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| ## libraries for data preprocessing | |
| import numpy as np | |
| import pandas as pd | |
| ## libraries for training dl models | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| ## libraries for pre-trained neural network | |
| from tensorflow.keras.applications.xception import preprocess_input | |
| ## libraries for loading batch images | |
| from tensorflow.keras.preprocessing.image import load_img | |
| import gradio as gr | |
| ## lets load the model | |
| model = keras.models.load_model('xception_v1_17_0.859.h5') | |
| def maize_disease_classifier(image): | |
| x = np.array(image) | |
| X = np.array([x]) | |
| X = preprocess_input(X) | |
| pred = model.predict(X) | |
| result = pred[0].argmax() | |
| ## lets create our labels | |
| labels = { | |
| 0: 'maize ear rot', | |
| 1: 'maize fall armyworm', | |
| 2: 'maize stem borer' | |
| } | |
| label = labels[pred[0].argmax()] | |
| return label | |
| ################### Gradio Web APP ################################ | |
| title = "Maize Disease Classification App" | |
| Input = gr.Image(shape=(299, 299), label="Please Upload An Image") | |
| Output1 = gr.Textbox(label="Type Of Maize Disease") | |
| description = "Type Of Diseases: Maize Ear Rot, Maize Fall ArmyWorm, Maize Stem Borer" | |
| iface = gr.Interface(fn=maize_disease_classifier, inputs=Input, outputs=Output1, title=title, description=description) | |
| iface.launch(inline=False) |