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import streamlit as st
from PIL import Image
import joblib
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
from keras.models import Sequential, load_model
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image as keras_image
from skimage import io, color, transform
from skimage.feature import hog
# from keras.layers import Dense,Flatten,BatchNormalization,Dropout
from scipy.special import softmax
# ... (Previous imports and model loading code remain the same)
def preprocess_image(img, model_name):
# if model_name == 'Resnet':
# # Resize image to match ResNet input size (256x256)
# img = img.resize((256, 256))
# img_array = keras_image.img_to_array(img)
# img_array = np.expand_dims(img_array, axis=0)
# img_array = preprocess_input(img_array)
# return img_array
if model_name == 'SVM':
resized_image = transform.resize(np.array(img), (100, 100), mode='reflect')
if resized_image.shape[2] == 4:
resized_image = color.rgba2rgb(resized_image)
grayscale_image = color.rgb2gray(resized_image)
hog_features = hog(grayscale_image, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), block_norm='L2-Hys')
return hog_features
elif model_name == "ANN" or model_name == "CNN":
resized_image = img.resize((256, 256))
img_array = keras_image.img_to_array(resized_image)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0 # Normalize pixel values to be between 0 and 1
return img_array
def predict(model, img, model_name):
img_array = preprocess_image(img, model_name)
# if model_name == 'Resnet':
# predictions = model.predict(img_array)
# decoded_predictions = custom_decode_predictions(predictions)
# return decoded_predictions
if model_name == 'SVM':
# Flatten the array before passing it to the SVM model
img_array_flattened = img_array.flatten().reshape(1, -1)
prediction = model.predict(img_array_flattened)
return prediction[0]
elif model_name == 'ANN' or model_name == 'CNN':
prediction = model.predict(img_array)
confidence_scores = softmax(prediction, axis=1) # Apply softmax to get confidence scores
predicted_class = np.argmax(confidence_scores, axis=1)[0] # Get the class with the highest confidence
confidence = confidence_scores[0, predicted_class]
return predicted_class, confidence
def custom_decode_predictions(predictions):
# Assuming predictions is a 2D array with shape (1, num_classes)
num_classes = predictions.shape[1]
# Assuming predictions contain class probabilities
confidence_scores = predictions[0]
# Get the index of the class with the highest probability
predicted_class_index = np.argmax(confidence_scores)
# Return the class index, confidence score, and probability distribution
return [
predicted_class_index,
confidence_scores[predicted_class_index],
]
# Load the models
pper_model_CNN = load_model("../pepper/models/pepper_CNN.h5")
pper_model_SVM = joblib.load('../pepper/models/pepper_hog_svm_model.pkl')
# pper_model_resnet = load_model('../pepper/models/pepper_resnet50.h5')
potato_model_CNN = load_model('../potato/models/potato_CNN.h5')
potato_model_SVM = joblib.load('../potato/models/potato_hog_svm_model.pkl')
# potato_model_resnet = load_model('../potato/models/potato_resnet50.h5')
tomato_model_CNN = load_model('../tomato/models/tomato_CNN.h5')
tomato_model_SVM = joblib.load('../tomato/models/tomato_hog_svm_model.pkl')
tomato_model_ANN = load_model('../tomato/models/ANN_tomato.h5')
# tomato_model_resnet = load_model('../tomato/models/tomato_resnet50.h5')
Potato_class = 3
Pepper_class = 2
Tomato_class = 10
# Set the title of the app
st.title('Plant Disease Detection App')
# Create a dropdown menu for the user to choose a plant
plant = st.selectbox('Choose a plant', ('Pepper', 'Potato', 'Tomato'))
# Create a radio button for the user to choose a model
if plant == 'Pepper':
no_of_classes=2
class_name = ['pepper_bacterial_spot', 'pepper_bell_healthy']
model = st.radio('Choose a model', ('CNN', 'SVM', ))
if model == 'SVM':
model_func = pper_model_SVM
elif model == 'CNN':
model_func = pper_model_CNN
# elif model == 'Resnet':
# model_func = pper_model_resnet
elif plant == 'Potato':
class_name=['EarlyBlight','Healthy', 'LateBlight']
no_of_classes=2
model = st.radio('Choose a model', ('SVM', 'CNN', ))
if model == 'SVM':
model_func =potato_model_SVM
elif model == 'CNN':
model_func =potato_model_CNN
# elif model == 'Resnet':
# model_func = potato_model_resnet
elif plant == 'Tomato':
class_name=['Tomato___Bacterial_spot',
'Tomato___Early_blight',
'Tomato___Late_blight',
'Tomato___Leaf_Mold',
'Tomato___Septoria_leaf_spot',
'Tomato___Spider_mites Two-spotted_spider_mite',
'Tomato___Target_Spot',
'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
'Tomato___Tomato_mosaic_virus',
'Tomato___healthy']
no_of_classes=10
model = st.radio('Choose a model', ('SVM','CNN', 'ANN', ))
if model == 'SVM':
model_func = tomato_model_SVM
elif model == 'CNN':
model_func = tomato_model_CNN
elif model == 'ANN':
model_func = tomato_model_ANN
# elif model== 'Resnet':
# model_func = tomato_model_resnet
# Create a file uploader for the user to upload an image
uploaded_file = st.file_uploader('Upload an image', type='jpg')
# Display the selected model and plant
st.write('Selected plant:', plant)
st.write('Selected model:', model)
# If an image is uploaded, display it and make a prediction
if uploaded_file is not None:
image = Image.open(uploaded_file)
# st.image(image, caption='Uploaded Image', use_column_width=True)
# Make a prediction using the selected model
prediction_result = predict(model_func, image, model)
print(no_of_classes)
# st.write(prediction_result)
# Display the result based on the model type
if model == 'SVM':
st.write(f'Prediction: {prediction_result}')
else:
predicted_class, confidence = prediction_result
st.write(f'Prediction: {class_name[predicted_class]}')
st.write(f'Confidence: {confidence}')
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