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import gradio as gr
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
# Load the model
model = load_model('model1.h5')
# Define class indices
class_indices = {0: 'Apple___Apple_scab',
1: 'Apple___Black_rot',
2: 'Apple___Cedar_apple_rust',
3: 'Apple___healthy',
4: 'Blueberry___healthy',
5: 'Cherry_(including_sour)___Powdery_mildew',
6: 'Cherry_(including_sour)___healthy',
7: 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
8: 'Corn_(maize)___Common_rust_',
9: 'Corn_(maize)___Northern_Leaf_Blight',
10: 'Corn_(maize)___healthy',
11: 'Grape___Black_rot',
12: 'Grape___Esca_(Black_Measles)',
13: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
14: 'Grape___healthy',
15: 'Orange___Haunglongbing_(Citrus_greening)',
16: 'Peach___Bacterial_spot',
17: 'Peach___healthy',
18: 'Pepper,_bell___Bacterial_spot',
19: 'Pepper,_bell___healthy',
20: 'Potato___Early_blight',
21: 'Potato___Late_blight',
22: 'Potato___healthy',
23: 'Raspberry___healthy',
24: 'Soybean___healthy',
25: 'Squash___Powdery_mildew',
26: 'Strawberry___Leaf_scorch',
27: 'Strawberry___healthy',
28: 'Tomato___Bacterial_spot',
29: 'Tomato___Early_blight',
30: 'Tomato___Late_blight',
31: 'Tomato___Leaf_Mold',
32: 'Tomato___Septoria_leaf_spot',
33: 'Tomato___Spider_mites Two-spotted_spider_mite',
34: 'Tomato___Target_Spot',
35: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
36: 'Tomato___Tomato_mosaic_virus',
37: 'Tomato___healthy'}
# Preprocess the image
def preprocess_image(image):
# Resize the image
image = Image.fromarray(image).resize((224, 224))
# Convert to numpy array and scale the values
image = np.array(image).astype('float32') / 255.0
# Add batch dimension
image = np.expand_dims(image, axis=0)
return image
# Predict the class of the image
def predict_image(image):
preprocessed_image = preprocess_image(image)
predictions = model.predict(preprocessed_image)
predicted_class_index = np.argmax(predictions)
predicted_class_name = class_indices[predicted_class_index]
confidence = round(predictions[0][predicted_class_index], 2)
return predicted_class_name, confidence
def build_gui():
description = """
<center><strong>
<font size='10'>Plant Disease Detection</font>
</strong></center>
<br>
<p>Welcome to the <a href='https://github.com/meet244/Plantex' target='_blank'>Plantex</a> demo!</p>
<p>
PLantex is a plant disease detection model that can predict the disease of a plant based on an image of its leaf.
To use the model, simply upload an image of a plant's leaf and click on the "Predict" button.
The model will then predict the disease of the plant and display the predicted class name along with the confidence score.
</p>
<p>Great thanks to <a href='https://huggingface.co/meet244' target='_blank'>Meet Patel</a>, the major contributor of this
demo!
</p>
""" # noqa
article = """
<p style='text-align: center'>
Model is trained on public dataset, and we are persisting in refining and iterating upon it.<br/>
<a href='https://github.com/meet244/Plantex' target='_blank'>Plantex - Plant disease detection & organic waste management</a>
</p>
""" # noqa
with gr.Blocks(title="Plantex - Disease detetion model") as demo:
gr.HTML(description)
gr.Interface(
fn=predict_image,
inputs=gr.Image(label="Plant's leaf Image"),
outputs=[gr.Textbox(label="Predicted"),gr.Textbox(label="Confidence")],
# examples=[
# ["test_apple_black_rot.jpg"],
# ["test_blueberry_healthy.jpg"],
# ["test_potato_early_blight.jpg"]
# ],
cache_examples=True,
allow_flagging='never'
)
gr.HTML(article)
return demo
if __name__ == "__main__":
build_gui().launch() |