File size: 1,889 Bytes
242e5dc fcefe06 242e5dc fcefe06 242e5dc fcefe06 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
st.title('🍅 Simple Tomato Leaf Disease Classifier')
@st.cache_resource
def load_model():
# Define the same model architecture as in training
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), input_shape=(128, 128, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Conv2D(8, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation='softmax')
])
# Load only the weights (not the full saved model)
model.load_weights('100-epoch with regularization.h5')
return model
model = load_model()
# Class labels
class_names = [
'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'
]
uploaded_file = st.file_uploader('Upload a tomato leaf image', type=['jpg', 'jpeg', 'png'])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption='Uploaded Image', use_column_width=True)
img = image.resize((128, 128))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
preds = model.predict(img_array)
pred_class = np.argmax(preds, axis=1)[0]
st.success(f'Predicted Class: {class_names[pred_class]}')
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