# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb. # %% auto 0 __all__ = ['plt', 'learn', 'interface_options', 'demo', 'label_func', 'predict'] # %% ../app.ipynb 2 from fastai.vision.all import * import gradio #import pathlib #temp = pathlib.PosixPath #pathlib.PosixPath = pathlib.WindowsPath import pathlib plt = platform.system() if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath def label_func(filepath): return filepath.parent.name # %% ../app.ipynb 3 learn = load_learner('model.pkl') # %% ../app.ipynb 5 def predict(image): img = PILImage.create(img) _pred, _pred_w_idx, probs = learn.predict(img) labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)} return labels_probs # %% ../app.ipynb 6 interface_options = { "title": "KhetAi", "description": "An web app that predicts the disease based on Image", "interpretation": "default", "layout": "horizontal", "allow_flagging": "never", "enable_queue": True } """demo = gradio.Interface(fn=predict, inputs=gradio.inputs.Image(shape=(512, 512)), outputs=gradio.outputs.Label(num_top_classes=5), **interface_options)""" demo = gradio.Interface(fn=predict, inputs="image", outputs="label") # demo_options = { # "inline": True, # "inbrowser": True, # "share": True, # "show_error": True, # "server_name": "0.0.0.0", # "server_port": 5000, # "enable_queue": True, # } #demo.launch(**demo_options) demo.launch(share=True)