggingface.co/datasets/susnato/stock_images/resolve/main/merged.png">
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</p>
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"""
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)
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with gr.Group():
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with gr.Row():
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gr.HTML(
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"""
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<center><h3>Our Approach</h3></center>
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<p align="center">
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<img src="https://huggingface.co/datasets/susnato/stock_images/resolve/main/diagram2.png">
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</p>
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"""
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)
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with gr.Group():
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image = gr.Image(type='pil')
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outputs = gr.Label()
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button = gr.Button("Classify")
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button.click(classify,
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inputs=[image],
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outputs=[outputs],
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)
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with gr.Group():
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gr.Examples(["ex3.jpg"],
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fn=classify,
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inputs=[image],
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outputs=[outputs],
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cache_examples=True
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)
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block.launch(debug=False, share=False)
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ex1.jpg
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ex2.jpeg
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ex3.jpg
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requirements.txt
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@@ -0,0 +1,5 @@
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torch
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datasets
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torchvision
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git+https://github.com/huggingface/transformers
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gradio
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