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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 = 'grape.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=='Grape Healthy':
predicted_label = predicted_label = """<h3 align="center">Grape Healthy</h3><br><br>
<center>No need use Pesticides</center>"""
elif predicted_label=='Grape Isariopsis leaf Spot':
predicted_label = """
<style>
li{
font-size: 15px;
margin-left: 90px;
margin-top: 15px;
margin-bottom: 15px;
}
h4{
font-size: 17px;
margin-top: 15px;
}
h4:hover{
cursor: pointer;
}
h3:hover{
cursor: pointer;
color: blue;
transform: scale(1.3);
}
.note{
text-align: center;
font-size: 16px;
}
p{
font-size: 13px;
text-align: center;
}
</style>
<h3><center><b>Grape Isariopsis leaf Spot</b></center></h3>
<h4>PESTICIDES TO BE USED:</h4>
<ul>
<li>1. Chlorothalonil (Daconil)</li>
<li>2. Mancozeb (Dithane)</li>
<li>3. Copper oxychloride (Kocide)</li>
<li>4. Azoxystrobin (Heritage)</li>
<li>5. Pyraclostrobin (Cabrio)</li>
</ul>
<p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
<p>Be sure to follow local regulations and guidelines for application</p>
"""
elif predicted_label=='Grape Black Rot':
predicted_label = """
<style>
li{
font-size: 15px;
margin-left: 90px;
margin-top: 15px;
margin-bottom: 15px;
}
h4{
font-size: 17px;
margin-top: 15px;
}
h4:hover{
cursor: pointer;
}
h3:hover{
cursor: pointer;
color: blue;
transform: scale(1.3);
}
.note{
text-align: center;
font-size: 16px;
}
p{
font-size: 13px;
text-align: center;
}
</style>
<h3><center><b>Grape Black rot</b></center></h3>
<h4>PESTICIDES TO BE USED:</h4>
<ul>
<li>1. Copper oxychloride (Kocide)</li>
<li>2. Mancozeb(Dithane)</li>
<li>3. Azoxystrobin</li>
<li>4. Chlorothalonil</li>
</ul>
<p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
<p>Be sure to follow local regulations and guidelines for application</p>
"""
elif predicted_label=='Grape Black Measles':
predicted_label = """
<style>
li{
font-size: 15px;
margin-left: 90px;
margin-top: 15px;
margin-bottom: 15px;
}
h4{
font-size: 17px;
margin-top: 15px;
}
h4:hover{
cursor: pointer;
}
h3:hover{
cursor: pointer;
color: blue;
transform: scale(1.3);
}
.note{
text-align: center;
font-size: 16px;
}
p{
font-size: 13px;
text-align: center;
}
</style>
<h3><center><b>Grape Black Measles</b></center></h3>
<h4>PESTICIDES TO BE USED:</h4>
<ul>
<li>1. Copper oxychloride (Kocide)</li>
<li>2. Mancozeb(Dithane)</li>
<li>3. Azoxystrobin</li>
<li>4. Chlorothalonil</li>
</ul>
<p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
<p>Be sure to follow local regulations and guidelines for application</p>
"""
else:
predicted_label = """<h3 align="center">Choose Correct image</h3><br><br>
"""
return predicted_label
except Exception as e:
print(f"Error: {e}")
return None
# List of class labels
all_labels = [
'Grape Isariopsis leaf Spot',
'Grape Healthy',
'Grape Black Rot',
'Grape Black Measles'
]
# 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="Grape Disease Predictor",
description="Upload an image of a plant to predict the disease.",
)
# Launch the Gradio app
gr_interface.launch(share=True) |