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| import gradio as gr | |
| from transformers import pipeline | |
| def predict_image(img): | |
| img_4d=img.reshape(-1,180,180,3) | |
| prediction=model.predict(img_4d)[0] | |
| return {class_names[i]: float(prediction[i]) for i in range(4)} | |
| pipe = pipeline(task = 'image-classification', model = "google/vit-base-patch16-224") | |
| image = gr.inputs.Image(shape=(180,180)) | |
| label = gr.outputs.Label(num_top_classes=4) | |
| gr.Interface.from_pipeline(pipe, | |
| title = "Maize Leaf Disease Detection", | |
| description = "Corn Leaf disease classification", | |
| fn=predict_image, inputs=image, outputs=label,interpretation='default', | |
| allow_flagging = "never").launch(inbrowser=True, debug='True', share='True') | |