import gradio as gr
from tensorflow.keras.utils import img_to_array,load_img
from keras.models import load_model
import numpy as np
# Load the pre-trained model from the local path
model_path = 'sugar.h5'
model = load_model(model_path) # Load the model here
def predict_disease(image_file, model, all_labels):
try:
# Load and preprocess the image
img = load_img(image_file, target_size=(224, 224)) # Use load_img from tensorflow.keras.utils
img_array = img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array = img_array / 255.0 # Normalize the image
# Predict the class
predictions = model.predict(img_array) # Use the loaded model here
predicted_class = np.argmax(predictions[0])
# Get the predicted class label
predicted_label = all_labels[predicted_class]
# Print the predicted label to the console
if predicted_label=='Sugarcane Healthy':
predicted_label = predicted_label = """Sugarcane Healthy
No need use Pesticides"""
elif predicted_label=='Sugarcane Yellow':
predicted_label = """
Sugarcane Yellow
PESTICIDES TO BE USED:
- 1. Insecticidal Soap
- 2. Pyrethroids
- 3. Imidacloprid
- 4. Bacillus thuringiensis
- 5. Spinosad
* * * IMPORTANT NOTE * * *
Be sure to follow local regulations and guidelines for application
"""
elif predicted_label=='Sugarcane Rust':
predicted_label = """
Sugarcane Rust
PESTICIDES TO BE USED:
- 1. Triadimefon
- 2. Chlorothalonil
- 3. Tebuconazole
- 4. Propiconazole
* * * IMPORTANT NOTE * * *
Be sure to follow local regulations and guidelines for application
"""
elif predicted_label=='Sugarcane RedRot':
predicted_label = """
Sugarcane RedRot
PESTICIDES TO BE USED:
- 1. Mancozeb
- 2. Chlorothalonil
- 3. Tebuconazole
- 4. Carbendazim
* * * IMPORTANT NOTE * * *
Be sure to follow local regulations and guidelines for application
"""
elif predicted_label=='Sugarcane Mosaic':
predicted_label = """
Sugarcane Mosaic
PESTICIDES TO BE USED:
- 1. Horticultural Oil
- 2. Spinosad
- 3. Pyrethrin
- 4. Neem Oil
- 5. Imidacloprid
* * * IMPORTANT NOTE * * *
Be sure to follow local regulations and guidelines for application
"""
else:
predicted_label = """Choose Correct image
"""
return predicted_label
except Exception as e:
print(f"Error: {e}")
return None
# List of class labels
all_labels = [
'Sugarcane Yellow',
'Sugarcane Rust',
'Sugarcane RedRot','Sugarcane Mosaic',
'Sugarcane Healthy'
]
# Define the Gradio interface
def gradio_predict(image_file):
return predict_disease(image_file, model, all_labels) # Pass the model to the function
# Create a Gradio interface
gr_interface = gr.Interface(
fn=gradio_predict, # Function to call for predictions
inputs=gr.Image(type="filepath"), # Upload image as file path
outputs="html", # Output will be the class label as text
title="Sugarcane Disease Predictor",
description="Upload an image of a plant to predict the disease.",
)
# Launch the Gradio app
gr_interface.launch(debug=True)