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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) |