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import os |
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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1' |
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model = tf.keras.models.load_model('plant_disease_classifier.h5') |
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def predict(input_image): |
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try: |
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input_image = tf.convert_to_tensor(input_image) |
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input_image = tf.image.resize(input_image, [256, 256]) |
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input_image = tf.expand_dims(input_image, 0) / 255.0 |
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predictions = model.predict(input_image) |
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labels = ['Healthy', 'Powdery', 'Rust'] |
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class_idx = np.argmax(predictions) |
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class_label = labels[class_idx] |
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confidence = np.round(predictions[0][class_idx] * 100, 3) |
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return f"Predicted Class: {class_label}. Confidence Score: {confidence}%" |
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except Exception as e: |
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return f"An error occurred: {e}" |
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examples = ["Healthy.png", "Powdery.png", "Rust.png"] |
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iface = gr.Interface( |
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fn=predict, |
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inputs=gr.Image(shape=(256, 256)), |
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outputs="text", |
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title="🌿 Plant Disease Detection", |
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description='<br> This is a specialized Image Classification model engineered to identify the health status of plants, specifically detecting conditions of Powdery Mildew or Rust. <br> \ |
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This model is based on a Convolutional Neural Network that I have trained, evaluated, and validated on my Kaggle Notebook: <a href="https://www.kaggle.com/code/lusfernandotorres/convolutional-neural-network-from-scratch">🧠 Convolutional Neural Network From Scratch</a>. <br> \ |
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<br> Upload a photo of a plant to see how the model classifies its status!', |
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examples=examples |
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) |
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iface.launch(share=True) |
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